I rented the $300 bounce house, set up the tables in the garden, bought decorations that weren’t supposed to get wet, and waited.

1 PM arrives and exactly when nine hyperactive pre-schoolers were supposed to turn up, the sky turned the color of a bad bruise.

It poured.

Not a light drizzle. A full-blown downpour that turned our lawn into a swamp and left a bunch of sugar-fueled five-year-olds trapped in my kitchen, screaming at a decibel level that probably violated local noise ordinances.

The app wasn’t exactly lying; it just gave me a single guess instead of the full probability distribution.

Human beings crave clean, single-point answers. But the world doesn’t work in yes or no. It works in ranges and variance and fat tails. In finance, ignoring that variance isn’t just lazy. It’s how you get wiped out.

That exact gap, the distance between a comforting guess and raw probability, is where Synth (Subnet 50) steps in.

Inside the Swarm: How Synth Maps the Future

Most price-forecasting products give you a single number. “BTC hits $65k.” It’s like flinging one dart at a dartboard and hoping for the best. Synth gives you the entire dartboard, and tells you which sections are bigger.

Here’s how it works. Hundreds of miners submit 100 simulated price paths every 5 minutes. Validators score them using CRPS (Continuous Ranked Probability Score), which answers one question: how close was your probability cone to what actually happened? The output is a live API that computes liquidation probability at 6, 12, 18, and 24 hours.

As Synth’s founder James Ross puts it, modeling raw price is nearly impossible, but you can continuously improve at modeling volatility. He calls the subnet “the Olympics for Monte Carlo simulations.”

By shifting the goalposts from predicting a price to mapping a distribution (sharpened every five minutes by hundreds of models competing to be least wrong), Synth turns Bittensor’s raw compute into a pristine financial utility.

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Why Now? The Rise of the Machine Allocator

In early 2026, prediction markets cleared over $20B in monthly volume.

But the bigger shift is the autonomous AI agents now trading real money on-chain.

A legacy oracle excels at telling an agent what an asset costs this exact second. But to size a massive bet or manage liquidation risk over a 24-hour horizon, machine allocators need deep statistical resolution: hundreds of models competing to be the least wrong.

Multiply hundreds of miners delivering 100 paths every five minutes across an entire asset index, and the network hammers out roughly 165 million high-resolution data points a day. The old way gave us a fragile guess, but the Bittensor way gives machine buyers the deep statistical map they actually need to survive.

It’s building the foundational risk API that the next generation of machine allocators, lending protocols, and DeFi agents will plug into to survive. Call it a new category: Risk Infrastructure.

Four Questions That Actually Matter

Let’s evaluate Synth the way a product manager would: through adoption signals, not just architecture.

Q1: What problem does it solve?

Anything making financial decisions on-chain is working from data that’s bad at the thing that matters most: tail risk.

Single-point guesses are close to useless for managing liquidations, sizing positions, or pricing options. A trader who acts on “BTC to $65k” has two outcomes: roughly right and a modest return, or wrong and annihilated. There’s no middle.

A trader who acts on a full distribution: “54% bullish, 90% confidence between $61k–$66k, fat-tail scenario at $52k” can size using the Kelly Criterion. Risk management transforms from paranoid guessing into a calculated, machine-readable routine.

Right now, miner behavior looks like it could mean two things: they’re building forecasts good enough to trade on, or they’re optimizing for protocol rewards. We can’t distinguish between the two from public data yet. Until we see live trading results or independent performance audits, the demand is largely based around the protocol paying miners who produce output the protocol consumes.

Q2: For whom?

Two tiers. Quant traders and options pricers are the primary market; on-chain DeFi and AI agents are the secondary. This is a power-user product for hedge funds, options desks, perpetual DEXs, and autonomous trading agents moving millions without human oversight.

The go-to-market advantage is Mode, the Ethereum L2 that co-founded Synth and serves as a built-in first customer, wired into its AI Terminal and DeFAI agents:

Most subnets die because they build a product and then go looking for customers; Synth built the product inside a customer. But if Mode is both builder and principal consumer, much of the apparent demand is captive, not independent validation. Synth shows signs of external monetization, but no public evidence yet demonstrates meaningful demand separation from Mode.

There’s a hidden third tier Ross emphasizes: ML teams training financial models. Synth’s daily data isn’t just for querying, it’s for training. Historical data leads to overfitting; synthetic data generates the edge cases that matter in live markets. A quant team that bakes Synth’s data into its model weights isn’t switching APIs easily. That’s a stickier, harder-to-commoditize relationship than query-time consumption.

Q3: How big is the opportunity?

A narrow SAM of approximately $2.5B in alpha signals and alt data, with a broader TAM of around $44B as DeFi and autonomous systems adopt better forecasting infrastructure. But data and oracle markets are notoriously hard to monetize, and the gap between a $44B opportunity and a $6M market cap only narrows if Synth can capture value, not just create it.

The core pain is that traders, protocols, and agents need dependable forward-looking signals that fit into live decision workflows. Synth is solving that by making forecasting a repeatable product loop: users get probabilistic outputs, miners compete to improve them, and the four-week Polymarket trial in October 2025, which returned roughly 110%, provides an early signal that the workflow can create measurable value.

There’s also an expansion vector the $44B doesn’t capture: Synth as a dependency inside the Bittensor subnet graph itself.

If composability becomes real, any subnet touching financial data needs a risk input: take a trading-agent subnet querying Synth for liquidation probabilities, or a lending subnet using it for collateral scoring. If Synth becomes the risk oracle in that stack, the addressable market is every financial subnet on Bittensor. Whether that happens depends entirely on the team's execution discipline: the current plan to calculate operating costs and redirect revenue to buybacks puts all value capture in their hands.

Q4: How will we know if it’s working?

We’ll know Synth is working when external users choose it as forecast infrastructure, not just when the internal network is active.

The clearest signal is independent API consumption: usage from traders, protocols, and agents outside the Mode ecosystem, with repeat integrations over time.

The second signal is out-of-sample performance across many trials and asset classes, showing that forecasts remain useful in live conditions rather than only in backtests or one-off wins.

On the supply side, we should see sustained miner participation from teams that consistently perform well, with weak speculative strategies filtered out through weekly reporting, immunity periods, and reward persistence.

Roadmap execution matters too: adding new assets (recently equities), shorter time horizons, and eventually more use cases should expand both demand and supply without degrading forecast quality.

The Takeaway: Bull vs. Bear

The bull case: Synth is the first financial subnet selling a better question, not just a better answer. It has a working product, a built-in customer in Mode, and hidden upside in ML training-data lock-in. If subnet composability happens, Synth becomes the default risk oracle. A $6M market cap against a $44B TAM is real asymmetry.

The bear case: Value creation isn’t value capture. Deep options markets already do this well. Mode’s demand may be captive. And if miners truly had a superior edge, they’d trade on it rather than farm emissions.

Synth is the most legitimate financial subnet on Bittensor right now. But the only number that confirms product-market fit is independent API consumption. When non-Mode entities start paying for this data, it shifts from a well-engineered project to an undeniable market force. Everything until then is promising but unproven.

The Bounce House Question

Back to the birthday party: that weather app didn’t give me a distribution because I didn’t want one. I wanted the comforting answer. The clean “yes” that let me rent the bounce house and move on. The app sold me certainty, and I bought it. Then I spent an hour hosing cake frosting off my kitchen walls.

If it had shown me the real distribution, let’s say 70% mild, 20% light rain, 10% biblical downpour, I still might have rented the bounce house. But I would have set up the backup plan, maybe bought indoor some decorations. Carried the variance instead of pretending it didn’t exist.

That distance, between the answer we want and the distribution we actually need, is where Synth lives.

The real bet isn’t that miners can beat options desks. It’s that the next million financial decisions on-chain won’t be made by humans who crave clean answers, but by machines that require dirty, honest, high-resolution probability.

Now, for me, the accessibility tension is still real. The subnet that figures out how to show probability distributions to someone who doesn’t know what CRPS stands for is the one that breaks out of the quant ghetto. Making probability visual is making probability adoptable.

But every piece of infrastructure that changed the world started as something nobody understood. TCP/IP was impenetrable, while HTTP was academic. Technology that matters arrives as a necessary first, and then the builders who survive figure out the translation layer.

So I’ll leave you with this: if you could see the full probability distribution for one decision in your life: your mortgage, your portfolio, your kid’s college fund, would it change what you do? Or would you still reach for the comforting answer?

The gap between those two responses isn’t just Synth’s market. It’s the entire gap between the world we live in and the world we’re building.

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Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research

Three are price charts. Five are on-chain analytics dashboards. Two are X feeds tracking whale wallets. One is a Google Doc where I’m manually logging trade ideas. The rest are incubating research rabbit holes.

And then there’s my wallet: a treasure-filled filing cabinet that does exactly nothing with any of this information.

But as artificial intelligence merges with decentralized finance (a movement known as DeFAI), this kind of passive wallet is becoming obsolete.

One team in the Bittensor ecosystem is trying to change this. Subnet 45 (Talisman AI) is attempting to turn the wallet from a static asset holder into an intelligent, autonomous co-pilot. Backed by Team Rizzo, the DNA Fund, and the builders of the Talisman Wallet itself, it’s one of the most ambitious subnet plays I’ve come across.

But how do we know if it’s actually solving anything?

Most Bittensor subnet coverage focuses on emissions, alpha price charts, or miner counts. Those are lagging indicators, telling you what happened, not whether something worth building is being built.

So I’m evaluating this differently using a structured way to separate products from projects. Four questions:

• What’s the business objective?

• How would we know if it worked?

• What problem does it actually solve?

• Who is it for?

Let’s break down Subnet 45 through this lens and diagnose where the strategy shines and where the product friction is starting to mount.

The Strategic Play: What Business Objective Does Talisman (SN45) Address?

For years, Talisman has been a leading multi-chain wallet, offering a sleek interface for Ethereum, Solana, Polkadot, and Bittensor. I covered it in my wallet guide here. However, in a crowded wallet market, beautiful design is no longer a sustainable moat.

The launch of Subnet 45 is a highly strategic shift to turn Talisman into the undisputed gateway for the AI-agent economy.

The DeFAI Integration Roadmap

Phase 1: Perception (Live)

Launch of Subnet 45 (Talisman AI) on Bittensor. Miners analyze social signals (initially from X/Twitter) to determine sentiment and technical relevance for specific subnets. Now available within the wallet.

Phase 2: The DeFAI Portal (In Progress)

Beta launch of the DeFAI Portal within the Talisman wallet. This introduces the “Decision Engine,” which provides users with trading signals and staking suggestions based on SN45 data.

Phase 3: Agentic Execution (Upcoming)

Launch of Policy-Based Automation. This allows users to set “intents” (e.g., “maximize yield on TAO”) where the agent executes rebalancing or staking within pre-defined risk boundaries and spend caps.

Phase 4: Autonomous Marketplace

A full Agent Marketplace and custom builder, allowing users to deploy specialized agents that manage liquidity autonomously across multi-chain environments.

To pull this off, the team is executing three core strategic moves:

1. Building a Proprietary Intelligence Moat: SN45 acts as the wallet’s decentralized “eyes and ears,” harvesting live market intelligence (social sentiment, whale movements, tokenomics). This gives Talisman an exclusive, real-time data engine you simply can’t bolt onto MetaMask or Phantom after the fact.

2. Constructing “Agent-Ready” Rails: Capitalizing on their May 2026 partnership with General Tensor (who recently acquired Backprop Finance), Talisman is co-developing secure multisig infrastructure. This provides the secure plumbing required for institutional funds and autonomous AI agents to co-exist safely on-chain.

3. Aligning Token Incentives: Transitioning from a pure software tool to a token-powered network, the SN45 Alpha token coordinates the ecosystem by rewarding miners for feed quality, acting as the currency to unlock premium wallet actions and compensating developers for new agent strategies.

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Measuring Success: How Will We Know If Talisman Has Succeeded?

For Subnet 45 and the Talisman ecosystem, success isn’t just about block times or mining difficulty. The framework for their product-market fit comes down to five core metrics:

The Friction Point: What Core Problem Does It Solve?

Talisman is directly targeting three massive user frustrations:

• Information Overload: you need to constantly monitor X for hype, watch block explorers for whale moves, and track technical charts, all in different apps. By the time you’ve pieced the picture together, you’re late. And you’re emotional. SN45 aggregates these fragmented workflows into a clean, structured data stream to beat FOMO-driven late execution.

• The Dumb Wallet Limitation: Current wallets cannot execute conditional logic like, “Take profit on my TAO if social sentiment drops 20% and on-chain whales start selling.” The Decision Engine introduces “if-this-then-that” intelligence directly into self-custody.

• The AI Trust Gap: Automated trading bots usually require you to hand over your private keys: a total non-starter for responsible people. Talisman solves this via human-in-the-loop approvals. The AI handles the heavy lifting of scanning and packaging trade batches, but you retain 100% control of your keys and must provide a one-click approval before anything hits the chain.

Who is Talisman for? The Split Identity Crisis

Talisman is currently trying to design a single product for three entirely distinct target markets:

1. The DeFAI Retail Power User needs simplicity. They want an invisible co-pilot that surfaces clean, one-click staking or trading opportunities without adding cognitive clutter.

2. The Institutional Treasury needs the exact opposite. They require explicit complexity: rigid audit trails, compliance-friendly multisig infrastructure, and custom programmatic risk boundaries.

3. The Autonomous AI Agent doesn’t care about a UI at all. It needs low-latency APIs, reliability, and permissionless financial rails to execute machine-to-machine commerce.

The core product risk is that the same wallet cannot be all three things without confusing all three users. Optimize for retail, and institutions view it as a toy; optimize for institutions, and retail finds it overwhelming. Trying to be everything to everyone risks alienating the core user base. Talisman needs to decide if these AI features are an invisible backend enhancement for retail, or an advanced cockpit for power users and whales.

The Product Reality: Where Ambition Meets Friction

Shifting a product from a clean consumer wallet to a decentralized intelligence layer introduces deep architectural complexity.

Ecosystem Sprawl vs. Core Focus

To the team’s credit, they are transparent on their website that the AI agent features are “Coming Soon.” The immediate issue isn’t vaporware, it’s ecosystem sprawl.

Right now, the platform is pulling the user in four different directions:

• The core multi-chain wallet (pitched at retail users managing standard swaps).

• The Subnet 45 AI integration.

• A section dedicated to the details, status, and utility of their native community NFT collection.

• A highly thematic portal for the $SEEK token (complete with its own staking and lore).

For a product whose primary value proposition is making Web3 easier, this volume of pillars creates a steep learning curve. Managing a native NFT project, a complex token ecosystem, and an advanced AI integration within a single interface risks severe feature bloat.

As the AI features roll out, the UI will need massive consolidation to keep the AI acting as a streamlined assistant rather than another overwhelming tab.

The Multi-Token Friction

The mental model for participating in this ecosystem is further complicated by asset fragmentation. Users have to navigate both $SEEK (the ERC-20 token for governance and premium agent access) and SN45 Alpha (the Bittensor-native commodity token minted via dTAO for network utility).

The immediate user confusion is obvious: “If I believe in Talisman AI, what am I supposed to buy?” Fortunately, leadership seems to recognize this friction. In May 2026, Frank Rizzo noted they are working on a plan to streamline the ecosystem, with the ultimate goal of having the SN45 token represent both the wallet utility and the subnet. Until that consolidation materializes, however, users are left navigating a token maze.

The Takeaway

Talisman is executing on a brilliant macro strategy. Most subnets would kill for what this team has: an existing consumer product with real users, a revenue model that doesn't depend on emissions, a functioning data network, and a credible institutional partner in General Tensor.

The partnership structure, Rizzo's subnet feeding intelligence into a wallet that people already use, bypasses the academic-only trap that strangles so much of the broader Bittensor network. And there’s a consumer product ready to use the data from day one.

But the gap between the vision and the product you can touch today is real.

The Bull Case:

The first AI Agent feature ships inside the wallet, and it's actually impressive. Adoption follows. The Alpha token unlocks real utility. The burn rate accelerates beyond baseline fees. General Tensor brings institutional flow. Talisman becomes the default intelligent wallet in Web3 with AI as a core component.

The Bear Case:

The roadmap stays ahead of shipped features for too long. Meanwhile, competitors with even larger user bases bolt basic AI tools onto their wallets (simpler, worse, but available) and the first-mover advantage evaporates. Feature bloat and token confusion stall retail adoption, and while the subnet keeps earning and the data keeps flowing, the product never catches up to the promise.

Here’s what I’m watching for: The day I open Talisman and an AI agent does something visibly useful without me asking. Maybe a trigger that fires, or a sentiment signal that saves me from a bad trade. A single automated action that I couldn’t have done as easily on my own.

When that happens, this project moves from a smart speculative bet to an essential piece of Web3 infrastructure.

The bet is smart today. But the clock is ticking, and competitors aren't waiting.

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Which subnet should I evaluate next? Reply and let me know.

And if you found this useful, share it with someone who’s trying to make sense of Bittensor subnets beyond the price chart.

Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

His pyjamas, pillow, duvet, the bedroom carpet.

And worst of all, his favorite penguin teddy took the direct hit.

But he wasn’t crying. Still half-asleep, he just stood in the hallway holding the soaked penguin in one hand, waiting for me to fix it. That moment told me something: the thing wasn’t working anymore, and there was no panic. Just an expectation that Mommy and Daddy would fix it.

May felt like that for Bittensor. April’s Covenant exit cracked the seam, but May was the month the community started stitching it back together.

And luckily for me, I’d had a replacement teddy ready for a while. Now he carries around three of them, a new penguin family.

How to Read This Compass

I’m organizing these updates by direction: North, East, South, West to give you a repeatable mental model for navigating Bittensor’s complexity.

North: Foundation 🏛️ (governance, protocol, core team, security events)

The structural layer. Changes here determine who controls the network and how decisions get made.

East: Capital 💹 (markets, volume, liquidity, integrations)

The economic layer. This is where price meets liquidity and institutional conviction shows up in the data.

South: Technology ⚙️ (shipped features, benchmarks, infrastructure, ecosystem health)

The execution layer. What actually works, what got built and where’s the technical proof.

West: Adoption📡(narrative, mainstream validation, external voices)

The perception layer. This is how the outside world sees Bittensor, from industry titans to normie investors.

Each month, some directions will dominate the signal. Others will stay quiet. The goal is to see which quadrants are moving and what that means for your strategy.

Three of the four directions on the compass moved this month, one stayed quiet and another dominated. Lets review what happened.

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🏛️ Conviction ships to mainnet

What Happened:

On May 13, Conviction shipped to mainnet. It’s Bittensor’s formal response to BIT-0011 and to the Covenant exit in April.

Every emission a subnet owner earns now auto-locks the moment it lands in their wallet. Conviction is calculated as stake multiplied by time, re-averaged every 30 days, and designed to make ownership more accountable.

Future plans involve converting those locked positions into transferable governance power so the community can vote underperforming owners out.

The first three subnets onboarded were the three Covenant abandoned: SN3 (Templar/Teutonic), SN39 (Basilica), and SN81 (Grail).

Why It Matters:

My April post called community miners rebuilding the abandoned subnets “ownership through commitment, not founder discretion.” Conviction is that idea, formalised at the protocol level.

What I noticed is the speed. Covenant walked out on April 9. By April 16, Steeves was on Discord explaining the mechanism. By May 13, it was code shipping to mainnet.

This is the kind of response cycle you want to see from a protocol that just took a public punch. Slow committees would have spent six months arguing. Bittensor wrote the fix, tested it, and pushed it in five weeks.

The Takeaway:

If you’re delegating, start tracking the conviction scores on the subnets you back. This is the new operator due diligence metric, and the people running the most committed teams are about to look very different from the people running the loudest ones.

For non-coders, its not quite available on Taostats yet (visible in the UI but no data), but until then you can view Total Stake Weight as proxy.

Conviction is locked alpha, and staked alpha can still be unstaked and sold, just not on a whim. But a high staked ratio means holders are parking tokens rather than keeping them sell-ready, which is the same question conviction is built to answer: who's committed, and who's liquid. So I read it as a floor, not a verdict.

🏛️ The emissions refactor changed how rewards flow

What Happened:

Around the same time Conviction shipped, Bittensor also updated its emission logic to a new model. The refactor concentrates rewards among roughly 30 productive subnets and introduces a new parameter called Net TAO flow.

The protocol now distributes newly issued TAO based on normalised flow data, tied to prior emissions and buybacks. In plain terms: the protocol now actively rewards subnets that produce useful work and starves the ones that don’t.

Why It Matters:

Conviction fixes who controls the network and now the emissions refactor fixes what the network rewards. Together they are the most significant tokenomics change since dTAO.

The economic logic is simpler now. Under the old model, low-emission subnets could still drain meaningful TAO into the market with little to show for it. Under the new model, that pressure compresses.

If you’re holding TAO, you should expect cleaner alignment between productive subnet activity and token issuance. If you’re staking into subnets, the gap between top performers and the long tail is about to widen sharply.

The Takeaway:

Emissions data over the next 90 days will be telling. Watch which subnets gain share and which lose it: the list of subnets gaining emissions share in this period could be the new smart money signal.

💹 Wormhole opens the Solana door

What Happened:

On May 5, the Solana Foundation and Wormhole Labs announced that TAO is now live on Solana via Wormhole’s Sunrise gateway. It’s a canonical bridged version of TAO trading natively on Jupiter, Meteora, Phantom, and Solflare.

