This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: GenLayer

2. Clankertown: Stock-Pair Fees Become an Agent Budget

3. The Other AI/RWA Layers I’m Watching

4. What’s Next

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

An AI agent can write a research report, build a tool or complete a task, but who decides whether it actually did what it was paid to do?

That question becomes much more interesting when money is waiting on the answer

GenLayer is building a network for AI-powered judgement, using Intelligent Contracts that can interpret natural language and access information from the web

Its approach brings multiple validators and AI models into the process, with a proposed result checked by others and an appeal mechanism for disputed decisions

Think about the potential applications: deciding whether an agent met a brief, evaluating evidence before releasing a payment, or resolving a disagreement over a piece of work

Those are examples of what this kind of infrastructure could support, rather than a claim that it’s already integrated into the projects below

As agents start doing more paid work, deciding what counts as successful completion becomes a valuable layer of its own

Explore GenLayer here

This newsletter goes out weekly to 7.4k+ subscribers.

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1) Clankertown: Grok AI Agents are Being Paid in SPCX Stock

There is now a town where AI agents can earn tokenized SPCX; they talk, assess each other’s contributions & submit code (“Gitlawb”) to improve the systems around them

The Grok + SPCX combination makes for an obvious headline with the upside it may catch Musk’s attention, but the mechanism underneath is what caught my attention

A speculative market is being used to finance an experiment in agent labour

Clankertown’s token, CLANK, sits in the Pons ecosystem on Robinhood Chain. The town receives creator fees and allocates part of that budget to agents, with rewards paid in

More details can be found here where I broke down the mechanism in this diagram:

For every 100 units of fees received under that allocation, 45 go towards the treasury, 45 towards rewarding agents and 10 towards buying back & burning CLANK

That gives the project three different economic functions:

1. capital to deploy,

2. a budget for participation, and;

3. a direct token sink

The agent pot also isn’t emptied immediately:

Every two hours, 5% of the accumulated pot is allocated by score, subject to participation requirements. The remainder stays available for future rounds

That smooths the distribution of rewards, although it doesn’t remove the underlying dependence on fees

What Are the Grok Agents Actually Doing?

Well, there are now 3,233 agents that have now made more than 2.5M interactions:

Clankerown’s current treasury and split of fees includes $14.4k in SPCX tokenized stock purely for agentic incentives:

Every two hours, 5% of the accumulated agent pot is distributed by score, subject to participation requirements. The remainder carries forward

Agents are run by human operators, with earnings paid to their wallets. They discuss ideas, answer questions and assess each other’s contributions

The workshop takes this further: agents can submit code and review each other’s work

Under the published rules, patches need 75% jury approval and workshop credits vest after seven days. The workshop receives 20% of the agent payout

On 22 September, the project reported 141 patch submissions and 14 merges, including a tool that checks whether individual rewards add up to a round’s stated total

So agents are already contributing tools to inspect the system paying them

Why This Matters

Stock-pair fees can become a working budget for a community: paying contributors, buying inference and commissioning tools

The next step could be agents building products that generate their own revenue, allowing the community to fund more work without depending entirely on trading activity

Clankertown has published a jobs pathway for commissioned work and collaboration, although that doesn’t yet establish repeat customer demand

The economics are what I’ll be watching. Operators need rewards that cover running costs, while the community needs useful output in return

Completed work, inference costs and paying customers will tell us more than agent counts

While this particular protocol is high risk, it sets the foundation of what could come next at a much larger scale with a significantly larger budget! So I would pay closer attention; this could become a successful Metaverse housed by billions of AI Agetns

2) Other AI/RWA Protocols I’m Watching

Long ecosystem tokens:

• Monitoring (Paired with stock): Was just followed by the co-founder of Palantir who quote tweeted one of their posts, sending the price of the token up 14x

• Saylormoon (paired with stock): One of the team at MSTR engages with the account + has a Saylormoon PFP; potential PLTR moment potential? Only downside is the x account isn’t as effective as PLTR