Wormhole’s Native Token Transfer framework backs the bridge, which means liquidity is unified across Solana rather than fragmented across wrapped variants. Within seven days of the announcement, TAO was up 25%.

Why It Matters:

Solana wants to be the execution layer for AI agents. Bittensor is the intelligence layer for decentralised AI. Until now, those two worlds were connected through wrapped versions of TAO that fragmented liquidity and created friction. Wormhole just removed that friction.

The result is that TAO can now be used inside one of the deepest DeFi ecosystems in crypto, with the same liquidity guarantees you’d get from a native token. For the agent economy thesis, this means agents running on Solana can now hold, transfer, and stake TAO without leaving their execution environment.

The Takeaway:

Track TAO volume on Solana DEXs over the next 90 days. If volume on Solana grows faster than volume on EVM bridges, the agent economy thesis is becoming more of a reality.

📡 Sara Hooker chose Bittensor

What Happened:

On April 30, SILX AI announced that Adaption Labs is partnering with them to advance the Quasar foundation models on Subnet 24. Adaption Labs is co-founded by Sara Hooker, the former Vice President of Research at Cohere and a veteran of Google DeepMind.

Her firm raised $50 million in seed funding to advance adaptive AI research. The partnership will see Adaption Labs supply high-quality adaptive datasets to train Quasar models, with model quality and competitive SOTA performance set as priorities from day one.

Why It Matters:

Hooker has real credentials at the top of her field. She could have gone anywhere, but chose Bittensor. This is a perception-layer signal that an investor in a corporate job, listening to colleagues debate whether decentralised AI is real, can finally point to.

The Quasar thesis is that decentralised training only matters if it produces models people actually want to use. Its the standard Hooker built her career around, and it’s something that Bittensor has been failing to clear in mainstream AI press.

The Takeaway:

Watch Quasar’s HuggingFace releases over the next 12 months. If their open models start showing benchmark numbers competitive with Llama or Mistral at similar parameter counts, that would be strong evidence that decentralized training on Bittensor can produce research-grade models.

If they underdeliver on benchmarks, the claim that decentralized training produces real research becomes harder to defend, though the subnet could still be valuable in niches like long-context recall or cost efficiency.

⚙️ Quiet

May had no headline benchmarks or major shipped features outside the protocol layer. This is somewhat to be expected in a month dominated by foundation work.

Summer time is when this has potential to fire. Teutonic’s targeted trillion-parameter run, the Rayon Labs subnet trio’s continued share of emissions, and Quasar’s first model releases are the candidates. Until one of them lands, this direction is a watch-and-wait.

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What sits with me most from May is the cadence. Five weeks from Covenant’s walkout to Conviction shipping live code is an unusual response time.

But its classic Bittensor, the kind of velocity you only see when the people building know exactly what’s broken and have already designed the fix.

The optimist’s read is that this network is more antifragile than its critics give it credit for. The skeptic’s read is that a fast patch can paper over a deeper governance problem, and we’ll only know which it is in the next couple of years.

Which of these moves you the most? The protocol moving fast on Conviction, or a researcher of Hooker’s calibre choosing Bittensor over a closed lab?

And if I missed something important from May, what would you add? Reply to this email and let me know. I reply to every single one.

Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

And whatever new streaming service she signs up for when the latest season of The Summer I Turned Pretty mercifully returns to our screens.

Every couple of months we sit down at the kitchen table and ask the “what are we actually watching?” question, and one of the streaming services gets axed.

The Claude bill sits in there with all of them. Auto-renews on the same day every month. It has never made that list and probably should have, as there are a few things that have been bugging me about it.

I can’t pick the model architecture I’m using. Anthropic picks for me, from their range. No DeepSeek for the hard reasoning problems, Qwen for spreadsheets, FLUX for images. Just Claude, in whatever version they decide to ship that month.

Every prompt I type goes to a server in San Francisco run by a company whose terms of service could quietly change next month.

And I hold TAO. I’m an investor in the largest decentralized AI network in the world, and the AI I actually use every day is closed, centralized, and locked behind one private company’s API.

There’s a stack you can build today, in about 15 minutes, that fixes all of that.

It runs on Bittensor and it’s free or as little as $10 a month for an API. You don’t need to write a line of code.

The three layers in every chat product

Every AI chat product is three layers stacked on top of each other.

The model. The brain. GPT-5.1, Opus 4.7, DeepSeek-V4, Kimi K2.6, GLM-5.1.

The inference layer. The thing that runs the model on a GPU when you hit send. AWS, OpenAI’s own data centres, or in our case, Bittensor’s network of miners.

The interface. The chat box you actually look at.

ChatGPT (or Claude, Gemini, Perplexity) bundles all three and hands you the result. You can’t change the model or the inference and the interface is non-negotiable.

The anti-ChatGPT move is to pick each layer yourself:

• Open-source model.

• Bittensor inference (Chutes).

• Whatever chat UI suits you.

Where Chutes fits

Chutes is Subnet 64 on Bittensor. It’s what turns subnet emissions into AI you can actually talk to. Open-source models (DeepSeek, Kimi, GLM, Llama) get served by a network of miner-operated GPUs around the world. Validators score the output quality.

You hit send, somewhere a miner runs your prompt and you get an answer back. The TAO you hold is, in part, paying for the GPU you just used.

Chutes is OpenAI-compatible. Anything that speaks OpenAI’s API speaks Chutes. That’s the key that makes every chat app and every browser extension in this post work without code.

A lot of other Bittensor subnets rely on Chutes for their own inference too. It’s a layer the rest of the network builds on.

When a subnet app needs to talk to a model, there’s a good chance Chutes is in the chain somewhere. As an example, Affine (SN120) fine-tunes models on hard reasoning problems, then pushes winners to Chutes where anyone can query them. It does the training while Chutes takes care of the inference. That composition is what the ecosystem is becoming.

A bit of recent history matters here also: until early 2026, Chutes ran a free tier. 200 requests a day for nothing. It was a beta in disguise, kept alive by token emissions.

In February this year, they killed it.

On March 20, they published a blog post called From Volume to Value, explaining they’d cut 45% of their token volume because it was unprofitable and pushed heavy users onto pay-as-you-go.

And on May 19, they did it again. The $3 Base tier is gone. The minimum is now $10 a month.

For some people, that was bad news. For me, it’s the opposite.

Free things on the internet don’t last: real products do. Chutes is becoming a real product and that’s the signal a serious investor should care about.

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Sidebar: where does my prompt actually go?

On Chutes, it lands on a miner’s GPU somewhere in the world. Validators score miner output, but that doesn’t keep your prompt private from the miner running it.

The fix is Trusted Execution Environments (TEE). Many Chutes models run inside one. Think of it like a sealed envelope that only the recipient’s hardware can open. Chutes encrypts your prompt on your end, ships it to a confidential-compute GPU, and the lock only breaks inside the chip. No one in the chain reads it first.

To use it, pick a model with “TEE” in the name. deepseek-ai/DeepSeek-V3.2-TEE, zai-org/GLM-5.1-TEE, moonshotai/Kimi-K2.6-TEE, MiniMaxAI/MiniMax-M2.1-TEE. Same API, same setup.

ChatGPT, Claude and Gemini can’t promise this. The contents of your prompt are physically unreadable by the people running the model.

One caveat: TEE doesn’t make Chutes anonymous. Your account is still linked to your usage. It just makes the contents of your prompts unreadable by the miner.

The Basic Stack: Chutes Chat

The simplest version of the stack is one URL.

Open chutes.ai/chat That’s the whole stack. No account, no API key, no setup.

Inside that chat, you can:

• Switch models mid-conversation. DeepSeek for one question, GLM for the next, no friction.

• Use Chutes Search, their built-in web search. Launched recently, this works well enough to replace ChatGPT’s search for most questions.

• Generate images with Qwen Image 2512, one of the best open-source image models around.

• Upload files. PDFs, documents, whatever you’d attach to a ChatGPT prompt.

• Save conversation history and come back to it later.

What you can’t do is build a saved system-prompt library. There is no plugin ecosystem or agents. The UI is theirs, and if they redesign it next month, you’re along for the ride.

If you’re a casual user (a few prompts a day, the occasional image, no power-user setup), stop here. Pocket the $20 a month you used to send Sam Altman.

Bull case: zero friction, real model choice, free.
Bear case: you don’t own the interface, and Chutes’ UI doesn’t yet match the polish of ChatGPT/Claude/Gemini.

Get your API key

Ready to push past Chat? Time to set up an account and grab an API key.

1. Go to chutes.ai. Make an account.

2. Pick a tier. Plus is $10 a month, Pro is $20. Each gives you up to 5x its dollar value in usage. Start with Plus, you can upgrade later in under a minute.

3. Generate your API key under the API Keys tab. It starts with cpk_.

4. Treat it like a credit card number. Don’t paste it into Discord, don’t email it to yourself and (for me) don’t screenshot it for the newsletter.

Your API key is the thing that makes every chat app on the next page work without code.

And on Chutes, this is big value. Ten dollars buys you up to $50 of inference per month. If you go over, you top up with pay-as-you-go credits.

For a few chats a day on open-source models, this cap is invisible. If you’re like me, who is not a coder but can still go pretty hard on research, you’ll never go anywhere close to those limits.

Now you need somewhere to actually use that key. A chat tool that takes your prompts in and renders the outputs back nicely. The one I use is up next, with a few other good options after.

The Advanced Stack: Chutes + TypingMind

TypingMind is where the stack gets serious.

Why I picked it: this is a one-time license, you own it.
Price tiers range from $39-$99 with regular discounts. There is no recurring fee. You still pay Chutes per-token for the actual inference, but the interface is yours for life.

It officially documents Chutes as a supported provider. They have per-model setup pages on their site.

What you get over the basic chat: plugins, agents, vision, function-calling, custom personas, a prompt library you can build over months. Switch between Chutes, OpenAI, Anthropic, and the rest from the same window if you want a multi-provider setup.

Setup is eight fields.

1. Open TypingMind. Models → Add Custom Models.

2. Name: Model name (I prepend with Chutes to be sure where its coming from)

3. API Type: OpenAI Chat Completions API

4. Endpoint URL: https://llm.chutes.ai/v1/chat/completions

5. API key: your cpk_ key.

6. Model ID: copied from the API tab on your chosen model at chutes.ai/app e.g. zai-org/GLM-5.1-TEE

7. Context length: set per the model also from the API tab.

   

8. Advanced- Custom Headers: Authorization [tab] Bearer API KEY (insert your API key here)

Hit Test. If it says the endpoint is working, just hit Add Model and you’re in.

My three starter model picks on Chutes:

• DeepSeek V3.2 TEE for general work, drafting, summarizing.

• GLM 5.1 TEE for creative writing with hardware-level privacy.

• Kimi K2.5 TEE for help with spreadsheets, formulas, or any code-flavored task.

The open-source model landscape moves fast. A model you’ve never heard of can leapfrog the top spot in a quarter. Bookmark the Open LLM Leaderboard at llm-stats.com/leaderboards/open-llm-leaderboard and cross-reference what’s running on Chutes at chutes.ai/app. That combination keeps you on the best model available without thinking about it more than once a month.

Other shapes of the same stack

The OpenAI-compatible thing means TypingMind isn’t the only option.

Good alternatives include Msty for a desktop-first local/remote workflow, Chatbox for cross-platform chat with custom endpoints, Page Assist for in-browser sidebar chat, and self-hosted options like LibreChat and Open WebUI.

Two honest Chutes trade-offs

1. The frontier-model gap is real. On the absolute hardest tasks (deep research, multi-step reasoning, agentic coding), the closed frontier models still pull ahead of the best open-source models on Chutes. For everyday work (drafting an email, summarizing an article, brainstorming, generating an image), the gap is invisible. And you’re not spending $20 a month.

2. The where-does-my-data-go question is the other one. Honest answer: through a network of GPU miners around the world, ranked by validators. If that makes you nervous, use a TEE model and the contents of your prompts are physically unreadable by the miner running them. Centralized providers don’t offer this at all.

For the absolute best model on a hard, specific job, OpenAI, Claude and Gemini still win. For everything else (which is most things), the anti-ChatGPT stack wins on cost, privacy, and the part where you’re paying the network you’ve already invested in.

When Chutes isn’t enough

Three alternatives to know in case you need them.

OpenRouter. 300+ models, single API key, free tier with rate limits. Best for exploration. Worth knowing: Chutes is one of OpenRouter’s providers, so sometimes you’re paying Chutes through OpenRouter without realizing it.

Groq. A strong choice when speed matters most, thanks to its custom LPU hardware and free developer access.

Together AI. A good alternative if you want a broad but more curated model platform and a practical fallback when you need another provider.

Bittensor isn’t your only ride. But for someone who already holds TAO, it’s the one that makes you a customer of the network you already invested in.

The stack you’ve just built

Run the three layers one more time.

The model: picked. Open-source, your choice, switched mid-conversation if you want.

The inference: Chutes. Bittensor’s network. Your TAO is paying for the GPU.

The interface: chutes.ai/chat if you want simple, or TypingMind if you want to own a serious model-agnostic chat tool.

The deeper move is the one you might not feel for a couple of weeks. You stopped being a passive token holder: now you’re a customer of the network. Your TAO has a job.

One last thing. If you decide to grab TypingMind for the advanced stack, feel free to use my link. Both of us pick up 0.5GB of TypingMind cloud storage at no extra cost. Skip it if you'd rather, the recommendation stands either way.

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Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

It was Saturday afternoon. I was refereeing two boys fighting over who gets to turn on the tap for the garden hose.

My phone buzzed. I got distracted, and the younger one ended up soaked.

A friend, also a TAO (and other cryptos) holder, texted in our “Crypto School” group chat: “Bro, what do you think of NEAR? 1M TPS, 46M monthly users, AI agents on the consumer wallet. This is getting real traction. Should I sell some TAO and buy NEAR?”

Quick disclosure before we go further: I’ve been aware of NEAR for years, long enough to have lost a pretty penny on NEAR NFTs in 2021, hoping to ride the next Solana boom. Now I’m a proud owner of NEAR Tinker Union and Antisocial Ape Club, which is to say I own priceless lessons on wasting money.

I get one version of this question every few weeks:

Should I be in TAO or NEAR? Or Render? What about ICP? Isn’t ASI an AI thing too?

The honest answer is: those projects aren’t competing for the same slot in your portfolio. They sit at different layers of the same stack.

Most “crypto AI landscape” pieces collapse them into a single horse race, and that’s how readers end up holding three accidental bets on the same layer and zero on the others.

So after the garden clean up job, I sat down with my coffee and answered the only way I know how.

With a map and a framework.

The Four Layers of Crypto AI

The AI economy is increasingly splitting into four layers: Compute, Intelligence, Agents & Data and Execution/UX. Each layer is seeing a centralized stack dominate today while decentralized networks are emerging to compete on incentives, access, and ownership.

Read it down, not across. A bet on Render isn’t a substitute for a bet on TAO. A bet on TAO isn’t a substitute for a bet on NEAR. They could all win or lose.

Let’s walk through each layer.

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Compute is the rented muscle

Render started as a decentralized GPU marketplace for 3D rendering and has aggressively expanded into AI inference and training workloads. Akash runs a “decentralized cloud” model: anyone can rent out spare compute. io.net aggregates GPUs on Solana. Nosana plays a similar game.

The pitch is the same across all of them: cheaper than AWS, more resilient, no single point of failure. The reported cost savings vs. centralized clouds run in the 50–80% range depending on workload, which is real money for AI startups burning through inference budgets.

This layer is the picks and shovels of crypto AI.

Where they win: Cost, access and resilience.

Where they lose: Frontier-scale training. The hyperscalers still have tighter GPU supply relationships and the kind of integrated networking you need for the biggest runs. Decentralized compute is best positioned for marginal workloads like inference, fine-tuning, edge deployment, or rendering pipelines.

Intelligence is where Bittensor lives alone

This is the part most readers underweight when they map the space.

Bittensor is the most established crypto network explicitly trying to build decentralized markets for model production at scale.

NEAR and ICP are more execution and app-layer plays, Render rents compute, and ASI is more about agent coordination and data infrastructure.

Bittensor is different. Miners compete to produce valuable model outputs, validators score them, and subnet economics determine where emissions flow.

With dTAO, that incentive system has become more market-driven, which is why I think of it as Proof of Intelligence: AI production coordinated by an open economy of 128 subnets rather than by one company’s balance sheet.

Where it wins: Specialization, open competition and economic alignment between the people who build the AI and the people who use it.

Where it loses: Onboarding. It is lacking the kind of seamless UX that makes ChatGPT effortless for someone who’s never opened a terminal.

I’ll come back to Bittensor in more detail in the spotlight section. The point for now: it’s the only one of the projects on this map that’s playing the intelligence game.

Agents and data are the ASI Alliance bet

The Artificial Superintelligence Alliance (ASI): Fetch.ai, SingularityNET, and Ocean Protocol who all merged under the FET ticker; are the full-stack agent and data play. Fetch handles autonomous agents that can negotiate and execute tasks, SingularityNET runs an AI services marketplace and Ocean handles tokenized data exchange.

The vision is good, but the execution is more fragmented than the marketing suggests and the merger is still in the awkward phase where three projects are operating under one banner without fully feeling like one stack. Its worth watching, but not yet a thesis I’d build a portfolio around the way I would around the intelligence layer.

Where it wins: Vertical integration. Owning agents, services and data in one stack is genuinely rare in crypto AI.

Where it loses: Coherence. The merger is still bedding in and you can feel the seams.

Execution/UX Layer: Where AI Apps Actually Live

NEAR is the most practical of the bunch. It’s an “AI-ready” L1 with developer tooling that lets you build with natural-language prompts, generate contracts and ship dApps without being deep in the weeds. Its high throughput, user-friendly and offers solid grants for AI builders. NEAR is trying to be the place where AI-powered applications get used.

Internet Computer (ICP) plays a different game. It runs AI models and agents fully on-chain via canisters, with ICP burned for compute cycles. This is total verifiability and sovereignty: nothing is off-chain, and everything is trustless. That makes it heavier infrastructure, but powerful when on-chain history and execution really matter.

Where they win: On-ramps. Both projects make AI-powered dApps actually usable.

Where they lose: Neither is producing intelligence. They’re hosting the apps that consume it.

One non-crypto note worth flagging

The most important decentralized AI player on the planet doesn’t have a token at all.

Hugging Face hosts more than two million open-source models. It’s the GitHub of AI.

It’s where Llama, DeepSeek, Qwen, and most of the open-weight world get distributed. Multiple Bittensor subnets host their models there too. Anyone can pull weights, fine-tune on their own hardware, and ship a product without ever touching a crypto network.

Why does this matter for your TAO thesis? Because it’s the alternative path.

Open-source AI is closing the gap with closed-source faster than most retail crypto investors realize. Bittensor’s value at the model layer has to clear a bar set by free, open weights you can run locally. Sometimes it does, when specialization, incentives, and economic alignment matter. Sometimes it doesn’t.

Knowing where Hugging Face fits on your map is part of being honest about where TAO fits.

Bittensor Subnet Spotlights

Three live subnets worth knowing by name, because “Bittensor subnets” as a concept is too abstract for most people to act on.

Chutes (SN64). Serverless inference. You hit an API, a subnet routes to the cheapest available miner, you get a response. The closest thing to a drop-in replacement for the OpenAI API that exists in crypto AI today. If you’ve ever wanted to test what decentralized inference actually feels like, this is the one to start with.

Apex (SN1). The flagship language-model subnet with agentic workflows, tool use, and chain-of-thought reasoning built to push inference quality past what single open-source models like Llama or Mistral can deliver alone. Clean SDK and API, explicitly positioned for builders. If Chutes is the fastest way to experience decentralized inference, Apex is the fastest way to build on it.

Teutonic (formerly Templar) (SN3) and the broader training subnets. Decentralized training of large models: the kind of work that produced the 72-billion-parameter run earlier this year that outperformed older Llama baselines. The governance picture around this layer of subnets has been turbulent in 2026, but the infrastructure work is still real and still worth tracking.

These three are a starting point and by no means a complete list. The point is that “subnets” stops being abstract once you’ve used one.

Two frameworks you can use this week.

Framework one: how to evaluate any crypto AI project

Three questions, in order:

• What layer does it sit at? If you can’t place it on the map cleanly, that’s a flag.

• Does the token actually do work? Is it required for the network to function, or is it bolted on as a pretend governance token?

• Is anyone using it? Look for real revenue, usage and workloads. Not pitch decks.

If a project clears all three, it’s worth deeper diligence. If it clears two, it’s worth watching. If it clears one or zero, it’s a vibe.

Framework two: no-code entry points

You don’t need to run a validator to participate. The simplest way to use Bittensor is to query a subnet. Chutes is one of the easiest on-ramps. I’m working on an upcoming walkthrough to demonstrate just how easy this can be.

The simplest way to participate economically is to stake TAO on a subnet whose work you believe in, which under dTAO directs emissions toward that subnet. Both can be done from a wallet without a terminal.

What this looks like in real life

A freelance writer today pays Anthropic $20–200 per month for drafting and fact-checking. Tomorrow: they query Apex (SN1) directly for comparable quality at lower cost (no corporate margins). If performance slips, they simply switch subnets with no lock-in or surprise hikes.

Apex is live today with working APIs and products, the main friction is awareness.

Or, picture a small business owner building a customer service agent: ASI coordinates the logic, Apex (SN1) powers the language model and NEAR handles the interface. No single vendor owns the stack.

Your compass from here

Crypto AI isn’t one bet. It’s at least four. The projects you’ve heard lined up against each other are doing different jobs.

The smart move is to know which ones you’re exposed to and why: Compute, Intelligence, Agents and Execution are different layers with different roles to play.

Bittensor’s edge isn’t that it’s the only AI token. It’s that it’s the only one playing the model game, coordinating the production of intelligence itself, not renting the hardware or hosting the apps.

If you’ve never actually used a subnet, do it this week. Start with Chutes. Then come back and tell me if the intelligence layer still feels abstract.

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Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

My younger son is obsessed with testing things. He drops food off his plate not because he doesn’t want it, but because he wants to know what happens.

He’s learning cause and effect: broccoli and potatoes mashed on the floor for Daddy to clean up.

The experiments never stop, even if he doesn't know to call them that yet.

April felt like that, except nobody planned this month’s experiment. One operator pulled out of three subnets, dumped $10 million in TAO, and walked away. The community left behind picked up the pieces.

This month’s issue covers the four compass directions for April. Two of them dominated, another went quiet and the last one kept building like nothing happened.

How to Read This Compass

I’m organizing these updates by direction: North, East, South, West to give you a repeatable mental model for navigating Bittensor’s complexity.

North: Foundation 🏛️ (governance, protocol, core team, security events)

The structural layer. Changes here determine who controls the network and how decisions get made.

East: Capital 💹 (markets, volume, liquidity, integrations)

The economic layer. This is where price meets liquidity and institutional conviction shows up in the data.

South: Technology ⚙️ (shipped features, benchmarks, infrastructure, ecosystem health)

The execution layer. What actually works, what got built and where’s the technical proof.

West: Adoption📡(narrative, mainstream validation, external voices)

The perception layer. This is how the outside world sees Bittensor, from industry titans to normie investors.

Each month, some directions will dominate the signal. Others will stay quiet. The goal is to see which quadrants are moving and what that means for your strategy.

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🏛️“Decentralization Theater”: the governance crisis and the aftermath

• What Happened:

On April 9, Covenant AI announced it was leaving Bittensor.

They shut down three subnets: SN3, SN39, and SN81, dumped 37,000 TAO worth roughly $10.2 million into the market, and publicly accused co-founder Jacob Steeves of centralized control.

The specific allegations were pointed: that Steeves unilaterally suspended subnet emissions, overrode operator authority, and used large visible token sales as a tool to enforce compliance.

Founder Sam Dare called it “decentralization theater.”

TAO dropped 25% in six hours. $650 million in market cap gone with $9.1 million in long liquidations. Trading volume spiked to $1.72 billion on April 10, more than triple the monthly average.

I covered the allegations in my piece here.

• Why It Matters:

The crypto press covered the 25% drop. They missed what followed: community miners, with no guidance from founders and no central operator involvement, rebuilt all three subnets from open-source code. The chain continued running normally.

This was widely reported as a demonstration of the protocol’s antifragility and organic decentralization in action. New teams (e.g., Teutonic stepping in for Templar) picked up the work and the subnets resumed operations.

I’ve been writing about Bittensor’s antifragility thesis for a long time. This was the first real test at scale.

The protocol’s direct response was BIT-0011: the Conviction Mechanism. Subnet founders and stakers will lock alpha tokens across 30-day intervals to earn conviction scores. The staker holding the highest score gains ownership of the subnet.

And crucially, tokens locked during the active period can’t exit (rug) until the interval closes.

It formalizes at the protocol level exactly what the community just demonstrated organically: ownership through commitment, not through founder discretion.

• The Takeaway:

Watch BIT-0011 closely. After it goes live, if you’re delegating to subnets, understanding which teams are locking tokens needs to become part of your due diligence from here.

The governance layer fractured in April. The response revealed as much as the failure.

💹Institutional capital made its moves before the crash

• What Happened:

On April 2, a full week before Covenant AI went public, Grayscale and Bitwise both filed for spot TAO ETFs on NYSE Arca. An SEC review is underway with the decision window in August 2026.

On April 7, two days before the worst day of the month, Grayscale raised its TAO weighting inside its AI-focused fund to 43%. Its largest single-asset reallocation in the fund’s history. They did that before the broader market knew what was coming.

On April 20, BitGo and Yuma launched institutional-grade custody and staking for Bittensor subnet tokens: the first regulated, secure access point for large capital to get direct exposure to subnet economics. DCG’s Barry Silbert called it a meaningful step forward for institutional participation in Bittensor.

• Why It Matters:

This is the timing detail that nags at me.

What does it mean that the biggest institutional moves of April happened before, and immediately after, the worst governance crisis in Bittensor’s recent history?

It means institutional analysts are not reading the same headlines retail is.

They’re reading the staking data, revenue figures and structural supply dynamics post-halving. What they see apparently survives a 25% single-day drop without changing the thesis.

• The Takeaway:

The August 2026 ETF decision window is now a live catalyst on your calendar. A positive signal from the SEC unlocks a whole class of capital that currently can’t touch Bittensor even if they wanted to.

⚙️The execution layer didn’t notice

• What Happened:

The network generated $43 million in revenue across 128 active subnets in Q1 2026. That number didn’t move when Covenant AI left (their subnets were research-based).

Covenant operated three of Bittensor’s highest-emission subnets. Their exit was the single largest operator departure the network had seen. And the revenue baseline held because the network doesn’t run on any one team’s output. It runs on 128 markets competing simultaneously.

Looking forward: Teutonic (formerly Templar) is targeting a 1-trillion-parameter model training run by late May.

After Covenant-72B proved in March that decentralized training at 72 billion parameters wasn’t a whitepaper concept, a trillion-parameter attempt is a real escalation. It would be the largest decentralized training run ever attempted.

• Why It Matters:

When the biggest operator in the room walks out and the revenue line doesn’t flinch, it’s worth noticing. The timing of the potential trillion-parameter run also matters, as that milestone could land inside the ETF’s highest-visibility window during the SEC review calendar. If Teutonic delivers, attention from the AI world becomes a real possibility.

The output layer is accelerating independent of the drama layer.

• The Takeaway:

The trillion-parameter run and the ETF review window overlapping is not a coincidence to ignore. Successful decentralised training at that scale is exactly the proof of utility the institutional thesis needs to go mainstream.

📡No Mainstream Breakthrough

• What Happened:

Nothing meaningful here this month.

April had no Jensen Huang moments or Jack Clark quotes. No mainstream AI press picking up the Bittensor story. The governance crisis got crypto coverage but not AI coverage.

• Why It Matters:

The Covenant crisis was legible to crypto investors who already know what a subnet is, but it wasn’t legible to the broader AI audience that would need to understand why decentralized training matters to care.

This story requires context most AI journalists don’t have yet, so the translation gap is still wide open.

• The Takeaway:

Track mainstream AI media, not just crypto media. When Bittensor breaks through to that audience (and April wasn’t the month) you’ll feel it in adoption before you see it in the price.

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Which of these moves your conviction most: the protocol’s response to the governance crisis, or the institutional positioning that happened before it? I’m genuinely curious where your head is at after this month.

And if I missed something important from April, what would you add?

Reply to this email and let me know. I read every single one.

Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

Last month, I watched my son discover that passwords exist.

He was trying to unlock my phone to watch Bluey. When Face ID failed, he demanded the passcode. I showed him the pattern: six digits I’ve used for years.

“But what if someone sees?” he asked.

“They’d need to watch very carefully,” I said.

“But what if they have a really good brain?”

Somehow my five-year-old explained the quantum threat in a nutshell: what if someone builds a brain so powerful it can guess every password on earth?

Right now, your TAO wallet is protected by math that would take a regular computer longer than the age of the universe to crack. But quantum computers don’t play by those rules. They can try millions of combinations simultaneously.

And according to Google’s recent research, that “really good brain” might arrive sooner than anyone planned for.

When Someday Became 2029

Let me start with what I actually understand (and what you need to know without a PhD in cryptography):

Every major blockchain: Bitcoin, Ethereum, Solana, Bittensor, uses an encryption standard called ECDSA to secure wallets and sign transactions.

What This Actually Means: ECDSA is like a really complicated lock. When you send TAO, you’re proving you have the key without showing the key itself. The math is so hard that even the fastest supercomputers can’t guess your key before the sun burns out.

Unless you have a quantum computer.

A sufficiently powerful quantum computer running Shor’s algorithm could reverse that math. We’ve known this since the 1990s. The threat has always felt distant, theoretical, a problem for a future generation of developers to solve.

Then the timeline compressed.

Google recently published research showing roughly a 20x efficiency improvement in running Shor’s algorithm, shifting credible threat estimates from 2035–40 toward 2029–30.

Solana’s Anatoly Yakovenko called it 50/50 within five years.

Nic Carter flagged something more alarming still: the possibility of a nine-minute on-spend Bitcoin attack, where a quantum computer intercepts a live transaction while it’s waiting to confirm within the standard ten minutes.

This isn’t just a far-off problem anymore. It’s a ticking clock.

The whole network has to be post-quantum before that machine exists.

Bitcoin has no roadmap.

Bittensor has no roadmap I’m aware of.

Ethereum has a public migration roadmap targeting 2029.

So when I started researching this piece, my first question was simple: Should I be worried about my TAO?

What Quantum Actually Threatens in Bittensor

Here’s where I had to stop and really understand the mechanics.

The panic is missing the target. Quantum doesn’t threaten the blockchain itself, the chain of hashed blocks is safe.

What quantum does threaten is authenticity: your signature proving you authorized a transaction. The ledger stays intact; the trust in who controls what does not.

Think of it this way: Quantum computers can’t rewrite the blockchain’s history. But they could steal your keys and write new history pretending to be you. This is a general blockchain problem, but Bittensor’s agile subnet architecture gives it tools to experiment and adapt faster than monolithic chains.

Blockchains like Bitcoin are like oil tankers. Changing course takes years of planning, coordination, and risk.

Bittensor is more like a fleet of speedboats. Each subnet can test different approaches, fail fast, and share learnings across the network.

When the quantum threat becomes real, that flexibility matters.

This is what clicked for me: Bittensor doesn’t need to be first to solve quantum threats. It needs to be fast to implement when someone else does. And it has infrastructure that could help test those solutions.

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The Quantum Subnets: Building Both Sides of the Equation

While everyone else is either panicking about quantum threats or hyping quantum computing, Bittensor has subnets building both.

QBittensor Labs runs two related subnets that caught my attention:

SN63 (Enigma): The Research Lab

Think of this as the quantum computing gym where miners train to get better.

Validators generate difficult quantum problems: things like peaked circuits and hidden-stabilizer tasks. Miners compete to solve them using simulation software called Quantum Rings, which runs on regular hardware (no actual quantum computers required yet).

The best solutions get scored on correctness and efficiency. Winners earn emissions.

What This Actually Means: Miners are getting really good at quantum algorithms right now, using simulated quantum hardware. When real quantum computers become more accessible, these miners already know how to use them.

SN48 (Quantum Compute): The Production Marketplace

This is where the training becomes real work.

At OpenQuantum.com, anyone can submit quantum circuit jobs: computational problems that might benefit from quantum approaches. Miners execute these jobs on simulators or actual QPUs (Quantum Processing Units) if they have access to them.

Then validators issue “proof-of-quantumness” (yes this is a real phrase) challenges to verify the results actually came from quantum computation, not classical shortcuts.

Both subnets are at full capacity: 256 miners registered in each. They’ve generated over 300 million proofs and the difficulty curve keeps climbing

Why This Matters for Quantum Threats:

If you want to test whether a new post-quantum signature scheme actually resists quantum attacks, you need quantum computers to try attacking it.

Right now, only IBM, Google, and a handful of others have that hardware. It’s expensive and you’re basically asking permission from the same companies whose centralized models you’re trying to escape.

SN48 is building a permissionless alternative. Need to simulate quantum attacks on a proposed new signature scheme? Submit the job. Miners with QPU access compete to run it with results verified on-chain.

The Structural Hedge Nobody Saw Coming:

QBittensor Labs is playing both sides of the quantum revolution:

If quantum becomes a commercial compute primitive (drug discovery, financial modeling, optimization), SN63 and SN48 are early infrastructure for those markets.

If quantum arrives as a cryptographic threat faster than anyone’s ready, the QPU access becomes directly useful for testing post-quantum defenses.

And because it’s happening on decentralized markets, the progress is visible. You’re not waiting for a Google press release. You can watch circuit complexity scores, fidelity rates, and proof validation metrics update in real-time on Taostats.

The Part I’m Still Figuring Out:

I don’t have strong conviction on whether these subnets are good investments yet. They’re early-stage infrastructure plays in a technology that’s still maturing.

I’m watching for signs these subnets keep delivering. Are circuit complexities climbing? Is proof-of-quantumness validation holding up under scrutiny?

If yes, this could become the R&D lab for Bittensor’s own quantum-safe future. Testing post-quantum signatures requires simulating quantum attacks. That’s exactly what this infrastructure enables.

Your Quantum Readiness Framework

Here’s the framework I now use to decide if quantum panic is warranted.

The Three-Signal System:

Signal #1:

Does Bittensor publish a post-quantum migration roadmap? TAO runs on Substrate with sr25519 signatures. This is the same threat class as Bitcoin and Ethereum. Ethereum’s targeting 2029. Bittensor has nothing public yet.

Here’s the test I ran this week:

1. I checked the last 8 weeks of OpenDev call notes for any mention of “quantum” or “cryptography upgrades”

   • Result: Zero mentions

2. I searched the Bittensor, Church of RAO and TAO Community Discord for “quantum” going back 90 days

   • Result: Numerous mentions from community members asking. No official response.

3. I looked at Polkadot’s quantum discussions (since Bittensor runs on Substrate)

   • Result: Polkadot has an active RFC discussing post-quantum primitives.

Signal #2:

Are institutional players treating this as real? Watch for:

• Exchanges announcing post-quantum wallet support.

• Large validators discussing migration plans.

• Foundation developers mentioning it in OpenDev calls.

Signal #3:

Does the timeline keep compressing? If Google publishes another efficiency breakthrough, or if someone demonstrates Shor’s algorithm on a meaningfully large key, the clock speeds up.

What Changes: Move your TAO to the most actively developed wallet (likely the one that responds first to quantum concerns). Consider keeping a larger percentage in liquid form rather than locked in long-term staking.

The Bittensor Advantage (And Why I’m Not Worried Yet)

Here’s what I’ve come to understand about Bittensor’s position:

The Speedboat Principle: Bittensor can upgrade faster than Bitcoin or Ethereum. When post-quantum standards solidify (likely NIST’s recommendations), Bittensor’s substrate-based architecture means implementing new signature schemes is a runtime upgrade, not a multi-year fork negotiation (looking at you, Bitcoin).

The Hidden Defender: Targon (SN4) is Bittensor’s private compute subnet. It’s not a quantum security tool, but it does help protect data while it’s being used, which can reduce the risk of it being stolen and decrypted later.

The Reality Check: Bittensor will need to upgrade its cryptography. Every blockchain will. But panic helps no one. The timeline is 2029-2030 and Bittensor’s architecture is designed for exactly this kind of coordinated evolution.

The story is quieter than the noise suggests.

We have time. We have tools. We don’t have a comprehensive plan yet, but we’re watching the right signals.

Your Three-Step Action Plan

This Week: Check your subnet holdings for any mention of quantum preparedness. Just search their Discord or GitHub for “post-quantum” or “quantum resistant”. Note which ones are silent and which are discussing it.

This Month: Bookmark these two resources:

1. Polkadot’s RFC discussions on post-quantum primitives (since Bittensor inherits Substrate’s cryptography roadmap)

2. NIST’s Post-Quantum Cryptography standards page (to track when official recommendations are finalized)

This Year: Watch for Signal #1 from my framework above. If Bittensor doesn’t publish a post-quantum migration roadmap in 2026, while Ethereum has one for 2029, that’s worth asking about loudly.

The quantum threat is real and the timeline is compressing.

But we’re not helpless. We’re just early in a process that every blockchain is navigating together.

And unlike Bitcoin’s oil tanker, Bittensor’s speedboats can change direction fast when the course becomes clear.

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Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

On April 9th, Sam Dare published a letter accusing Bittensor of being a lie.

It was long, carefully written, and timed for maximum damage. It arrived alongside a purpose-built whistleblower website and a coordinated dump of $11 million in subnet alpha tokens, most of it sold the night before the letter went public and before his own community had any idea what was coming.

Let me walk through what happened, what I think it means, and why I’m not selling.

Reconstructing the Exit

For readers newer to this ecosystem, some context.

Covenant AI was not a fringe player. The team operated three subnets on Bittensor: Templar (SN3), Basilica (SN39), and Grail (SN81). Templar was the crown jewel: a decentralized pre-training environment that produced Covenant-72B, a 72-billion-parameter model trained across more than 70 independent contributors on commodity hardware and coordinated through the Bittensor incentive layer.

The achievement was real. NVIDIA CEO Jensen Huang praised it publicly. Chamath Palihapitiya mentioned it on the All-In podcast. TAO rallied 90% in the month that followed. It was a mainstream moment for Bittensor, the proof of concept the whole ecosystem had been working toward.

Then, on April 9th, Sam Dare, the founder of Covenant AI, posted his open letter announcing the withdrawal of his three subnets from the Bittensor ecosystem. TAO dropped 27% in two hours with nearly $900 million in market cap evaporated.

Dare’s letter laid out four specific allegations against Bittensor co-founder Jacob Steeves, known universally in the ecosystem as Const.

He alleges Const had:

1. Suspended emissions to Covenant’s subnets.

2. Removed the team’s moderation capabilities over their own community channels.

3. Unilaterally deprecated their subnet infrastructure.

4. Applied economic pressure through large, visible token sales timed to moments of conflict.

Explosive claims, but before we get to Const’s rebuttal, it’s worth understanding how Bittensor’s emission mechanics actually work. Because this is the technical detail that makes or breaks the most serious accusation.

Under dTAO, emissions are determined entirely by an on-chain algorithm. There is no admin key or pause function.

Each subnet’s share of daily TAO emissions is set by net TAO flows into its liquidity pool calculated automatically by the protocol, in the same way for every subnet without exception.

What Const can do, what any TAO holder can do, is buy or sell alpha tokens in those pools. When he sold his positions in Covenant’s three subnets, that reduced TAO in the pools and affected the flow-based emission calculation. The same way anyone’s sell would.

This is market mechanics at work. Emissions cannot be modified in any other way. Dare’s central accusation requires a technical capability that does not exist in the protocol.

Const addressed each claim directly. He stated:

1. He has no authority to pause emissions (as described above).

2. He did not remove Dare’s moderator role outright, but removed his ability to delete posts and later restored it. Community member Alex DRocks corroborated in real time, it was Dare who deprecated his own Discord channels, and the posts deleted were questions from community members asking why SN39 was replicating work another compute subnet had already done. Dare was deleting scrutiny.

3. He’s unsure what deprecating their subnet’s infrastructure actually means (yet to be confirmed).

4. His token sales (less than 1% of what he had invested in Covenant’s teams) were made because Covenant’s subnets were not running and were operating on near 100% burn code, affecting emissions the same way any buy or sell would.

The Rug Frame: Reading Sam Dare’s Exit Clearly

Now pay attention to the choreography.

On the same day as Dare’s open letter, a newly launched site called Tao Papers published internal documents and on-chain forensics from multiple whistleblowers.

The headline claim: of 41 network upgrades carried out between 2023 and 2026, 38 were proposed, signed, and deployed entirely from infrastructure controlled by Const, with the other two signers approving each upgrade within minutes and no public discussion on record.

Taken at face value, that’s worth examining properly.

But it is not spontaneous. You don’t build a forensic website overnight.

The fact that the open letter and the whistleblower site launched simultaneously tells you everything about the intent. This was a campaign planned and timed for maximum narrative impact on exit.

And the exit was the most malicious part.

Prior to the public announcement, Dare had already sold 37,000 TAO ($11m) worth of subnet alpha tokens across Templar, Grail, and Basilica.

The dump hit retail holders tied to Covenant’s projects before they had a single word of warning.

TAO’s sales volume had already reached its highest level since December 2024 in the 24 hours before the letter went public.

This was not a principled departure, but an exit strategy, wrapped in governance language, timed for maximum extraction.

The Tao Papers data may contain real information about governance concentration, but its framing as independent corroboration collapses the second you notice it arrived gift-wrapped, synchronised with the open letter, hours after the pre-announcement dump had already cleared.

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The Legitimate Critique Underneath the Drama

Let me be honest here, because this newsletter does not earn your trust by only telling you what you want to hear.

The Tao Papers governance data, whatever you think of Sam Dare’s motives, raises a legitimate question about Bittensor’s current governance structure.

38 of 41 network upgrades originating from a single founder’s infrastructure, with co-signers rubber-stamping within minutes, is not the picture of distributed governance.

The triumvirate structure, as currently operating, functions more as a legal architecture than a genuine check on centralized decision making.

Const has not explicitly addressed that structural critique. He responded to Dare’s specific allegations point by point. He did not address the broader claim that meaningful governance authority has not yet transferred.

I think that’s intentional, because his response is in line with a design pivot. His statement in the aftermath of the exit:

“This will prove to birth the first subnets on Bittensor that run headless and as true commodities.”

These are the words of a builder saying the criticism identified a real structural dependency, and the answer is to architect it away.

The reality is, decentralization is a gradual process for the good of the protocol.

Every major network retains concentrated authority early to move fast and prevent paralysis, then systematically transfers power as architecture matures. That transition creates friction. Big players in the ecosystem: subnet founders, major investors, validators, all have different timelines for when centralization should end.

When those views collide with financial stakes this large, exits like Dare’s become possible.

Const stepped down as CEO of the Opentensor Foundation two months ago, explicitly to push decentralization forward. This episode accelerates that trajectory.

Revealing a structural weakness is different from being terminally broken. Every serious protocol has gone through this and now we will see how the Bittensor ecosystem responds.

The Road to Full Decentralisation: What Changes Now

The specific structural reform announced in the aftermath of this crisis is worth understanding clearly, because it directly addresses the failure mode that Dare exploited.

Bittensor is introducing lock-based subnet ownership.

The mechanics: ownership of a subnet will be determined by a team’s long-term economic commitment, not just their technical presence.

Investors will have transparent, advance notice if a subnet owner unlocks their tokens, allowing the market to reprice before founders can use their communities as exit liquidity.

The system will also allow investors to shift their staked capital fluidly to alternative management teams.

If lock-based subnet ownership had existed on April 9th, every holder in Templar, Basilica, and Grail would have seen the unlock coming before the dump hit.

The market would have repriced on Dare’s decision to exit, not on his public announcement of it. The $900 million wipeout would have been a controlled, visible repricing rather than a midnight ambush.

Autonomous, headless subnets, running as true commodities without a single operator as a point of failure, are the longer-term architectural answer. The Covenant crisis exposed a network that is mid-decentralization, under pressure and now moving faster.

Stillcore Capital (Bittensor Fund) partner Mark Jeffrey put it plainly:

“Bittensor is a quite a lot more than Subnet 3, and TAO will carry on fine without it.”

He is right. And multiple big players have come out in support of Const in the aftermath of this incident. But I’d go further.

Bittensor is stronger for this than it was two weeks ago. The lock-based model and the headless subnet architecture were coming. This crisis brought them forward.

What Happens Now for Covenant and the Three Subnets

Covenant operated three subnets: Templar (SN3), Basilica (SN39), and Grail (SN81). Templar was the crown jewel — the pre-training environment that produced Covenant-72B. Basilica was focused on decentralised compute. Grail handled more complex task-driven AI workloads. All three now show as deprecated on Taostats, removed from active participation in the network.

The team will continue developing Covenant-72B off-network, with new project announcements promised but no ecosystem named.

They’ll be building without the TAO incentive layer, without the established subnet infrastructure and carrying the reputational weight of what much of the community now views as a coordinated exit dump.

New investors in whatever comes next will be asking the same question Bittensor holders are asking today: who’s really in control, and when are they planning to leave?

For Bittensor, the three subnets now need transition to new operators or a headless model, and the process is untested at this scale. If the transition is messy, it reinforces the single-point-of-failure narrative.

If it goes smoothly, it validates Const’s “true commodities” vision: that subnets should eventually run without any dependency on their founding team.

That transition is the first real test of everything Const said would come next. I’ll be watching it closely.

What the Price Action Was and What It Wasn’t

When a high-profile governance headline breaks alongside a prominent team exit and a visible token dump, the market reacts predictably.

Traders operating on shorter timeframes, whether that’s algorithms, momentum players, or simply anyone with a stop-loss or a risk tolerance that got breached, will sell into uncertainty.

Not because they’ve evaluated whether Sam Dare’s accusations are credible or Const’s rebuttals hold up. Because uncertainty itself is the signal.

Even longer-term investors who might otherwise hold through volatility often reduce exposure when a story that felt clean suddenly feels complicated. They’re not abandoning their thesis necessarily, they’re stepping back until the fog clears.

That is also rational behavior. Preservation comes before conviction when the facts are in dispute.

Your job, if you hold TAO with conviction, is not to front-run these reactions or predict when sentiment will stabilize.

It is to ask different questions entirely:

• Has the network stopped working?

• Has the underlying technology been invalidated?

• Has anything happened here that changes the fundamental thesis?

• Has the team with the long-term vision left, or the team that just cashed out?

Those are the questions that matter. The price tells you what the market did on April 9th. It does not tell you what TAO is worth in 2027 and beyond.

Ecosystems Have Always Had Bad Actors: The Price of Upside

Here is what I want non-crypto readers to understand. And what I want crypto natives to remember.

Every transformative decentralized network has had this moment. More than once.

Bitcoin didn’t emerge from consensus and goodwill. It emerged from years of the block size wars, a community riven between competing visions, ultimately splitting into Bitcoin and Bitcoin Cash, and then Bitcoin Cash splitting again into Bitcoin Cash and Bitcoin SV, driven by Craig Wright’s years of fraudulent Satoshi claims, litigation, and manipulation.

Those were genuine existential moments. Bitcoin absorbed all of it. The network is worth over a trillion dollars today.

Ethereum’s defining early crisis was the DAO hack in 2016. A vulnerability was exploited, $60 million drained, and the community faced an impossible choice: honor the immutability they had promised, or fork the chain to recover the funds. They forked. Half the community followed.

The other half stayed on the original chain and called it Ethereum Classic. People said Ethereum would never recover its credibility. ETH is now the settlement layer for trillions of dollars of financial activity.

The pattern is consistent. Early-stage decentralised ecosystems attract the most visionary builders and the most opportunistic actors simultaneously, because the rules are still being written.

Power is concentrated by necessity before it can be distributed by design. And some people see that window and take it.

This is the cost of being early to asymmetric upside.

If you wanted zero drama and full governance maturity, you would be buying index funds. You are here because the upside on the early innings of decentralized AI infrastructure is unlike anything in a passive portfolio.

That upside does not come without episodes like this one.

Reading the Aftermath

What This Builds

Lock-based ownership is a genuine structural improvement, not a PR response. The headless subnet architecture, if it delivers, creates a more resilient and more trustworthy system than what Covenant left behind. The ecosystem survived one of its largest subnet’s exit without collapse.

What to Monitor

Watch two things.

First: how cleanly SN3, SN39, and SN81 transition. Smooth operator handoffs would validate the true commodities thesis and demonstrate that Bittensor’s incentive layer is more durable than any single team.

Second: where Covenant deploys next. If they name a competing ecosystem, the protocol-switching cost argument becomes live and worth monitoring.

What Remains Unresolved

The triumvirate governance concentration is real and not yet fully resolved. Const’s point-by-point rebuttals were credible. The structural critique has not been fully answered. Further prominent departures, especially if they cite similar governance concerns, would compound the credibility damage faster than any structural reform could repair it.

What Dies Here

The narrative that any single subnet team is Bittensor. This moment definitively ends that story, which is uncomfortable in the short term and healthy in the long term. The ecosystem should never again be this exposed to a single founder’s exit.

The Morning After

There is a difference between having a genuine grievance and performing one for maximum effect.

Real concerns deserve real processes. You don’t burn the room down to prove the room had a fire hazard. And when someone times their concern to the moment of maximum personal extraction, you learn important things about them more so than their peers.

The Bittensor network is still running. The technology that produced Covenant-72B still exists, built on infrastructure that no departing team can take with them.

The builders who stayed chose to stay. And the structural reforms now in motion were always where this ecosystem was heading.

The price will do what prices do. I’m watching the transitions.

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Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

If you spent March focusing on your day job or your family instead of doom-scrolling the Bittensor Discord, congratulations. You made the right choice.

The ecosystem was incredibly loud this month, filled with X hype and subnet drama. Here is the pure signal: the 4 things that actually moved the needle for your portfolio and understanding of the network this month.

How to Read This Compass

I'm organizing these updates by direction: North, East, South, West to give you a repeatable mental model for navigating Bittensor's complexity.

North: Foundation (governance, protocol, core team, security events)
The structural layer. Changes here determine who controls the network and how decisions get made.

East: Capital (markets, volume, liquidity, integrations)
The economic layer. This is where price meets liquidity and institutional conviction shows up in the data.

South: Technology (shipped features, benchmarks, infrastructure, ecosystem health)
The execution layer. What actually works, what got built and where’s the technical proof.

West: Adoption (narrative, mainstream validation, external voices)
The perception layer. This is how the outside world sees Bittensor, from industry titans to normie investors.

Each month, some directions will dominate the signal. Others will stay quiet. The goal is to see which quadrants are moving and what that means for your strategy.

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[Foundation] The Founders Step Down and a Leap Towards Full Decentralization

• What Happened: In an unexpected shift, on the This Week in Startups podcast Ala Shaabana officially confirmed his departure from the Opentensor Foundation to focus on Crucible Labs, following co-founder Jacob Steeves’ exit in February.

• Why It Matters: In my years in the corporate world, I never saw a CEO willingly give up the corner office to go back to the trenches. But that’s exactly what Jacob and now Ala are doing.

  They are mirroring Bitcoin’s path, stepping away from the base chain to build crucial infrastructure (wallets, subnets) on top of it.

  It reduces our key-person risk and proves the incentive layer works, they’d rather be competing in the trenches than sitting in the foundation.

• The Takeaway: Watch what Crucible Labs and Steeves’ new subnet Affine produce; the founders are now competing and building within the very incentive layer they created.

[Capital] TAO’s Monster Rally

• What Happened: TAO experienced a massive breakout, surging over 67% in the calendar month and hitting highs around $377+, fueled by Alpha token demand exploding ecosystem-wide.

• Why It Matters: Whenever TAO or any large cap crypto goes on a massive run like we saw this month, I always get friends asking if they missed the boat. But I’m not staring at the price; I’m staring at the $900 million in daily trading volume on consecutive days. That level of deep liquidity means institutional money is finally treating Bittensor as a standalone, blue-chip infrastructure play rather than just a retail hype cycle. For my own portfolio, this volume is the strongest signal of conviction we’ve seen yet.

• The Takeaway: Track the daily volume metrics just as closely as the price. Maintaining a high level is the liquidity moat required to keep institutional funds flowing into the network.

  

[Technology] Covenant-72B Advances

• What Happened: Covenant-72B, the largest decentralized LLM training project on the network (on Subnet 3 Templar), made major strides this month by officially matching the performance of centralized, open-source baselines on key reasoning benchmarks. Rather than relying on a single, massive server farm, Covenant successfully trained a 72-billion parameter model across a permissionless, globally distributed network of GPUs.

• Why It Matters: Watching a huge LLM being trained across a global, decentralized network absolutely blows my mind. For the longest time, critics called Bittensor a speculative tokenomics experiment, but Covenant-72B shatters that narrative. It proves the network can actually handle production-scale AI compute that legitimately rivals the massive, centralized tech labs.

• The Takeaway: Subnet 3 (Templar) emissions just became a serious bet. Expect miner incentives to surge as builders chase Covenant-style workloads, pulling more TAO into high-utility delegation pools.

[Adoption] The Attention of Industry Titans

• What Happened: NVIDIA CEO Jensen Huang explicitly endorsed Bittensor on the All-In Podcast and Anthropic co-founder Jack Clark talked up a “meaningful win” for the network in his Import AI newsletter.

• Why It Matters: For the last year, whenever I (rarely) tried to explain Bittensor in corporate settings or internal tech discussions, I’d usually get polite, blank stares. But with NVIDIA’s Jensen Huang and Anthropic’s Jack Clark publicly acknowledging the network this month (on the back of Templar's distributed training of the 72-billion-parameter model), they essentially did my job for me.

  When the world’s biggest AI hardware supplier and a top frontier model architect validate the ecosystem, it mainstreams our decentralized thesis overnight and makes the elevator pitch to TradFi peers a whole lot easier.

• The Takeaway: Use these endorsements to explain Bittensor to your TradFi and Web2 peers. It’s the ultimate elevator pitch validator.

When I look at this list, the shift that impacts my own strategy the most is the remaining co-founder stepping down from the Foundation to focus solely on building Crucible Labs. It’s a massive de-risking event for the network. Removing this centralized point of failure is what truly shifts TAO from a speculative bet into resilient public infrastructure.

The one risk I’m still sitting with, and the trade off worth watching this year: Crucible Labs, Affine (Steeves’ subnet) and the founders’ reputations are now tightly coupled. If they stumble publicly, the halo effect reverses.

Which of these impacts your TAO strategy the most? Are you more bullish on the founders’ stepping back, or the macro endorsements from leaders like Jensen Huang?

And if I missed something important from March, what would you add to this list?

Reply to this email and let me know. I read every single one.

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Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

Take a look at the calendar. It’s March 2026.

If you want to understand the sheer magnitude of what Bittensor has become over the last year, look at what just captured the attention of the most important hardware company on the planet. Last week, Nvidia’s CEO, Jensen Huang, publicly praised Bittensor.

This wasn’t just a polite nod to a novel technology, it was validation of a milestone Silicon Valley traditionally thought impossible. Inside Subnet 3 (Templar), a group of 70+ independent, uncoordinated contributors just pulled off the largest permissionless, decentralized LLM pre-training run on record.

Using ordinary commodity GPUs and standard home internet connections, this swarm successfully pre-trained a massive open-weight LLM in a fully decentralized way across the open internet.

This is the exact moment a technology crosses the chasm. The mainstream tech media isn’t just covering Bittensor as a crypto token anymore; they’re covering it as a legitimate existential competitor to the centralized AI data center.

But signals can mean three very different things depending on what happens next.

That Nvidia validation could be the beginning of Bittensor becoming the default infrastructure layer for AI.

Or it could be a brief moment of hype before the ecosystem retreats back into the crypto bubble.

Or (most likely) it could be something in between: a quiet B2B backend that powers specific workloads while the giants dominate everything else.

Today, I’m not giving you a price prediction. I’m giving you a compass.

We’re going to walk through three concrete futures for Bittensor—Bear, Base, and Bull—and for each one, we’ll ask the only question that matters: What has to be true for this to happen?

By the end, you’ll have a framework to grade your own conviction. Not based on hopium or FUD, but on trackable, real-world metrics you can monitor over the next 12 months.

Scenario 1: The Niche Network (Bear Case)

Probability (by March 2027): 30–50%

The 2027 snapshot: Your friend at the bar still asks “what’s TAO?” Most subnets remain crypto-native tools, judged by on-chain rewards rather than broad external demand.

How to think about this scenario:

• Product: Bittensor has lots of subnets, but very few are profitable outside the crypto bubble. The best are judged by on‑chain rewards, not real‑world revenue.

• Adoption: You rarely hear Bittensor mentioned by non‑crypto founders. Discussion stays on X and Discord, obsessing over yields.

• UX: Trying to use Bittensor directly still requires wrestling with wallets and gas. Gateways exist but see minimal non-crypto traffic.

• Competition: Big AI players keep getting cheaper. Bittensor’s main advantage remains purely ideological, which doesn’t justify moving real enterprise workloads.

Your Investor Thesis: TAO is mostly “decentralized AI’s native token,” not a must‑hold infrastructure asset. You’re betting the ecosystem stays alive for crypto‑native builders, but not the rest of the world.

Litmus Test (March 2027): If fewer than 5 subnets have recurring external revenue, or most developers still need wallet setup for basic access, you’re in the Niche Network outcome.

Scenario 2: The B2B Backend (Base Case)

Probability (by March 2027): 40–60%

The 2027 snapshot: Your company’s DevOps team quietly uses Bittensor for running inference on internal tooling and doesn’t tell you. When you discover it and ask why, they shrug: “It was cheaper and faster than the alternatives.”

How to think about this scenario:

• Product: A small group of subnets (5–15) stand out with real‑world utility and growing revenue. Like Chutes for inference, Hippius for storage, Score for computer vision, each carving out a defensible niche.

• Adoption: Bittensor‑backed services are mentioned in technical blogs and internal product discussions. Mid‑tier tech companies quietly use it for large‑scale model fine‑tuning and inference, synthetic data, or coding tools.

• UX: User‑friendly gateways exist: “Pay in dollars, we handle the crypto plumbing.” Integrating a subnet feels like integrating any normal SaaS API.

• Competition: Big clouds dominate, but Bittensor carves out niches where it’s definitively cheaper or more flexible.

Your Investor Thesis: TAO is an infrastructure token tied to real AI usage. You’re betting on Bittensor becoming a quietly important layer for specific workloads.

Litmus Test (March 2027): If mid‑tier tech companies are quietly paying fiat invoices for AI services running on Bittensor, and developers use them without touching wallets, you’re in the B2B Backend outcome.

Scenario 3: The Decentralized Singularity (Bull Case)

Probability (by March 2027): 5–10% (This is more of a 2029+ upside).

The 2027 snapshot: You pay for AI the way you pay for electricity. You have no idea where it comes from, but Bittensor is powering a meaningful chunk of the inference requests happening across the internet. Censorship-resistant, cheaper, and good enough that enterprises are publicly announcing they’ve shifted workloads away from AWS.

How to think about this scenario:

• Product: A visible top tier of subnets dominates with multi‑million‑dollar revenue streams. The narrative shifts entirely from token emissions to “which subnet is the best at doing X?”

• Adoption: Mainstream non‑crypto founders talk about Bittensor‑backed products as “the best option.” Companies actively seek out this infrastructure for its performance and cost.

• UX: A “Stripe for Bittensor” exists. You sign up with an email and credit card, and the blockchain layer is completely abstracted away.

• Competition: Centralized giants still exist, but Bittensor offers credible neutrality and censorship‑resistant access. Major companies publicly shift significant workloads away from hyperscalers to Bittensor.

Your Investor Thesis: TAO becomes a critical infrastructure token for the AI era. You are betting it becomes the default decentralized layer for AI, accruing massive value as independent businesses rely on it.

Litmus Test (March 2027): If a non‑crypto developer googles ‘fastest cheap LLM inference API,’ lands on a Bittensor‑powered gateway and adopts it purely on its merits, and major enterprises publicly announce shifting meaningful workloads to the network, you’re on the Decentralized Singularity path.

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How to Grade These Scenarios (The Four-Dimension Compass)

Think of building Bittensor like opening a restaurant in a city dominated by McDonald’s.

For your restaurant to survive and potentially thrive, four things have to be true:

1. The Food Has to Be Great (Quality)
With the 128-subnet cap, the question isn’t how many subnets exist, but whether the competitive pruning mechanism has eliminated the mediocre ones. By 2027, have we evolved from 128 experiments into a consolidated leaderboard of high-revenue, undeniable utility subnets?

2. People Need to Hear About It (Distribution)
Are non-crypto developers and mainstream AI media talking about Bittensor organically? If we’re still sitting in an echo chamber on Discord arguing about validator yields in 2027, we’re not there yet.

3. Ordering Has to Be Easy (Developer Experience)
The “Stripe for Crypto” test. Ten years ago, Stripe won the internet by reducing payments to three lines of code. How many clicks does it take a Web2 developer to use a Bittensor subnet today? If they have to understand seed phrases and gas fees, the friction is too high.

4. You’re Competing With McDonald’s Next Door (Competition)
Centralized tech giants are pouring tens of billions into proprietary models. Can Bittensor maintain a legitimate cost, open-source and privacy advantage against that tidal wave of capital?

Over the next year, these four dimensions will tell you which scenario we’re heading toward. Not token price, but real-world adoption metrics.

Why I Built This Framework (And Why Crypto Metrics Are Broken)

A few weeks ago, I found myself staring at a subnet analytics dashboard, feeling completely paralyzed.

And I’ve been tracking Bittensor regularly for a long time now, I’m supposed to understand (mostly) this stuff.

But I was looking at Alpha/TAO price ratios, emission curves, validator weights, staking APYs, and I still felt lost.

Then a familiar realization hit me: None of these numbers tell me if a subnet is actually building a good product.

So I did something simple: I texted a friend who works closely with Enterprise AI at a TradFi company and asked: “What would it take for you to stop paying AWS and OpenAI, and route your company’s AI requests through open-source intelligence instead?”

His answer had nothing to do with tokenomics. Before it even got close to approval at board level, he cared about four things:

Is the output high quality?

Has anyone else in his industry tested it?

Is the API easy to plug into existing code?

Is it significantly cheaper or more private than what Sam Altman is selling him?

That conversation became the four-dimension compass above. It’s how I’m grading Bittensor’s trajectory as someone betting on teams building software the real world actually wants to use.

The Playbook: How to Be a Smart “Involuntary VC” Today

If these three scenarios are the maps, how do you navigate your capital today? The first decision you need to make is fundamental: Are you investing in the protocol or the applications?

Option 1: The Protocol Bet (TAO Only)

You’re buying the thesis that Bittensor itself becomes critical infrastructure, regardless of which specific subnets win. This is the “buy the index, not the stocks” approach.

• Base/Bull lean: Your conviction is that TAO accrues value as the settlement layer for decentralized AI, regardless of which subnets succeed. You hold TAO and ignore subnet tokens entirely. You’re betting on aggregate network growth, not picking winners.

• Bear lean: You stay mostly liquid or allocate minimally. You monitor whether TAO maintains its premium to Bitcoin/Ethereum, and watch for signs the network is actually capturing value from real usage versus pure speculation.

Option 2: The Subnet Bet (Alpha Tokens)

You believe specific subnets will create massive value, and you want direct exposure to those winners. This is venture capital: high risk, high potential return, requires active management.

• Base/Bull lean: Your job is to find the teams building the bridges. Diversify your stake across 3-5 high-conviction Alpha tokens. Look specifically for subnet owners actively building strong Web2 frontends, identifying clear end-customers and abstracting the token entirely away from the user experience.

• Bear lean: Your focus is capital preservation. Monitor subnet liquidity heavily. Thin Alpha pools mean highly volatile exits if a subnet fails its competitive metrics. Watch 30-day net TAO flows on platforms like Taostats—the direction of capital moving into or out of a subnet tells you more about its survival chances than its current Alpha/TAO price ratio.

This week’s action step: Pick one subnet in the top 15 emissions. Spend 30 minutes using their product as a non-crypto user would. Does it require a wallet? Is it faster/cheaper than ChatGPT? Grade it against the four dimensions above. If you can’t figure out how to use it without reading docs for an hour, that’s a potential red flag.

The 2027 Time Capsule

A compass is only useful if you actually check it against your surroundings. So, I want to hear from you.

Vote in the poll or hit reply to this email and tell me: which of these three scenarios do you assign the highest probability to, and why? I read every response, and the smartest insights make it into future issues.

Exactly one year from today, we’ll open this email back up together. We’ll grade these exact “What Has To Be True” conditions, step by step. We’re going to look at the metrics, the ecosystem and see exactly which timeline we manifested.

I’ll see you in the future.

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Until next time,

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

I didn’t check if it was up to date.

I clicked confirm. Then I went to make coffee.

When I came back to my desk, I refreshed the exchange. Nothing.

That’s when the cold feeling started spreading from my chest outward.

I pulled up the transaction hash. Confirmed on the blockchain. The ETH had definitely left my wallet. It had gone somewhere.

The exchange had upgraded their security infrastructure and rotated their deposit addresses. My ETH had gone straight into the void, not to a scammer, just to an address that no longer existed in their system.

My hands went numb. There’s no emergency undo button in crypto. Just me, a blockchain that doesn’t care, and an exchange support ticket system that takes an age to respond.

Cue a very nervous couple of weeks checking my email every few hours, imagining explaining to my wife that I’d basically thrown thousands of dollars into a digital trash can.

When the support team finally replied saying they had manually retrieved the funds, I actually stood up from my desk. The relief was physical.

It was a very lucky escape.

Now picture that same scenario, but with your TAO and this time there is no exchange involved. There’s no customer support to email. No manual retrieval. The blockchain is immutable and the funds are just gone.

Most crypto losses are simple human mistakes. We obsess over the technology—hardware wallets, encryption, multi-sig—while completely ignoring the human element that leaves the back door wide open.

This is especially true here. Bittensor is a machine intelligence network.

But it runs on a blockchain.

You might be here purely for the AI, but the underlying plumbing is crypto. Even if you don’t care about DeFi or web3, if you hold TAO, you are operating in a crypto-adjacent world. And that means we have to adopt their security practices to protect our assets.

So lets make sure we don’t lose them.

Why Security Actually Matters (Beyond Fear)

The reason we’re obsessing over security isn’t because TAO is doomed to be stolen. It’s because TAO is worth securing.

You’re protecting your stake in the only decentralized AI network that’s actually working. You’re protecting a bet that monopoly AI eventually annoys enough people that alternatives become valuable.

And if you follow three boring steps: offline seed phrase, isolated environment, routine checks—you’re already in the top 10% of secure holders. The bar is shockingly low.

Don’t think that because TAO is trading at $200 as I write this, security doesn’t matter yet. What happens when it’s 10x from here? Or 30x? You know the network has that potential.

You need to treat your TAO like it’s worth what it could be in 5-10 years, not what it’s worth today.

Remember the guy who threw away a hard drive containing thousands of BTC? It’s sitting in a UK landfill right now, now worth hundreds of millions of dollars. He was careless when Bitcoin felt like Monopoly money.

We don’t want to be that person in 2035, frantically trying to remember what happened with our TAO that was casually bought and lost.

How Bittensor Guru Got Duped

Because scammers can’t hack the Bittensor blockchain, they don’t even try. Instead, they try to hack you.

Right now, the number one social engineering threat in crypto is Social Media DMs.

Take Keith Singery’s story. Better known as Bittensor Guru, Keith is a sharp, early Bittensor validator who lived in the Discords and codebases every day.

He understood the tech, the risks, the culture. And he still lost over 5,400 TAO ($300k+) to a Discord impersonator.

Keith was running a Bittensor validator and holding others’ TAO on his cold wallet. In early 2023, a scammer impersonated one of his delegators and tricked him into sending TAO to the wrong address.

He’d normally double‑check via a second channel, but skipped his usual checks that morning. The scammer had reused an old DM, changed their name and avatar to match the real user, and mimicked their writing style.

After the loss, Keith tightened his security, removed most third‑party funds from his validator and took an extended break from the ecosystem.

This is where the reality hits. There is nobody to call if you mess up.

We have to build systems that guard against the stupidest, most tired versions of ourselves.

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The Threats You Actually Face

Let’s strip away the complexity of what a wallet actually is.

At its core, a wallet is just two things:

• A public key: Your routing number. It’s how people send you funds.

• A private key/seed phrase: The master vault combination. It gives total control.

The cardinal rule of crypto is that your seed phrase never touches a screenshot, a cloud note, or a random Wi-Fi printer. You write it on paper or engrave it on metal. You never copy-paste it into a chat box.

(For a deeper dive on which specific wallets to use, check out The 2026 Guide to Bittensor Wallets).

Now let’s break down what you’re really defending against, because abstract security advice doesn’t stick:

Social Engineering (Discord DMs, Fake Upgrades):

• How it works: Someone impersonates a validator, subnet owner, or support rep. They convince you to send funds to their address. Or they send you a link to “upgrade your wallet.” You enter your seed phrase. They drain your wallet.

• Real example: Keith Singery lost 5,400 TAO this way despite being deeply technical.

• Defense: Only use your seed phrase if the idea originated with you. Never click links in DMs. Reach staking sites via your own bookmarks. Don’t rely on Google search results, which are frequently hijacked by malicious ads.

Operational Mistakes (Wrong Address, Fat-Finger Errors):

• How it works: You send TAO to the wrong address (typo, outdated saved address, wrong network). The blockchain doesn’t care that you made a mistake—the transaction is final.

• Real example: My ETH exchange mistake from earlier.

• Defense: Verify every character, send test amounts first, never rush.

Theft (Seed Phrase Compromise):

• How it works: Your seed phrase gets photographed, screenshotted, saved in cloud notes, or typed into a compromised device. Someone finds it and drains your wallet while you sleep.

• Real example: Countless “I lost everything” posts where people stored seeds digitally.

• Defense: Seed phrases never touch screens. Paper or metal only.

Environment Risk (Compromised Device, Malware):

• How it works: You use a wallet on a device loaded with malware, keyloggers, or malicious browser extensions. Even without your seed, sophisticated attacks can trick you into signing malicious transactions.

• Real example: Browser extension scams that clone legitimate wallet interfaces.

• Defense: Dedicated device or isolated browser profile for your TAO activity only.

The Framework: The 3-Tier TAO Setup

Bittensor’s blockchain architecture actually gives us a massive advantage: the coldkey and the hotkey.

Think of your coldkey as your identity. It’s your vault. It holds the power. Think of your hotkey as a worker badge. It goes out, does the daily work on subnets, and can be replaced if lost.

Each tier below addresses specific threats and gives you concrete defenses.

Pick your lane:

1) The Everyday Participant (Low-Medium Holdings)

You hold TAO, delegate, and don’t run miners yourself.

The Setup:

• Keep your serious TAO on one main hardware wallet (Ledger + Crucible/Talisman/TAO.com/Nova)

• Set up a “burner” browser wallet with smaller amounts for playing with subnets

• Seed phrase: Handwritten on paper, stored in fireproof safe at home

• Environment: Dedicated browser profile for crypto only (no shopping, no random extensions)

Threats This Solves:

✅ Phishing: Your serious stack never touches the internet

✅ Operational mistakes: Test transactions happen in burner wallet first

✅ Environment risk: Isolated browser means malware can’t see your main wallet

If all you do after reading this piece is separate “experiment TAO” from “serious TAO”, you’ve already upgraded.

2) The Active Operator (Miners / Validators / Builders)

You run miners, validate, and interact with subnets frequently.

The Setup:

• Coldkey: Hardware wallet, never touches servers. Only signs big moves (staking, redelegating, withdrawals)

• Hotkeys: Live on your servers, funded minimally

• Seed phrase: Split storage: one copy at home, one copy in safe deposit box

• Transaction verification: Every coldkey transaction gets verified on two different devices before signing

Threats This Solves:

✅ Theft: Even if your server is compromised, attacker can’t access coldkey

✅ Operational mistakes: Dual-device verification catches wrong addresses

✅ Social engineering: Your process doesn’t allow rushing as verification takes time

Move slow and carefully with the coldkey. Move fast with hotkeys. Never keep more power on the hotkey than absolutely necessary.

3) The High-Stakes Holder

You have significant TAO and a multi-year horizon.

The Setup:

• Primary: Hardware wallet, never touches servers

• Seed phrase: Written on metal (like Cryptosteel), stored in two separate physical locations (bank deposit box and one other location)

• Environment: Dedicated laptop for TAO only, never used for email/browsing/downloads

• SOP document: Written instructions for every fund movement, stored separately from seed

• Recovery test: Annual test recovery with small wallet to ensure process still works

Threats This Solves:

✅ All of the above, plus...

✅ Physical disaster: Fire/flood can’t destroy both seed copies

✅ Death/incapacity: Your family can access funds with clear instructions

✅ Memory failure: 5 years from now, you remember the process because it’s documented

If your future self finds your setup obvious and dull, you’ve done it right. Boring is good.

The Hit-By-A-Bus Plan

This is the big gap in most security guides.

What happens if you vanish tomorrow?

Don’t build a fortress so strong that your own family can’t get in. As Pamela Morgan says in her seminal work Cryptoasset Inheritance Planning: you need a clear, boring backup process.

Design recovery for:

• Forgotten password but seed still safe

• Device lost or stolen

• One copy of seed damaged (fire, water, etc.)

• Death/incapacity: can anyone you trust eventually access funds?

  

What I’ve actually implemented:

1. The Recovery Document (stored separately from seed):

   • “If something happens to me, here’s what TAO is and why it matters”

   • Exact locations of all seed phrase copies (e.g. home safe, bank deposit box)

   • Step-by-step wallet recovery instructions in non-technical language

   • Contact info for two trusted helpers.

2. The Annual Test:

   • Once a year or so, I set up a fresh wallet with a small amount of crypto

   • Intentionally wipe it

   • Recover it from seed phrase following my own instructions

   • This ensures: (a) my seeds still work, (b) my instructions are clear, (c) the process is muscle memory

3. The Conversation:

   • I’ve walked my wife through the recovery document once

   • She knows where it is

   • She knows it exists

   • She doesn’t need to understand subnet emissions, just how to access the value if I’m gone

This may sound like overkill, but it gives you peace of mind. The best time to practice recovery is when nothing is on fire.

Adversarial By Design

Bittensor expects bad actors.

The entire protocol is designed assuming every participant is trying to game the system. In fact, the ecosystem treats exploiters as the best red-team testers. People are implicitly paid to break things so the protocol can harden.

If the protocol evolves under adversarial pressure, we have to do the same. Not with paranoia, but with preparation.

Pick exactly one thing from this issue and do it this week. Set up an experiment wallet. Buy a hardware wallet (Ledger for TAO compatibility). Write down your “Hit-By-A-Bus” plan.

Remember: Bittensor is an infinite game. You’re not trying to time the perfect exit, you’re trying to stay in the game long enough for the network to mature.

Security isn’t about being paranoid. It’s about being still holding TAO in 2030 when the rest of the world finally catches up.

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Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

The lights are off. The house is quiet. You take a step.

And suddenly, your foot finds it.

A single, rogue LEGO brick.

Last weekend I sorted LEGOs for an hour. Not by choice, after stepping on three bricks in two days, I’d had enough.

But somewhere in that pile of Spidey and Bluey pieces, I saw Bittensor.

On the floor, those bricks are just sharp edges. But snap them together with an instruction manual and that clutter becomes Bluey’s house.

As my four-year-old would tell you, if he wasn’t screaming about a missing piece he flung away in rage five minutes earlier, the value isn’t in the brick.

It’s in the assembly.

For the last year, Bittensor has felt exactly like my living room floor.

We have the absolute best pieces in the world: subnets that generate incredible text, scrape the entire internet in real-time, handle complex mathematics. But they’re scattered and they function in isolation. Brilliant, lonely bricks.

If you’re an enterprise looking at Bittensor today, you don’t see a functional machine: you see a floor covered in sharp objects that looks painful to navigate.

To turn a pile of plastic into a vehicle that can actually drive, you need a mechanism that snaps them together.

You need a Master Builder.

The Master Builder’s Blueprint

This is where Affine (Subnet 120) changes the game. And it’s no accident who is building it.

The team includes Jacob Steeves (Const), the co-founder of Bittensor itself. He recently stepped down as CEO of the Opentensor Foundation, not to retire, but to build in the trenches again.

When the architect of the entire network decides to focus his time on one specific subnet, that’s a signal to pay attention to. He’s the literal Master Builder coming back to show us how the pieces fit.

Here’s what Affine actually does: It coordinates other subnets so they can work together through reinforcement learning and model evaluation.

In other words, its an invisible gym where the world’s smartest open-source AIs train 24/7 to get better at reasoning.

It doesn’t sell you a chatbot. It runs a never-ending competition: miners fine-tune models on hard puzzles (coding, multi-step logic, program synthesis). They upload the improved model to a public library (Subnet 64: Chutes).

Validators test every model head-to-head. The single best one wins the entire daily prize pool and every other miner immediately downloads it and tries to beat it next round.

The winner becomes the new baseline for the whole network. This is decentralized intelligence that keeps leveling up automatically, forever.

Think of it as the neural spine of Bittensor. The connector that glues 100+ specialized subnets into one evolving super-brain instead of 100 isolated apps.

Open https://www.affine.io/

You’ll instantly see:

• A leaderboard of real models ranked by how much money they’re earning right now (top one often $1,000–$1,300 per hour)

• Scores on actual benchmarks (LiveCode, GPQA math, coding challenges, etc.)

• A radial chart + “COMPARE” button so you can click two models side-by-side

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The Machine Comes Alive: How the Pieces Snap Together

If Affine is the Master Builder, what exactly is it building?

Affine has positioned itself as the glue that snaps the most powerful, specialized blocks in the ecosystem together:

1. The Hardware Bridge: Integration with Chutes (Subnet 64)

The most critical technical update is Affine’s successful integration with Chutes (Subnet 64).

Think of this as the bridge between mind and muscle. Affine provides the high-level logic and reasoning agents, but those agents need a physical place to run.

By integrating with Chutes, Affine effectively bridges the gap between software and hardware. It allows Affine to utilize Chutes’ decentralized compute and model hosting capabilities instantly.

The Result: A seamless pipeline where logic/reasoning agents (the software) are automatically deployed onto scalable compute resources (the hardware). The LEGOs aren’t just sitting there; they are now powered on.

2. The Composition Layer: Making Subnets Talk

In ecosystem analysis, Affine is increasingly described as “The Bridge” or a “composition layer.”

Until now, subnets have largely been isolated silos. A forecasting subnet makes a prediction, and it stays there. A trading subnet executes a trade, and it stays there.

Affine’s core utility is allowing these subnets to talk to each other. It enables the output of one subnet to serve as the input for another.

• Example: Affine takes a market prediction from a forecasting subnet and feeds it directly into a trading bot or agentic subnet to execute a strategy.

Affine’s architecture is fundamentally built to integrate with reasoning subnets (like Numinous or Almanac) to create complex, multi-step AI workflows. It turns a room full of shouting voices into a coherent conversation.

3. The External Gateway: Project Rubicon

Affine isn’t just connecting subnets to each other; it is connecting Bittensor to the global economy.

In November 2025, General TAO Ventures announced Project Rubicon, a massive initiative designed to bridge Bittensor subnets to global Web3 markets.

Affine was named as part of the initial cohort for this project, alongside heavyweights like Vanta (Subnet 8).

This isn’t just internal plumbing. This partnership focuses on external integrations, helping Affine export its utility beyond the Bittensor ecosystem. It creates a path for real-world capital and external crypto markets to access the super-brain Affine is assembling.

4. The Agentic “Hands”: Future Integration with Ridges (Subnet 62)

While Chutes provides the raw power, the architecture is fundamentally built for a higher purpose: Agentic Coordination.

This is where Ridges (Subnet 62) fits into the blueprint. Ridges is an agentic coding powerhouse as it writes and executes software.

The vision for this integration creates a complete brain-to-hand workflow:

• The Architect: Affine uses high-level reasoning to break a complex problem into a multi-step plan.

• The Builder: It then hands those blueprints to Ridges, which writes the actual code to execute that plan.

This moves us beyond simple chatbots into complex, multi-step AI workflows. We are looking at a future where one subnet thinks, checks its work against Chutes, and then commands Ridges to build the solution, all automatically.

Why Invisible Infrastructure Is Where Smart Money Goes

Most people invest in products they can touch.

Smart money invests in infrastructure before anyone knows they need it.

The AWS Moment

In 2006, Amazon Web Services launched. Most people didn’t understand why an online bookstore was suddenly renting servers.

The real money wasn’t made by the people who bought things on AWS. It was made by the people who bought Amazon stock while everyone was confused.

By the time AWS became obvious, the asymmetric upside was gone.

The Nvidia Moment

In 2016, Nvidia was known for gaming GPUs. Then they started talking about “AI compute.”

Most investors thought: “Why would AI companies need gaming chips?”

The smart ones thought: “If AI scales, whoever makes the picks and shovels wins.”

By 2023, when everyone understood that AI runs on Nvidia chips, the 100x gain was over.

The Affine Moment

Right now, Affine is in the “What do you mean subnets need coordination?” phase.

Most people are chasing Chutes because they can use it. They can see the chat interface. They can test the models. It’s tangible.

Affine has no consumer demo. It has no viral moment. It’s pure B2B infrastructure.

But the market is telling you something: Affine is firmly in second place for market cap, behind only Chutes.

That’s millions of dollars in TAO staked by people who understand the coordination layer matters more than any individual application.

The dTAO market already decided that Affine is important. The question is whether you’ll recognize it before it becomes obvious to everyone else.

The Bull Case and The Bear Case

Let me give you both sides, because that’s how you evaluate infrastructure bets.

Bull:

• Jacob Steeves doesn’t do vanity projects. When the co-founder leaves the CEO role to build in the trenches, that’s a signal.

• The market has already voted with emissions: #2 spot without a consumer demo means deep conviction from sophisticated players.

• Every subnet solving real-world problems will eventually need high-quality reasoning models and coordination. Affine is building that foundation.

• First-mover advantage in coordination infrastructure is massive. Once subnets integrate with Affine, switching costs are high.

Bear:

• “Invisible infrastructure” can stay invisible indefinitely if consumer adoption never materializes.

• Coordination could happen at the application layer (like Macrocosmos is doing) instead of the protocol layer.

• Affine’s technical complexity might be overkill for what the market actually needs.

• If Bittensor never breaks out beyond crypto-native users, enterprise coordination layers remain theoretical.

The Infrastructure Checklist (What I’m Watching)

If you want to monitor Affine’s progress without getting lost in technical specs, here’s your simple checklist:

Monthly Check-In:

1. Were any new subnet integrations were announced? (Look for press releases, X posts)

2. What’s Affine’s current emission rank? (Taostats updates daily)

3. Which subnets have started using Affine-trained models? (X posts, Discord miner discussions)

Quarterly Deep Dive:

1. Review Affine’s roadmap (future milestones are not published as yet)

2. Check if new subnet categories (trading, coding, research) are citing Affine as necessary infrastructure

3. Monitor whether alternative coordination solutions are emerging (competition validates the thesis)

The Future is Assembled

When I finally sort the piles of LEGO with my kids, we build something cool. But we only get there because someone had written down which pieces connect to which.

Affine is writing that instruction manual for Bittensor.

For a long time, we’ve had the pieces: just scattered, disconnected, painful to step on. But with Affine, for the first time, we’re hearing the click. The pieces are fitting and the messy floor is finally being cleared.

Infrastructure wins by becoming invisible:

You don’t think about TCP/IP when you browse the web.

You don’t think about Amazon Web Services when you stream Netflix.

And eventually, you won’t think about Affine when you use a Bittensor-powered app.

But it’ll be there. Coordinating. Verifying. Assembling the pieces so the rest of us can just solve problems.

That invisibility is the point. And if history has taught us anything, it’s that the boring stuff nobody understands at first is where the asymmetric returns hide.

My ask for you:

Bookmark this piece. Set a calendar reminder for March 2027. Come back and tell me: How many of those dependencies materialized? Which subnets couldn’t survive without Affine? Was this infrastructure bet obvious in hindsight, or did I completely miss the mark?

Because that’s how we learn to spot the next one before everyone else does.

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Until next time.

Cheers,

Brian

The Dow Jones dropped nearly 1,000 points in about 36 minutes. Some stocks traded for pennies. Accenture, a Fortune 500 consulting firm, briefly hit one cent per share. Procter & Gamble fell 37% in minutes. Then it mostly recovered.

The culprit? Not a rogue trader, not a panic sell-off.

Algorithms meeting unusual conditions.

High-frequency trading systems amplified a large sell order into a cascade. Computers followed their rules perfectly. But the rules didn’t account for the moment. By the time humans stepped in, billions had briefly evaporated.

That was TradFi’s wake-up call about automation in thin liquidity.

It took regulators four months to piece together what happened.

Fast Forward to February 2026

On Bittensor, we didn’t need four months.

We didn’t need regulators to subpoena emails and reconstruct order books.

We watched it happen.

In mid-February 2026, Subnet 76 experienced its own Flash Crash, or even a Flash Spike. An algorithmic anomaly drove the price of a single Alpha token to roughly 627,450 TAO before collapsing back to earth hours later.

For human observers, it was a spectacle.

But for users of Mentat Minds’ automated indexes, it was expensive.

Algorithms managing the Mentat 5 and Mentat 15 portfolios saw the spike, calculated the new market cap, and automatically rebalanced into the subnet at peak prices. This meant slippage of up to 97% and drawdowns of nearly 80% for affected allocations.

The Mechanics of a Glitch

Normally, when a new subnet registers on Bittensor, there is a 7-day immunity period. This buffer allows validators to come online, miners to register, and price discovery to happen in something resembling order.

However, a recent update to the subtensor code changed a setting called InitialStartCallDelay to zero.

That meant instant activation.

On February 19, at block 7,574,784, Subnet 76 registered. Forty-four blocks later, it was live.

The problem? There were zero validators online.

So Subnet 76 launches, and here’s what happens:

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When Automation Meets Anomaly

This is where Mentat Minds steps in.

As founder Gustave Laurant puts it, Mentat is built to be the “Amazon for Subnets.” It’s a non-custodial proxy staking tool designed to make life easier for non-coders. You pick a strategy, like the top 5 subnets by market cap, and their code handles the rebalancing.

It’s a fantastic tool for accessibility.

But on Feb 19, the tool worked too well.

When SN76’s market cap artificially spiked to #1, the Mentat 5 and 15 algorithms did exactly what they were coded to do: they bought the winner and into a liquidity vacuum.

Mentat Minds addressed this quickly in their Discord:

“Since the Mentat 5 and Mentat 15 indexes are weighted by market capitalization, this affected the automatic rebalancing process. Some of our users have been affected.”

This wasn’t a custody failure. No one stole funds. The loss came from network-level price action meeting a blind algorithm.

Other indexes, like TrustedStake (which uses manual inclusion and slippage guards), sat on the sidelines and watched.

The Community Response: “Where Were the Brakes?”

While the mechanics explain how it happened, they don’t soothe the frustration in the Mentat Minds Discord.

The inevitable comparison is being drawn: If TrustedStake had manual guardrails, why didn’t the “Amazon of Subnets”? When you market convenience, users expect safety features to be standard.

Mentat has stated that the damage was contained to just 1.5% of users. They are currently analyzing the full extent of the slippage and have promised a detailed update soon.

But the elephant in the room remains: Will there be compensation? Will Mentat make users whole, or will this be treated as a harsh lesson in on-chain execution? We are waiting to find out, and their reputation may depend on how they deal with the fallout.

Why This Matters Now

I’m not writing this to pile on Mentat Minds and I’m certainly not writing it to scare you away from delegation tools.

If you’ve been following this newsletter for a while, you know how much I value the on-ramps that make Bittensor accessible to non-coders.

Your TAO never leaves your wallet. You authorize them to manage allocations and they handle the validator selection and optimization. But Subnet 76 revealed something important about the path from passive delegation to active literacy.

Tools don’t replace awareness.

In 2010, traders relying on algorithmic execution without understanding the conditions got crushed.

On Bittensor in 2026, the stakes are visible, but the lesson is the same.

Your Mentat Risk Ladder Framework

So, how do you participate without getting wrecked?

You need to move from Passive Trust to Active Supervision.

I’ve built a simple Risk Ladder to help you decide where to park your TAO in Mentat based on how much attention you want to pay.

The New Subnet Checklist

If you are manually trading or using an aggressive index, you should run this checklist before any rebalance or buy order:

1. Age Check: If the subnet is under 48 hours old, don’t touch it.

2. Validator Count: If there are no active validators online, stay away.

3. Liquidity Depth: If the pool is thin, hold back to avoid high slippage.

4. Root Claims: If emissions haven’t started flowing back to the pool, wait.

Here’s what I actually do:

I use Crucible Wallet’s Smart Allocator for my core exposure. It’s comparable to Mentat’s Protected Alpha product in that it protects principal while still participating in subnet yields of the top subnets. No unexpected exposure to anomalies like SN76 because the principal is shielded from the volatility of individual subnets.

I also have a small amount of principal invested in high-conviction subnets I have thoroughly researched. The rest I keep in root—stable, predictable, exactly what I need for my base position.

The Safety Assignment

Instead of going all-in on one index, consider a split approach:

85% Root: Steady APY without surprise exposure, or invest yields in alpha using Mentat Protected Alpha or Crucible Wallet’s Smart Allocator while protecting your TAO.

10% Index: Automated discovery, small experiments with clear caps.

5% Manual: Active conviction on subnets you’ve researched.

Rebalance periodically and monitor liquidity. Start with the conservative index, scale into experimental ones as conviction grows.

The Small-Test Rule: Before committing significant capital to any index, run a small allocation through 1-2 rebalance cycles and watch how it behaves.

This Is Transparency In Action

I’ve said it before, and I’ll keep saying it because it matters:

Decentralization is messy.

In 2010, traders had to wait months to understand why their portfolios were hammered. The opacity was part of the damage.

On Bittensor, you could see the trades on Taostats. You could read Doug Sillars’ breakdown of the mechanics. You could check Mentat’s Discord announcement. You could trace the blocks yourself.

Everything is on chain.

That doesn’t minimize the sting of losses. If you are reading this and you got hit, I’m sorry. Use it as tuition.

But this does give us something the 2010 traders never had: complete visibility into what went wrong and why. Now we know and we can adjust.

What Comes Next

There’s an open-dev call likely revisiting the 7-day activation buffer. Index providers will probably add liquidity filters to initial rebalancing. Post-halving dynamics should favor utility-driven subnets over price anomalies.

The protocol will evolve. The tools will improve.

But the fundamental lesson remains:

The best tools make things easier. The best users make themselves harder to catch off guard.

As we move through the post-halving landscape, expect more edge cases, more learning moments, and if you stay thoughtful, more conviction in what you’re building.

Hit reply and tell me: where does this leave you?

Still comfortably in Root? Dipping your toes into Mentat 15 with small allocations? Going full manual with the Risk Ladder in hand?

I want to know where you’re standing after this.

Share

Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

If you’ve ever had a newborn, you know the specific kind of brain fog that sets in around week three. You are operating on two hours of broken sleep, caffeine, and pure survival instinct.

In this zombie state, my friend drove to our local grocery store. He wandered the aisles in a daze, filled his cart with diapers, milk, and frozen dinners, and walked out to his car. He loaded the bags, drove home, and started unpacking.

Halfway through putting the milk in the fridge, there was a knock at the door.

It was the police.

In his fog, he had completely bypassed the checkout. He hadn’t paid. He wasn’t trying to steal; his brain just skipped a step. But to the store’s security system, there was no difference between a tired dad and a shoplifter.

Here, the only difference that matters in the eyes of the law is a slip of paper about four inches long.

The Receipt.

We usually think of receipts as trash. But that piece of paper is a “Proof of State.” It proves a transaction happened exactly as claimed. Without it, my friend was technically a criminal. With it, he would have been a customer.

Right now, the $2 trillion AI industry is operating like my sleep-deprived friend.

When you ask an AI model to diagnose an X-ray, or when you ask a trading bot to manage your crypto portfolio, the process happens inside a black box. You put an input in and you get an output out.

But just like my friend walking out of the store, we have no proof of what happened in the middle.

• How do you know the AI used the specific medical model it claimed to use?

• How do you know the trading bot actually executed the strategy you paid for?

• How do you know your private data wasn’t siphoned off?

Currently, AI runs on a philosophy of blind faith.

That works fine for writing poems. It does not work when we start building the future of finance and healthcare.

Today, we are looking at DSperse (formerly Omron), Subnet 2 on Bittensor. They are building the receipt printer for the AI age.

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The Invisible Problem

To understand why DSperse is critical, we have to look at where AI is going.

We are transitioning from the Chatbot Era to the Agentic Era.

In the Chatbot Era, the stakes were low. If ChatGPT hallucinated a fact about the Roman Empire, it was annoying, but nobody got hurt.

But then came the rise of AI Agents.

An AI Agent is fundamentally different from a chatbot. It is autonomous software designed not just to talk, but to do. It breaks out of the text box to integrate with your environment; executing shell commands, controlling browsers and managing files without human intervention.

Agents represent the shift to active AI.

They don’t just suggest an action; they take control of your computer.

They can move a mouse, navigate to a decentralized exchange, and execute a trade.

This shifts the paradigm completely.

• Chatbot Era: “Write me an email about buying Apple stock.” (Low Risk)

• Agentic Era: “Here are my API keys. Go buy $5,000 of Apple stock.” (High Risk)

If I am going to let a decentralized AI agent manage my savings, I cannot rely on blind faith. I need proof. Currently, the industry offers two imperfect options:

1. The “Black Box” Model (Blind Faith)
We trust Anthropic or Google because they are massive corporations. But we have no actual proof of what is happening inside the box. We just hope they aren’t manipulating the results.

2. The “Glass House” Model (Total Exposure)
The alternative is Open Source. To prove a model is honest, the developer shows their code.
The Problem for a business here is, that code is their “secret sauce.” If a hedge fund reveals their proprietary model just to prove it works, they lose their competitive advantage.

This is the deadlock.

Businesses can’t afford to give away their secrets (Option 2). Yet, users won’t accept blind faith (Option 1) when their bank accounts are on the line.

We need a third option. We need a way to verify the result without exposing the secret.

The Solution (The “Secret Recipe”)

The solution is a technology called zkML (Zero-Knowledge Machine Learning).

Let’s bring this back to my neighborhood.

There is a local bakery that makes incredible malt loaf. The owner claims it uses three specific organic grains and a signature white sourdough starter. That’s why he charges $12 a loaf.

How do I verify he is telling the truth? For all I know, he uses generic supermarket flour.

The “Black Box” Solution:
I just buy the bread and hope I’m not getting ripped off. (This is how we currently treat AI).

The “Glass House” Solution:
I stand in his kitchen and watch him bake. The problem is if I watch him, I’ll learn his recipe and open a competing bakery next door.

The “DSperse” Solution:
Imagine the baker baked his bread inside a transparent, locked glass box containing a Digital Inspector who watches every step.

This inspector scans the flour barcodes and weighs the starter. When the bread pops out, the box prints a Certificate. The Certificate says: “I certify the secret steps were followed (99.9% confidence)”

The certificate proves the process without revealing the recipe:

• I (the customer) get verification that I wasn’t scammed.

• The Baker (the developer) keeps his secret recipe safe.

This is what DSperse does for AI. It acts as that Digital Inspector, generating a cryptographic receipt that proves a computation happened correctly without revealing the proprietary code.

The Bittensor Angle

If this technology is so revolutionary, why doesn’t ChatGPT come with a receipt?

Because generating these receipts is incredibly heavy.

Creating a Zero-Knowledge Proof is computationally expensive. Historically, proving a simple AI inference took 1,000x to 10,000x more computing power than just doing the inference itself.

If an AI takes 1 second to give you an answer, it might take 1,000 seconds to generate the receipt. That is too slow for the real world.

This is where the magic of Bittensor comes in.

The team behind Subnet 2, Inference Labs, realized a single centralized server would never be fast enough. So, they built DSperse to act as a distributed supercomputer.

Instead of asking one computer to lift a 1,000-pound rock, DSperse breaks the rock into a thousand 1-pound pebbles. It slices the proof generation into tiny segments and distributes them across the Bittensor network.

Hundreds of miners compete to verify these segments. Because there is money (TAO) on the line, miners have started a hardware arms race, building custom chips specifically designed to generate these proofs faster than anyone else on earth.

Subnet 2 has already generated over 300 million proofs as of February 2026, doing at scale what academic researchers said was too impractical just a few years ago.

Real-World Stories

Technology is only interesting if it solves human problems.

The Trustless Hedge Fund (Vanta SN8)

If I ask you to invest in my AI trading bot, and I show you a spreadsheet of 300% APY returns, how do you know I didn’t just type those numbers into Excel?

In the traditional world, you hire an auditor like Deloitte to come in, check the books, and stamp a document. That takes six months and costs a fortune.

By integrating DSperse’s proof generation, Vanta (SN8) creates a mathematical “audit trail” for every trade the AI makes.

It proves the trade was executed by the specific model you subscribed to and it proves the returns are real without Vanta having to reveal their proprietary trading strategy to the world. This is the infrastructure Vanta is building right now to create the first truly verifiable hedge fund.

Verifiable Vision (Score SN44)

In early February 2026, Score (SN44) and DSperse (SN2) announced a major partnership to solve a critical problem in computer vision.

Score specializes in analyzing video feeds in real-time. Think of security cameras at airports, sensors on oil rigs, or tactical cameras in football stadiums (like their work with Reading FC).

But in high-stakes industries like aviation, you can’t just trust an AI when it says “The runway is clear.” If the AI is wrong, people die. Regulators like the FAA and NAV Canada require an audit trail. They need proof.

DSperse proves that the AI actually analyzed the specific video frame it claimed to, and the output (e.g., “Runway Clear”) was generated by the certified model, not a glitch.

The Receipt Economy

The vision for Subnet 2 is that DSperse becomes the “SSL Lock” of the AI internet.

Ask anyone over 30 about the early web and they’ll tell you they were terrified to put their credit card into a website until they saw HTTPS and that little green lock icon. That icon meant: This connection is secure.

DSperse wants to put that lock icon on AI.

In a world where AI agents will negotiate your mortgage, manage your retirement, and draft your contracts, the question is: “Who owns the truth?”

Right now, OpenAI and Google own the truth. They say their model did X, and you’re expected to believe them because they’re big.

Bittensor (and specifically DSperse) is building something different: a world where the truth is owned by no one, but verifiable by everyone.

In the future, when you’re browsing a marketplace of AI agents—looking for a lawyer, a financial advisor, or a co-pilot for your business—you won’t look for the biggest brand name. You’ll look for the “Verified by Bittensor” checkmark.

Which brings me to a question for you:

If you had that checkmark today, an agent with absolute mathematical certainty, what is the first high-stakes task you would hand off to it?

Would you let it file your taxes? Rebalance your crypto portfolio? Diagnose a medical scan? Reply and let me know.

Because in the end, we don’t need more powerful AI. We need accountable AI.

And accountability starts with a simple piece of paper.

The receipt.

Share

Until next time.

Cheers,

Brian

Disclaimer: This is not financial advice. I am a writer documenting the Bittensor ecosystem. Always do your own research.

My two-year-old just learned what gravity means.

He was standing on the couch, confident, giggling, acting like he’s Spider-Man as usual.

Then took one step too far. I caught him, obviously. But the look on his face was pure betrayal: Wait, you mean the ground isn’t always there?

That’s exactly how February 2025 felt for Bittensor holders.

dTAO launched, and suddenly the invisible floor we’d been standing on: Root staking, predictable yields, passive income, had vanished.

The pitch was simple: stop letting a handful of validators play God with emissions. Let the market decide instead. Turn every TAO holder into a venture capitalist.

And I fell for it.

Not the pitch. The promise.

I had the research and understood the mechanics.

I picked three subnets with real utility, spread a large share of my TAO across their alpha tokens and waited for the genius of decentralized markets to reward me.

Down only.

Not catastrophic. But watching your stack shrink while everyone screams about meritocracy hits different.

That’s when I realized this isn’t passive income anymore: this is the 2017 shitcoin era all over again, except everything’s priced in TAO instead of Bitcoin.

So when I tell you dTAO worked, I’m not talking about my personal portfolio.

I’m talking about whether the mechanism actually did what it promised: let markets decide which subnets deserve to exist.

The Promise vs. The Setup

Before February 2025, Bittensor had a centralization problem.

Root validators decided emissions and politics ruled.

dTAO was the solution: turn every TAO holder into a validator.

Each subnet issues its own alpha token. You stake TAO to support it and more emissions flow to that subnet.

The promise was simple: utility would win. The best subnets would rise to the top.

How it works: TAO is the reserve currency. Alpha tokens are subnet equity. Utility drives alpha price up. Failure drives it down—and eventually, deregistration.

Now let me show you what happened in practice.

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The Reality: Three Stories

Winner: Chutes (SN64)

Chutes is a serverless AI inference powerhouse.

Pre-dTAO launch, it delivered practical compute that beat centralized alternatives on cost and latency.

Post dTAO it rapidly attracted massive staking inflows, securing top spot with 9.3% of network emissions and a market cap of $64M in February 2026.

The narrative: Utility + market signal = explosive growth.

dTAO rewarded real-world demand directly. No committee meetings or politics, just developers voting with their wallets because the service actually worked.

Loser: Tiger Alpha (SN107)

Tiger Alpha registered in June 2025 with an initial 200 TAO stake. It promised real-time, verifiable crypto market data via decentralized AI agents.

It failed to build traction. By December 2025, it pivoted branding to Knowledge Delivery Network (KDN-1).

Daily emissions peaked at 18 TAO, but no meaningful milestones materialized. It was deregistered in early February 2026 with 900 TAO returned to alpha holders.

The narrative: Lack of product-market fit + liquidity starvation = swift cull.

The market didn’t wait. No product or users means you’re out.

Survivor: Gradients (SN56)

“Anyone Can Train AI.”

Gradients democratizes AI training on Bittensor. It removes technical barriers, allowing you to simply upload data and select models while network miners compete to handle the training.

Registered pre-dTAO (November 2024), it survived the gauntlet: Taoflow, pruning, the cap and the halving. Steady performance at 2.5–3% emissions with consistent utility.

The narrative: Quiet utility + reliable staking inflows = resilience.

No fireworks. Just stayed in the game while the exit door got crowded.

Root Yield Collapsed, Forcing Alpha Migration

The original dTAO design promised something: you could stake TAO to Root (Subnet 0) and earn “risk-free” yields while the market sorted out which subnets deserved capital.

That theory lasted until the math caught up.

Post‑dTAO, TAO staked on Root is only counted at 18% of its nominal value in validator weight, while Alpha stakes are fully weighted.

As Alpha supply and subnet demand grow, this design shifts rewards away from Root and has already pushed Root APYs down from their pre‑dTAO highs into lower territory over the following months.

By late 2025, Root staking on average was barely beating inflation.

Investors had a choice: accept near-zero yields or migrate into higher-risk Alpha tokens.

The mechanism has forced capital allocation. If you want returns in a dTAO world, you have to pick winners. There’s no neutral ground anymore.

The side effect is retail TAO holders became involuntary venture capitalists, forced to research subnet fundamentals or watch their stack erode in real terms.

Note: This is where Subnet Edge helps—I use their independent market intelligence to stay current on subnet fundamentals without spending hours digging through data myself. Check out their publication here and read on for details on how to interpret the data so you can invest in subnets with confidence.

What It Means for TAO Holders

You’re a capital allocator now. Here’s how:

Track net TAO flows, not just price. The direction of capital tells you more than the alpha/TAO ratio.

Understand liquidity. Thin pools means volatile exits. Check depth before staking.

Unstaking hurts the subnet. Taoflow is bidirectional.

Your stake is a vote. Treat it seriously.

The dTAO Liquidity Checklist

Before staking TAO into any subnet, ask:

1. What’s the TAO pool size?

2. What’s the daily trading volume?

3. What’s the 30-day net flow direction?

Step-by-Step Screenshot Guide: dTAO Liquidity Checklist for Gradients (SN56)

1. What’s the TAO Pool Size?

What it means: The amount of TAO available in the pool for trading the subnet’s token (Alpha).

Why it matters: High liquidity means you can buy or sell larger amounts with less price impact (low slippage). Low liquidity means even small trades can move the price a lot.

Step 1: Go to taostats.io/subnets/56/statistics

Step 2: Look for a section or card labeled “TAO in Pool”

This is usually near the top, in the main statistics area.

Interpretation:

This means there are about 60,780 TAO currently locked in the Gradients pool.

A higher TAO pool size generally means more liquidity, making it easier to buy/sell or stake without large price swings.

2. What’s the Daily Trading Volume?

What it means: The total TAO traded in the last 24 hours for this subnet.

Why it matters: High volume means lots of trading, so it’s easy to enter or exit positions and prices are more likely to reflect real demand. Low volume can mean it’s hard to find a buyer or seller and prices may be more volatile.

Step 1: On the same statistics page, look for “Volume / 24H” or similar.

Interpretation:

About $2.08mm of Gradients alpha was traded in the last 24 hours.

Higher daily volume means more active trading, which usually translates to better liquidity and easier entry/exit.

3. What’s the 30-Day Net Flow Direction?

What it means: Whether TAO is flowing into or out of the pool over the past month.

Why it matters: Positive flow means confidence is building. Negative flow means capital is fleeing. Under Taoflow, negative flow can zero out emissions entirely.

Step 1: Scroll down to the charts or history section of the subnet statistics page.

Step 2: Look for a chart labeled “TAO Injected vs TAO In Pool”, or similar.

Interpretation:

If the TAO in Pool is increasing over 30 days, more TAO is entering the pool (positive net flow).

If it’s decreasing, more TAO is leaving (negative net flow).

For Gradients, the TAO in Pool has been relatively stable with minor fluctuations, suggesting a balanced or slightly positive net flow recently.

How They Interplay

Imagine you’re at a currency exchange booth:

Liquidity is how much cash they have on hand.

Volume is how many people are exchanging money today.

Flow is whether their cash reserves are growing or shrinking.

A good booth (subnet) has plenty of cash, lots of customers and growing reserves. This makes it safe and easy to use.

Think of these three like the features of a busy marketplace:

A. High Liquidity + High Volume + Positive Flow

Like a major city market: lots of goods (liquidity), lots of buyers/sellers (volume), and growing trust (positive flow).

Result: Stable prices, easy trading and strong community confidence.

B. High Liquidity + Low Volume

Like a well-stocked shop with few customers.

Result: You can trade large amounts without moving the price, but it may take time to find someone to trade with.

C. Low Liquidity + High Volume

Like a small shop with a rush of buyers.

Result: Prices can swing wildly (high slippage) and trades may be harder to execute at expected prices.

D. Low Liquidity + Low Volume + Negative Flow

Like a quiet, small-town store with little stock and customers leaving.

Result: Hard to trade, prices unpredictable, and higher risk of getting zeroed out under Taoflow.

In Practice (for Gradients)

If Gradients has high TAO in Pool, high 24h volume, and positive flow, it’s a sign of a healthy, active and trusted subnet.

If one metric lags (e.g., high liquidity but low volume), consider how that might affect your ability to trade or stake efficiently.

Check the main Subnets page in taostats if you’re unsure how a subnet compares against its peers on these metrics:

The dTAO verdict

So did it work?

Yes. And that’s exactly the problem.

dTAO works. Markets are deciding. Capital is flowing to utility. The winners are winning. The losers are losing. Darwinian capitalism is doing its thing.

But if history has taught us anything about markets, they're never truly free.

As we settle into this new era, a controversial reality is setting in: First-Mover Advantage.

Early subnets keep better economics forever because their Alpha tokens started halving earlier. Every TAO halving creates permanent tiers. A 2024 subnet has better economics than an identical 2026 launch.

The bull case is straightforward: early risk deserves permanent reward.

The bear case is equally clear: this creates an entrenched oligarchy where innovation gets punished for showing up late.

The ecosystem hasn’t decided yet whether this is a feature or a bug.

But here’s what dTAO has proven: markets will force that decision faster than committees ever could. Either the mechanism adjusts to create fairer competition, or the next wave of builders goes elsewhere.

The experiment worked. Now comes the hard part: surviving it.

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Until next time.

Cheers,

Brian

You’ve convinced your friend to ditch Claude for the combined weight of Open-Source AI models using Subnet 64 (Chutes) at a fraction of the cost.

Next day: “I’m in. Where do I start?”

You send them to Discord.

30 minutes later: “What the f*ck is a hotkey weight validator epoch? I can’t understand a word of this.”

They’re gone.

You nailed the pitch but couldn’t bridge the vocabulary gap.

Last week, I (kind of) fixed my Bittensor pitch. This week, the problem evolves: They’re interested. They ask follow-up questions. And suddenly you’re drowning in jargon.

“What’s a validator weight?” “How do emissions actually work?” “Wait, what’s the difference between TAO and alpha?”

This is the language you’ll actually encounter in Discord, governance proposals and subnet performance debates.

Here’s what I learned: you can’t explain Yuma Consensus the same way to a validator as you do to your friend at the bar.

Most Bittensor resources only give you the technical definition. That’s perfect for Discord power users, but useless when your curious colleague asks “wait, what’s a validator again?” at lunch.

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The Three Vocabulary Barriers That Kill Adoption

After I went down in flames trying to pitch Bittensor in a bar, I realized there are three distinct ways vocabulary kills conversations. Understanding them is the first step to bridging the gap.

Barrier #1: The Jargon Wall

This is when technical terms stop conversations cold.

My friend asked “how does it work?” and I said “validators judge miners” and watched his brain shut down. He heard “blockchain people doing blockchain things.”

Here’s what validators actually are: The Quality Inspector.

Think mystery shopper meets health inspector. They walk through each store (subnet), test the products (miner outputs), and file official reports. Their job is to make sure no one’s selling garbage and ruining the mall’s reputation.

That’s all “validator” means.

The moment I translated it from jargon to job description, it clicked.

The lesson: If you can’t explain it without jargon, you don’t understand it yet.

Barrier #2: The Metaphor Mismatch

This is when we use technical metaphors that mean nothing to civilians.

“Subnet” sounds like IT department speak. Crypto “Miner” makes people think of Bitcoin energy FUD and warehouse farms in Iceland.

So instead, I’ve committed to using the mall metaphor consistently:

Bittensor is The Mall of Intelligence. Think of a whole shopping district, not just one store, but the entire city center where anyone can set up shop. Want to build compute? Open a store. Storage? Another store. AI predictions? Yet another.

Subnets are The Specialty Stores. Subnet 85 is the “Video” store. Subnet 62 is the “Coding Agents” store. Subnet 8 is the “Trading” store. Each one is a mini-market competing to solve one specific problem better than anyone else.

My kids have a Lego store, a dinosaur store, and a toy car store at home. They’re all competing for Dad’s attention (emissions). That’s basically subnets.

Miners are The Store Workers. These are the workers behind the counter. Whether coding, generating images or scraping data, they’re doing the actual heavy lifting. If the workers fail, the product fails.

Once I committed to this metaphor and stuck with it, everything else fell into place. People can visualize the system instead of trying to decode it.

The lesson: The right metaphor makes complexity collapse into common sense.

Barrier #3: The Crypto Tax

This is when blockchain-native concepts make people think “this is a crypto thing, not an AI thing.”

The moment I said “TAO emissions,” my friend’s eyes said “here we go, another ponzi.”

But emissions aren’t a scam, they’re just the scoreboard.

TAO is The Mall Currency. It’s the only money accepted in this shopping district. You need it to open a shop (register a subnet), rent a unit (stake), or pay your workers (miners). Customers spend it, stores earn it, and landlords (stakers) collect it. The busier the mall, the more valuable each dollar becomes.

Emissions are Payday, Every Day. The network mints exactly 3,600 TAO every 24 hours. That’s the pot. Every subnet, miner, and validator is fighting to claim their share before it runs out.

When I stopped using crypto language and started using economic language people already understand: currency, payday, competition for rewards, the stigma evaporated.

The lesson: Decouple blockchain mechanics from blockchain stigma.

Why This Matters In The Real World

Bittensor’s growth depends on reaching beyond Discord power users.

Every subnet needs users. Every validator needs stakers. Every builder needs collaborators.

And all of that dies in the vocabulary gap.

Right now, someone is trying to explain Bittensor to their colleague at lunch. Someone else is pitching a subnet idea to a potential miner. Someone is attempting to convince their spouse why they should move their TAO off an exchange.

Most of those conversations are failing because we’re drowning people in terminology before we’ve given them a mental model.

The good news? You only need 5-7 terms to have 80% of the conversations.

You don’t need to memorize the encyclopedia. You need a survival kit.

The 5-Term Survival Kit

These are the terms that unlock understanding. Master these, and you can follow Discord conversations, explain Bittensor to a friend, and understand what people are actually arguing about in governance debates.

1. Bittensor 🔴

The Mall of Intelligence. A whole shopping district where anyone can set up shop. Want to build compute? Open a store. Storage? Another store. AI predictions? Yet another. It’s Bitcoin for brains: a global marketplace where intelligence is the commodity and no single landlord controls the whole operation.

2. TAO 🔴

The Mall Currency. It’s the only money accepted in this shopping district. You need it to open a shop, rent a unit, or pay your workers. The busier the mall, the more valuable each dollar becomes.

3. Subnet 🔴

The Specialty Store. Each subnet is a mini-market competing to solve one specific problem better than anyone else. Subnet 75 does storage, Subnet 34 detects deepfakes, Subnet 8 handles trading signals.

4. Miner 🔴

The Store Workers. These are the workers behind the counter doing the actual heavy lifting. Whether coding, generating images, or scraping data, if the workers fail, the product fails.

5. Validator 🔴

The Quality Inspector. They walk through each store, test the products, and file official reports. Their job is to make sure no one’s selling garbage and ruining the mall’s reputation.

Bonus terms if you’re going deeper:

6. Emissions

Payday, Every Day. The network mints 3,600 TAO every 24 hours. Every subnet, miner, and validator is fighting to claim their share based on the quality of work they’re producing.

7. Staking

The High-Yield Savings Account. You park your TAO with a validator to say “I trust this guy.” In exchange for your trust, you get a cut of their paycheck.

The Complete Arsenal

The 5 terms above will get you through most conversations. But if you’re ready to go deeper I’ve created something for you:

Bittensor Terms Decoded has 20 terms (so far) organized by skill level:

• 🔴 Survival Kit (5 terms) - Don’t sound like an idiot

• 🟡 Staking Decision Tier (13 terms) - Make informed choices

• 🟢 Builder/Operator Tier (20 terms) - Debate governance, evaluate subnets

Bookmark it. You’ll use it every week. And I’ll keep adding to this over time to be a more expansive resource.

11pm: When Your Wife Finally Asks

On Sunday I retired late from my home office after researching Bittensor for a bit too long. Arriving in the living room, to my surprise my wife asked the question I’d been subconsciously avoiding for a while:

“So what exactly are you spending all that time on with this Bittensor AI newsletter? What is it actually?”

This was my shot at redemption.

“Think of it like this: Bittensor is a giant shopping mall for AI services. You’ve got stores competing to give you the best product. The currency is TAO. And the whole thing runs on quality inspectors making sure nobody’s selling garbage.”

She paused the TV.

“So basically no one company owns it? That’s the whole point?”

“Exactly.”

“And the mall thing means anyone can open a store?”

“Right. No permission needed. You just need to prove your store is worth keeping around.”

She nodded. Actually nodded.

“Okay, that makes way more sense than all that crypto AI stuff you were rambling about before.”

The vocabulary gap closed. Not because I memorized 20 definitions, but because I had the right 5 in the right language. That’s what this decoder is for.

Your Mission This Week

Pick one person in your life who’s curious about AI but doesn’t know about Bittensor.

Pitch them the concept (guide in my last piece) and use the mall metaphor to explain these 5 terms. See what happens.

Then come back and tell me: What worked? What broke? What question did they ask that you couldn’t answer?

Because the only way we fix the vocabulary gap is by testing these explanations in the wild and learning what actually lands.

Where Do You Go From Here?

If you’re new: Start using these 5 terms this week. Join a Discord channel, follow a governance discussion, listen to a podcast. See how much more you understand.

If you’re ready to go deeper: Bittensor Terms Decoded has the full 20-term reference organized by skill tier. Bookmark it. It’s designed for quick lookups when someone drops “Yuma Consensus” in chat and you need the definition now.

If you just want to lurk: That’s fine too. You now know enough to follow along. The decoder will be there when you’re ready.

Subscribe now

Until next time.

Cheers,

Brian

He’s sharp. Runs his own business. Reads the news.

He had no idea what Bittensor was, so I decided to try my pitch on him that has been refined in the last year.

I won’t run through it all, but it started with “It’s like... a decentralized AI marketplace.”

His face did that thing, the polite nod that means “I have absolutely no idea what you just said.”

I tried to recover.

“Okay, so there are these things called subnets, right? They’re like... specialized AI services that compete for rewards...”

“Rewards in what?”

“TAO. The token. It’s how the network distributes value based on…”

“Hold on a second. What’s a subnet again?”

I pivoted. “Think of it like... validators judge the quality of miners’ work, and then emissions get distributed based on consensus and…”

His pint stopped halfway to his mouth. “Validators? Emissions? Are we talking about AI or pollution control?”

Hurting inside, I tried to pursue it but I knew I’d already lost the battle.

I watched him check his phone. The conversation was over.

We talked about football for the rest of the night.

Later, driving home past closed shops and empty car parks, I replayed the conversation in my head. The pitch I’d practiced a hundred times in the shower, the one that sounded so clear in my own mind had completely fallen apart in reality.

I knew I had a few weeks to fix this before I saw him again.

So I committed to an experiment: test five different Bittensor pitches on five different people, document exactly what breaks, and see if anything actually works.

Here’s what actually happened.

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Test #1: My Stock Picking Friend (The Investor)

The Angle: The Mispriced Opportunity

He’s a friend from college who works in finance. The type who bought Tesla in 2020 because he saw asymmetric upside, not ideology.

Over drinks, he mentioned AI investments felt “priced to perfection or obvious trash.”

“Everyone’s piling into AI infrastructure,” I said. “But every dollar reinforces the same three-company oligopoly. What’s mispriced is the alternative.”

He leaned in. “Go on.”

“Everyone assumes decentralized AI can’t work because the economics don’t close. Bittensor TAO fixes that, it’s economic incentives for open AI infrastructure. Run models, validate outputs, then earn TAO. Bitcoin did this for decentralized ledgers. TAO does it for AI.”

My pitch: “TAO is a bet that monopoly AI eventually pisses off enough people that alternatives become valuable. The market’s pricing it like that never happens.”

What happened:

“Show me traction.”

I pulled up Chutes. “Run multiple open-source models side by side. Three dollars a month instead of $100+ for commercial APIs.”

“What’s the bear case?”

“OpenAI drops prices to zero. Governments kill crypto. Decentralized systems never match performance and users don’t care about privacy enough.”

“But if there’s any shift toward decentralized alternatives: regulation, privacy backlash, cost pressure— TAO captures it. It’s the only protocol level economic layer for open AI.”

He nodded. “So it’s a call option on decentralized AI mattering.”

“Yes. And priced like it probably won’t.”

What I learned: Smart investors need the thesis (what’s mispriced), the mechanism (how value accrues), current proof (it works now), and the bear case (what breaks it). Skip the philosophy.

Test #2: My Uncle (The Pragmatist)

The Angle: Bittensor The Machine Economy

He’s a small business owner. Practical. No time for theory. Just wants to know what this thing does.

“Remember calling a taxi after a night out? You’d wait 45 minutes, maybe they’d show up, maybe they wouldn’t. And you paid whatever they charged because they owned the monopoly.

Then Uber happened. Five drivers competing for your fare. You could see their ratings, pick the best one. The marketplace solved the problem.

That’s where we are with AI right now. Except instead of rides, it’s everything.”

The pitch: “It’s like Uber for AI. Instead of one company deciding what model you get, providers compete. The best service wins. You pay less, get more choice and keep your data private.”

What happened:

He got it. Immediately. “So it’s competition instead of monopoly?”

Then he asked: “Can I try it?”

I sent him to Bitmind to test deepfake detection. He used it that afternoon and sent me a screenshot. Win.

What I learned: Pragmatists don’t need philosophy. They need a clear comparison to something they already understand, then a link to try it immediately.

Test #3: My Developer Friend (The Builder)

The Angle: TAO The Economic Primitive

This is where my bar pitch had fallen apart. I’d tried explaining tokens and validators to my school friend, a business owner, not a developer. Wrong audience, wrong approach.

But this friend codes for a living. He knows blockchain basics. I could go slightly technical.

We were grabbing lunch when he asked what I’d been working on lately.

“There’s this decentralized AI network called Bittensor,” I started. “You could run a miner and earn rewards for providing compute.”

His face lit up. “I’d like to see that repo. How does it work?”

Perfect.

The pitch: I started explaining the economic model. TAO flows, validators, consensus mechanisms...

What happened:

His eyes glazed over. I watched him mentally check out.

“You know what, just send me the setup docs,” he said, “I’ll look at it later.”

He didn’t look at it later.

What I learned: Technical people want to see code before they believe you. I should have sent documentation links first and let him read the README. Then, maybe, answered questions about economics if he asked.

Test #4: My Wife (The Skeptic/Casual User)

The Angle: Corporate AI Capture

Last week, my wife used ChatGPT to draft a sensitive work email about a colleague’s performance review. When I mentioned OpenAI was storing that conversation, training their models on it, she looked at me like I’d grown a second head.

“But it’s just helping me write an email.”

Right. Just like Facebook was just helping you stay in touch with friends.

Right now, three companies (OpenAI, Google, Anthropic) control the most powerful AI models in existence. They decide what questions you can ask, what answers you’ll get and what happens to your data.

Every conversation with ChatGPT trains their next model. Every image you generate becomes part of their dataset. The terms of service you didn’t read give them that right.

And when they decide something is “unsafe” or “against policy”—maybe mentioning a competitor, maybe criticizing their approach—you have zero recourse. No appeal. No alternative.

Imagine if three companies owned all the world’s electricity. They’d decide who gets power, when, and at what price. They’d monitor your usage and sell it to your insurance company.

That’s where we are with AI. Except it’s not powering your lights: it’s screening your job applications, diagnosing your medical scans and writing your code.

My pitch: “You know how you don’t trust Instagram with your data? This is that exact problem, but for AI. And the stakes are about ten times higher.”

What happened:

Believe me, this isn’t the first time I’ve tried to talk about Bittensor with my wife.

When I introduced the privacy element, she got worried about using AI at all and asked if she should delete ChatGPT from her phone.

What I learned: I gave her a problem she couldn’t solve. I should have offered her a solution first, “Try this privacy-focused AI tool I use”, then justified the need for it. Otherwise I’m just a doomsayer with no answers.

At least I get the chance try it on her again soon with a different angle.

Test #5: The School Teacher (The Idealist)

The Angle: Open Intelligence

I have a friend who left corporate to become a teacher. He’s the type who tries new apps enthusiastically, then gets disillusioned when they turn exploitative.

At a football game this month: “ChatGPT was amazing for lesson planning, then I read the terms. Every sensitive email about students, every curriculum draft, they’re mining it. And now I hear they’re bringing in ads too.”

Perfect opening.

“So you need AI, but can’t trust the only option that works.”

“Exactly.”

“Let me ask you this: in your classroom, do you let one student answer every question?”

He smiled. “Not at all. They’d get lazy.”

“That’s it, you make them compete. Three hands go up and the best answer wins. That’s the difference between ChatGPT and what I’m about to show you. When you’re in their app, ChatGPT is one company with no competition. What if dozens of AI services had to compete to give you the best lesson plans?”

“That sounds too good to be true.”

My pitch: “There’s a network where AI providers compete for your business. Better service, lower prices, they can’t sell your data. It’s like Uber when it first started, except no one company can ruin it later.”

What happened:

I showed him Chutes. “Three bucks a month, not $20.”

“What’s the catch?”

“Slightly less convenient. But it won’t double in price next year and can’t read your lesson plans to train their next model.”

“So it won’t turn into another Uber.”

“It can’t.”

He handed me his phone. “Sign me up.”

What I learned: He already knows the pattern: good thing gets ruined by monopoly. Position it as “Uber before Uber got greedy, except the greed is impossible.” Then show them it works.

The Pattern I Missed (And You Probably Are Too)

Here’s what I should have seen from the start. Every single failure happened because I used my entry point, not theirs.

Bittensor is building 100+ parallel economies, each with different incentives and users. The mistake most advocates make is having one pitch for a system that deliberately fragments into niches.

Will this work every time?

No. Some people are simply not ready for this conversation and that’s fine.

Bittensor’s adoption will be a grind, not a viral moment. But if you diagnose first and pitch second, you’ll waste less breath and build more believers.

Your Diagnostic Cheat Sheet

Before you pitch Bittensor to anyone, spend 30 seconds asking yourself:

What do they care about most?

• Money/Returns: Use the Investor angle (mispriced opportunity, TAO as a bet)

• Simplicity/Utility: Use the Pragmatist angle (Uber for AI, competition vs. monopoly)

• Control/Privacy: Use the Skeptic angle (corporate capture, data ownership)

• Building/Creating: Skip the pitch, send docs and a working example

• Values/Fairness: Use the Idealist angle (platforms that can’t rug you)

What’s their technical level?

• Non-technical: Analogies only, zero jargon, show them a working product

• Technical: Link first, explain later (if ever)

What’s their crypto comfort?

• Crypto native: You can mention TAO, emissions, subnets

• Crypto skeptical: Hide the blockchain entirely, focus on the outcome

Match the angle to the person. Not the other way around.

Your Turn

I want to know: which of these five pitches would work on someone in your life?

Pick one person. Diagnose them using the cheat sheet above. Try the corresponding angle this week.

Then hit reply and tell me what happened. What questions stumped you? What worked better than you expected? What broke that I didn’t predict?

I’m collecting these stories, the real ones, where things go sideways in ways nobody planned for. That’s where the good insights hide.

Share

Until next time.

Cheers,

Brian

It wasn’t about price action, subnet emission schedules, or validators. It was simple, direct, and exactly what every normal person is thinking:

“Is Bittensor likely to get a user-friendly interface? Similar to ChatGPT, DeepSeek, etc.?”

The user went on to ask why they can’t just open an app and use the network they keep hearing about.

This question is a milestone. A year ago, people were asking, “What is Bittensor?” Now, they know the name, they know the narrative, but they want a clean, intuitive experience.

But it’s also a fair critique and the only one that matters for long-term adoption.

Reality Check: The Shopping Mall, Not the Store

ChatGPT is one product.

Bittensor is a marketplace of many.

When you walk into a shopping mall, you don’t “buy the mall.” You look for a directory to find the specific store that sells what you need, whether that’s clothes, coffee, or electronics.

Bittensor doesn’t have that directory yet.

When I first tried to use Bittensor back in 2023, the only consumer-facing app I could find was Corcel chat. It was basic. Everyone else was getting blown away by early ChatGPT, and here I was testing a chatbot that felt like it was stuck in 2018.

I remember reading old tweets saying Bittensor was a scam. “There’s nothing happening on it. Corcel is useless.”

But those critics missed the point entirely.

Corcel wasn’t the network. It was one room in the mall. And the mall was still under construction.

Framework: The Three-Layer Front Door

If you are waiting for an official Bittensor Super App to launch from the Foundation, you might be waiting a while. That’s not what this protocol is designed to do.

Instead, the ecosystem is building the Front Door in three layers. Here is where we stand today:

Layer 1: The Key (Wallets)

This layer is basically solved.

You need a way to hold TAO and interact with the network. There are several wallets now that make this relatively painless- check here if you want a quick start figuring out which one to start with.

• TAO.com: iOS beginner-friendly option

• Nova: Mobile wallet with hardware support

• Crucible: For more technical users

• Talisman: For multi-chain power users

The key is no longer the bottleneck. You can get in.

Layer 2: The Lobby (Discovery)

Six months ago, finding a subnet meant scrolling X threads, decoding raw blockchain data on Taostats, or asking Discord and getting 47 conflicting opinions.

That just changed in December 2025 with Subnet.ai.

This is the first real Mall Directory for Bittensor. Not just a prettier Taostats—a purpose-built discovery layer.

Every subnet gets one profile page with plain English explanations and key metrics (emissions, liquidity, miner distribution, age, TVL).

But it goes further than other explorers have so far, now you can access information on the subnet team (most of them doxed), their roadmap and even historical context (who owned the subnet slot before and what they did) in the one place.

I’ve spent months drowning in information on Taostats and other explorers, switching between tabs, cross-referencing Discord messages, trying to piece together whether a subnet is legitimate or just emission farming.

Subnet.ai gives you just enough context to make a decision. Seeing actual LinkedIn profiles of team members, understanding the subnet’s history, knowing who you’re trusting with your attention. It makes the abstract feel real.

The Remaining Gap:

We still need guided discovery—a tool that asks what you need help with and surfaces the right subnet in 3 clicks. Subnet.ai gives you the full directory, but for now (the app is still in beta) everything is not so easily categorised.

Most people still want a simple path: state their need, get directed to the solution.

That’s coming. But for now, if you want to research a subnet before getting stuck in, this is where you start.

Layer 3: The Rooms (Applications)

This is where the magic happens.

These are the actual apps that give you something to do once you're inside. Think ChatGPT equivalents, trading tools and image generators.

The good news is they’re emerging fast.

The bad news? They’re scattered and most people don’t know they exist yet.

Here are some user friendly options:

For Chat & AI Assistance:

• Try Chutes (chat interface with auto model selection).

For Trading:

• Explore GoTrader from Gopher (reported ~60,000 signups in ~1 month, high retention).

For Compute & Infrastructure:

• Check out Celium (rent GPU compute peer-to-peer).

For AI Detection:

• Download BitMind to verify the authenticity of photos and videos in real-time.

For Decentralized Storage:

• Try Hippius (decentralized file storage with encryption).

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Now that you know what exists, here's how a beginner actually navigates these layers.

User Journey: Find Your Room in 4 Steps

This is a simple onboarding journey a non-technical user can follow starting this week.

Layer 1: Get a Key (Wallet)

Choose one wallet, transfer a small amount of TAO. Stake to root once to start earning basic network rewards.

Layer 2: Use an Explorer as a Map

Use Subnet.ai to identify 1-3 subnets that match a goal you care about. Don’t try to understand all 128 or you hit instant overwhelm.

Layer 3: Step Into One Room

Choose a subnet based on your use case. Sign up and run one basic task a few times. See how it feels. Check the latency. Does it solve your problem?

Track and Expand

Check how often you actually use it. Stay or try a different subnet.

My Current Setup:

I’m following this exact path. I use Crucible as my wallet (as I mentioned last issue), now Subnet.ai as my directory when I need to research new subnets and I’ve settled into a simple daily workflow: running models through the Chutes API for AI tasks and Hippius for decentralized storage when I need it.

Nothing fancy. Just two rooms that solve specific problems.

That’s the point—you don’t need to use the entire mall. Find your two or three rooms and stick with them until you need something else.

Spotlight: A Front Door You Can Try Today

Want to see what a unified front door actually looks like right now?

Check out Macrocosmos. They’re building something that feels closer to an integrated AI research workspace than isolated tools scattered across Discord channels.

Their web app at Gravity Marketplace is the hub. Think of it as your home base.

From there, you can jump into:

• Gravity – an LLM-powered scraper that pulls data from the web

• Mission Commander – an AI agent (powered by their Apex subnet) that helps you brainstorm queries

• Nebula – visualizes your data by sentiment and scale so you can actually see patterns

Here’s a real use case:

Say you want to build a global sentiment dataset analyzing how people really feel about AI automation—the emotional reactions, ethical debates and the balance between optimism and fear.

You could scrape X conversations, Reddit threads, and news commentary with Gravity, use Mission Commander to refine your search parameters (filtering for specific industries, demographics, or discussion themes), then visualize the emotional landscape in Nebula to see where fear clusters versus where optimism dominates.

Or maybe you’re training a custom chatbot and need quality datasets. Same workflow.

It's still beta, still evolving. But you can use it right now to get actual work done.

This is what the future looks like: subnets bundled into workflows that solve actual problems.

The UX Gap Is Worth Millions

Joseph Jacks said it best on The TAO Pod:

“Bittensor subnets provide raw commodity outputs. It's really the job of the subnet team to provide an interface to access that commodity.”

Think oil before refineries, electricity before light bulbs. The value isn’t in the raw resource, it’s in making it accessible. We’re where the internet was in 1991. Most subnets are grossly undervalued because they have no user interface.

Here’s what actually matters in 2026. Watch for these signals:

• Which subnets ship no-login demos that deliver value in under 60 seconds.

• Which teams bundle workflows, not just APIs (like Macrocosmos is doing).

• Which apps stop requiring you to understand TAO, validators, or miners to get started.

Multiple teams are racing to build competing front doors. Some will bet on AI chat interfaces, others on productivity suites, others on specific verticals like storage or compute.

One or two will nail it so well that the technology disappears. You won't be just using Bittensor, you'll just be solving problems.

Don’t wait for the perfect onboarding experience. Click one link from this piece—Subnet.ai, Macrocosmos, TAO.com—and test one workflow this week.

Because six months from now, when someone asks you “How do I get started with Bittensor?” you won’t send them a whitepaper.

You’ll say: “Here’s the app I use. Takes two minutes.”

Be that person.

Share

Until next time.

Cheers,

Brian

What’s your biggest frustration trying to use Bittensor? Hit reply and tell me what confused you most. I’d love to feature community experiences in a follow-up piece.

It was 2017. I had just bought my first serious amount of Ethereum. I was moving it from Coinbase to a platform called MyEtherWallet (MEW) to participate in an ICO.

If you weren’t there, it’s hard to describe how primitive it felt. The interface looked like a phishing site. I checked the hexadecimal address probably twenty times. I sent a test transaction. I waited. I sweated. I refreshed the page.

It felt like defusing a bomb.

Crypto in those days was user-hostile by design. It was a test of faith.

Then came the Solana cycle, and with it, the Phantom Wallet. Suddenly, crypto almost felt like using an iPhone.

It was clean and intuitive and showed me my NFTs. It just worked.

That was the moment I realized: adoption happens when the interface becomes invisible.

We’re finally reaching that moment with Bittensor.

For a long time, interacting with the Bittensor network meant wrestling with command-line interfaces.

It was reserved for developers and people comfortable with terminal windows—which, as a dad who spends his mornings pulling toddlers apart over green and yellow breakfast spoons, is not me.

If we’re building a decentralized world brain to rival OpenAI and Google, it can’t live behind code. It has to be usable by anyone who cares about owning their data.

That begins at the front door: wallets.

There are now four solid wallet options, each claiming to be the best for different reasons. If you’re new, the sheer volume of comparisons can leave you paralyzed.

So I’m going to cut through it.

Not as an expert who’s been here since mainnet launch, but as someone who fumbled through this decision months ago and learned what actually matters.

The Landscape: One Size Does Not Fit All

Unlike Bitcoin, where you generally just hold, Bittensor is a living organism. You want to interact with it.

Your wallet is the controller: stake to subnets, delegate to validators, earn rewards.

The command-line era is ending. But now we face a different problem: choice paralysis.

We finally have a handful of strong options, but the sheer volume of claims and comparisons can leave anyone confused.

Here’s the framework I wish someone had given me when I started: Pick based on where you are now, not where you think you should be.

Below are four wallets mapped to user archetypes. Find the one that sounds like you, start there, and know you can switch later.

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The Mobile All-in-One for Everyday Use: TAO.com (iOS)

If you’re new to crypto or new to Bittensor, this is where you should start.

TAO.com is as close as you can get to the official Bittensor wallet, evolving from the original Opentensor Foundation app. It’s iOS only (for now), and built specifically for Bittensor. Not a multi-chain compromise trying to be everything to everyone.

You get FaceID security, a clean portfolio view and a built in fiat on-ramp via Coinbase integration.

Why I like it: It handles the complexity for you.

It prevents you from making silly mistakes (like staking below the minimum threshold). It’s perfect for the person who wants to check their staking rewards while waiting in line for coffee.

Best feature: Native Subnet exploration. You can click on a subnet and actually see what the team is doing.

The catch: It’s mobile-only and ecosystem-locked. You won’t be storing your ETH here.

The trade-off: You’re giving up some sovereignty for ease. TAO.com controls certain security decisions for you. For most people starting out, this is the right trade. But if you’re holding more than a few hundred dollars of TAO, you’ll eventually want more control.

The Mobile Power User: Nova Wallet (iOS and Android)

If you are on Android, or if you are deep in the Polkadot ecosystem, Nova is the powerhouse.

Because Bittensor is built on Substrate (the same tech stack as Polkadot), Nova treats TAO like a first-class citizen. It is incredibly robust, fast, and feature rich.

Why I like it: Ledger Support on Mobile.

If you’re holding more than a few hundred dollars worth of TAO, or you’re the type who sleeps better knowing your private keys are in a hardware device that’s never touched the internet, this is your path.

Best feature: Zero-commission staking and phishing protections make it safe and rewarding. The interface is battle tested across the entire Substrate ecosystem.

The catch: It’s a multi-chain wallet, so it can feel a bit noisier than the dedicated TAO.com app.

When to use this over TAO.com: When you’re ready to pair a hardware wallet with mobile convenience, or when you’re already managing Polkadot assets and want everything in one place.

The Staking Automation Specialist: Crucible (Chrome Extension)

Built by Ala Shaabana (Bittensor’s co-founder) and his team. Crucible is designed for the desktop user who treats their TAO as a serious investment vehicle.

Why I like it: The Smart Allocator.

Staking strategies can be complex. Which subnet is performing best? Should I move my stake? Crucible offers a Smart Allocator that automatically deploys your rewards to the top performing subnets. It effectively compounds your growth without you having to micromanage the dashboard every day.

This is the wallet for people who think of TAO as a position to optimize, not just a token to hold.

Best feature: Custom Staking. You can design your own allocations (e.g., top 10 subnets) or stick to Root.

The catch: It’s a browser extension, so no mobile. The Smart Allocator works best with amounts over 15 TAO (smaller amounts work but are slower).

When to graduate here: When you’re checking your staking yields weekly and thinking “I could optimize this.”

The AI-Native Multi-Chain Explorer: Talisman (Chrome extension)

If you are used to MetaMask or Phantom, and you hold assets across Solana, Ethereum, and Polkadot, Talisman is the everything wallet for you.

Talisman is positioning itself as the “AI Wallet.” They are integrating directly with Bittensor Subnet 45 (Talisman AI) to bring AI agent features directly into the wallet experience.

The AI features are still emerging but the integration is real. Cross-chain swaps mean you can move assets without leaving the wallet.

Why I like it: The UI is polished and intuitive. It gives you a unified view of your entire crypto net worth across all chains. It allows you to see your ETH and TAO side-by-side.

Best feature: The Portfolio View. It is arguably the best visualizer of wealth in the ecosystem.

The catch: The AI features are promising but still rolling out. And if all you care about is Bittensor, the multi-chain breadth might feel like overkill.

When this makes sense: If you’re already juggling ETH, SOL, DOT, and TAO and need one pane of glass. Or if you’re genuinely excited about AI agents managing your portfolio in the future. Otherwise, stick with something simpler.

The Decision Framework

Don’t overthink this. Here’s how to decide in 60 seconds:

The wallet you choose today doesn’t have to be your forever wallet. I started on MyEtherWallet in 2017 because it was the only option at the time. You can upgrade later.

My TAO Setup: Ledger hardware + Crucible interface (Desktop).

The Ledger is my vault. My private keys literally never touch the internet.

Even if my laptop gets compromised tomorrow my TAO stays safe. The transaction has to be physically confirmed on the device itself.

The Crucible Smart Allocator runs in the background, automatically rebalancing my stake across high-performing subnets. I check in once every few weeks. It’s set it and forget it compounding.

This is the setup that helps me sleep well at night. Maximum security with acceptable flexibility.

Your Three-Step Launch Plan

Step 1: Pick your wallet based on the framework above.

Sixty seconds of honest self assessment saves hours of regret.

Step 2: Download and set up.

Five to ten minutes max. These are designed to be easy.

Step 3: Start small.

Transfer minimal TAO to test. Stake to root or a subnet. Watch the rewards tick up daily. Get comfortable with the flow before you move anything significant.

The Golden Rules

You’ve picked your wallet. You’ve done the test run. You’re ready to move real TAO.

Here’s where most people screw up. Not because they’re stupid, but because crypto punishes small mistakes with large consequences.

Back up your recovery phrase offline.

Write it on paper. Put it somewhere safe. Do not screenshot it. Do not save it in a notes app. Do not email it to yourself.

Why? Because your phone gets backed up to the cloud. Your notes app syncs across devices. Your email gets hacked. And suddenly, the 12 words that control your entire TAO balance are sitting on a server somewhere waiting to be exploited.

If your recovery phrase exists digitally anywhere, it’s not secure.

Use official links.

Phishing is so good now that even experienced people get fooled. Scammers buy Google ads. They register domains one letter off from the real thing. They clone entire websites.

How to avoid this:

• Verify URLs before downloading anything.

• Bookmark the correct site immediately.

• Never click links in Discord DMs or X replies.

• If you’re searching for “TAO.com wallet” and the first result is a sponsored ad, keep scrolling.

Understand fees.

Bittensor has transaction fees. They’re usually small (fractions of a TAO), but they add up if you’re constantly moving things around.

Before you confirm any transaction, look at the fee estimate. If it seems high, wait. Network congestion drives fees up temporarily.

Also: unstaking has a waiting period (varies by subnet). Plan accordingly. Don’t unstake everything if you need liquidity tomorrow.

Your Mission

Nine years ago, I sweated through my first wallet setup because I had no choice. There was no easy button. MEW looked like a phishing site because everything looked like a phishing site back then.

Today, you have four viable paths. Each designed for a different stage of the journey. Each with trade-offs that are clear if you know what to look for.

If your TAO is still sitting on an exchange, you aren’t really participating in the network. You’re just a spectator.

Exchanges own the keys. They control staking decisions. They take a cut of your rewards. And if they decide to delist TAO, freeze withdrawals, or get hacked, you’re at their mercy.

The Bittensor ecosystem is complex and that’s exactly why choosing a wallet matters. It turns complexity into action.

Pick a wallet this week. Download it. Transfer a test amount of TAO. Stake it to root. Watch what happens.

Then come back and tell me what your onboarding experience was like. What confused you? What surprised you? What should change?

Wallets are the front door to this ecosystem. Pick one. Walk through. See what happens when you stop being a spectator.

Share

Until next time.

Cheers,

Brian

It’s also the time, if you’re so inclined, for the end of year review, where you sit back and ponder how your year went, journal on where it all went wrong and plan feverishly for a new start in January.

In reality, year end reviews do matter: it’s easy to forget how action‑packed a year was and what it taught you.

Between Bittensor’s first halving and the dTAO upgrade rewiring incentives and my own push to contribute more meaningfully to the ecosystem, this year felt like one long, intense upgrade cycle.

Looking back, Bittensor evolved from a promising experiment into a battle-tested network. Subnets multiplied, incentives sharpened, and yes, there was plenty of drama to keep things interesting.

As for me, writing this newsletter turned from a side project into my main vehicle for making decentralized AI accessible.

Today, I’m reviewing the highs, lows, and lessons from 2025. Both for the ecosystem and my journey in it.

Bittensor’s 2025: From Halving Hype to Real Maturity

After a messy, high velocity 2024, Bittensor hit maturity in 2025. This was a year of enforced focus: cap, pruning, flow emissions, and the halving pushed value toward utility.

Key highlights:

February 2025: dTAO Launch

Introduction of dynamic TAO (dTAO) as subnet-specific “Alpha” tokens. Instead of rewards being handed out from one big central pot, they now flow to subnets that actually perform well.

This resulted in less freeloading, fairer rewards, and value routed toward real work.

June 2025: Subnet Cap Announced

Temporary subnet cap introduced at 128 (deployed in September) following rapid expansion in the prior months.

New registrations were paused to reduce noise, improve technology quality and allow deeper exploration of scaling impacts.

October 2025: Subnet Deregistrations Begin

This “pruning” mechanism enforced performance standards, automatically removing underperforming subnets after a 4-month immunity period (SN100 was the first to go), keeping the network lean and focused on quality.

New registrations were reinstated alongside the pruning.

November 2025: Taoflow Transition

Shift to the “Taoflow” flow-based emission model. Instead of paying everyone on a fixed schedule, the network now pays subnets in real time based on the work they actually do.

If a subnet is useful and active, it earns more; if it’s idle, it earns less, cutting waste and rewarding real contributions.

December 2025: The First Halving

The big one. Daily TAO emissions were cut in half, from 7,200 to 3,600 per day. Leading up, prices dipped as miners repositioned, but post-halving, we’ve seen stabilization that hints at stronger fundamentals.

It wasn’t the moonshot some predicted, but it pruned inefficient subnets and rewarded those with real utility.

As I covered previously, halvings aren’t fireworks; they’re the quiet force that builds long-term value.

Ecosystem Milestones

Community activity surged, with visible growth in social engagement and developer chatter (OpenDev, Discord, Telegram, X).

DeFi integrations grew and tools like Bitstarter made funding more accessible.

We saw major emission changes to root stake in December, shifting power dynamics and emphasizing sustainable incentives.

OpenDev’s weekly summaries kept everyone aligned, covering deployments and plans into 2026.

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Challenges That Defined Us: The Messy Side of Decentralization

Post-Halving Volatility and Price Pressure

The December halving tested the network, with TAO’s price falling roughly 20% amid miner repositioning and market jitters.

This exposed overinflated subnet valuations and squeezed margins, but it consolidated capital around proven utilities, stabilizing fundamentals over hype.

Deregistration Drama and Pruning Pressures

September’s subnet cap rollout and October’s deregistration sparked debates, with over 10 subnets at risk and disappointment at subnets with a real use case losing out to others offering no value to the ecosystem.

Incentive Misalignments and the “Death Spiral” Fears

dTAO’s early launch fixed static emissions, yet grifting accusations and miner margin collapses raised “death spiral” alarms at launch: where declining value, participation and rewards could create a vicious cycle leading to network collapse.

Technical and Scaling Hurdles

The codebase faced criticism for messy metrics, scaling issues complicated onboarding for new users and exposed centralization risks.

Adoption and External Pressures

Real-world traction lagged in areas, competing with centralized AI giants amid regulatory shadows on tokens. Alignment woes, like corporate-biased AIs, underscored Bittensor’s edge but kept skepticism alive on utility and incentives.

Overall, 2025 proved Bittensor’s thesis: incentives coordinate intelligence without gatekeepers. The network’s Total Addressable Market expanded as real-world apps emerged, from Claude Code killers like Ridges to confidential compute in Targon. If you’re new, this was the year we moved from “cool idea” to “essential infrastructure.”

End of term grade: B

Maturity gains (dTAO/Taoflow/halving, pruning) offset by onboarding/UX/messaging gaps that still slow mainstream adoption.

My Contributions: Turning Confusion into Clarity

This newsletter started as my way to make sense of Bittensor. Six months later, it’s become a bridge for non-coders like me to navigate the ecosystem. Here’s what I shipped in 2025:

• Practical frameworks: the 5-Stage Awareness Journey for newcomers, plus VITALS and the RALLY plan to help subnet owners avoid deregistration.

• Subnet spotlights: Bitmind (deepfake detection), Aurelius (alignment), Hippius (cheap private storage), with real-use examples like verifying viral posts or ditching Google Drive.

• Ecosystem insights: marketing pitfalls, the “Infinite Game” of network drama, and the Funnel Framework for entry points (from creators to miners) plus UX critiques to make pages clearer.

• Community Engagement: I lurked in Discords and X, and incorporated reader feedback. My goal is to make decentralized AI feel like flipping a light switch with no PhD required. If even one reader went from lurker to contributor because of these pieces, mission accomplished.

Personal routines that made it possible:

• Productivity: As a Dad with limited time, Pomodoros turned paralysis into progress: 25-minute sprints on one task (a subnet CTA, a draft), break, repeat. Gamifying each burst finally made me consistent.

• Tip: Define your One Thing (e.g. master one new tool weekly), measure it and start with Pomodoros—the simplest habit that compounds.

These aren’t revolutionary ideas, but they helped make Bittensor feel a little less chaotic.

From Survival to Signals: Turning 2025’s Lessons into 2026 Wins

2025 wasn’t perfect, but it solidified Bittensor as the infinite game: coordinating global intelligence without collapsing under its own weight. For me, this newsletter proved non-coders can add value—through frameworks, spotlights, and honest takes.

Looking to 2026: Expect a shift from experimental to production-grade outputs, with revenue from real-world AI outputs surprising skeptics and driving organic growth. I’ll keep delivering practical tools to make it accessible.

What’s your 2025 Bittensor highlight? Biggest lesson or win? Hit reply or comment here I’d love to know.

Best wishes to you for the holiday period- I’ll be refereeing an impending war over Spider-Man Lego pieces at home for the next two weeks, so I’ll talk to you next in 2026.

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Cheers,

Brian