• Moo (paired with stock): close with the Long team + building more tools for memory stock infrastructure and exposure

• Cache Cow (Paired with $SDSK ) as an alternative lower cap beta memory stock pair to Moo

• Schiffy (Paired with ): Has strong team around Axie gaming with potential for burns of the meme token through player activity

• CHONK (Paired with ): has a chonkification AI tool + basic game

• QUBIT (paired with )

• iCoin (paired with ): Great X account here, similar to PLTR although Apple is more of a corporate stickler and less likely to engage with these kind of plays from a branding perspective

Broader AI RWA side:

I put out a post recently for insights into protocols building at the intersection of AI & tokenized stock mechanisms:

There were a number of solid responses; the projects cover different parts of the opportunity. Some already connect fees to AI usage; others provide tools that tokenized-stock communities could adopt

• Orbio | RH: ORBIO staking generates CREDIT for inference. It also describes token/CREDIT pools whose liquidity fees can help fund agents, connecting token economics with compute budgets

• Token Machine: Paired with Orbio, enables Gacha style mechanism for AI credits… novel idea that merges the intersection of AI inference and gambleFi

• Surplus | Base: A marketplace for unused inference capacity that routes requests towards cheaper offers. Lower model costs could extend the operating budget of fee-funded agents

• VCAT + Venice / VVV pair | Base: Staking VVV or DIEM provides inference allowance. That gives communities a potential use for accumulated rewards: funding compute for their own agents… VCAT gives VVV reflections

• ZZZ / Exponent Labs | RH: Now presented as Autonomous Protocols, the project coordinates people and AI around shared resources, proposals and tasks. The relevant metric is work completed with those resources

• Agent Wormhole | RH, Arc, + Base/SOL payments: Security tools check malicious instructions, token metadata and payment details before agents spend. The project says paid API revenue funds WORM buybacks & burns (4% gone to date):

• Others include: BundleCat, Ubik, Cerebro, Musebook

4) What Next?

I’ll continue to monitor for innovative mechanisms, particularly the app layer that leverages agentic/AI infra to create RWA products that consumers will find sticky!

I believe we are month 3 of 7 (or maybe 9-12… nobody really knows!) of a bull market, led by RWAs and catalyzed by AI. I go back to this post earlier this month which compared prior timelines to the AI Agent boom in Q4 2024:

This leads me to believe the largest opportunities are yet to come; particularly if the broader crypto market takes another leg up… we could see that mythical alt season return with AI/RWAs leading that charge!

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights

Disclaimer: The content of this newsletter is for informational purposes only. I am commenting on the underlying activity of what I am observing in the market to keep my audience updated and informed.

Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting

This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: Axis Robotics

2. Standard Reserve’s monetary model

3. Bank Charters and Branches

4. Benchmark Analysis: Standard Reserve vs NetNet Capital

5. The launch and current economics

6. How to participate + the main risks

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Large Language Models (LLMs) learned from the internet with “digital” being sufficient, but Robots need physical interaction data to evolve

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As robotics companies move from impressive demos towards general-purpose machines, proprietary interaction data is becoming one of the industry’s most valuable resources

Axis is building the engine that collects it.

Explore Axis Robotics and track their monthly progress through their X articles

This newsletter goes out weekly to 7.4k+ subscribers.

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The Standard Reserve: An “Onchain Central Bank”

Onchain banking normally means lending, borrowing and trading; “The Standard Reserve” has interpreted it more literally…

It’s created 1,000 banks, issued each one a Charter & connected them to an autonomous monetary system that adjusts issuance according to capital flows

Why has this gotten so much attention?

Well, it’s been likened to “Ohm” which was a crypto protocol in 2021 that ran into the billions; some of the largest participants netting $80m in unrealised gains, until it all came crashing down:

Prior protocol shortcomings have been analysed, so perhaps this new Reserve Banking mechanism will follow a different outcome this time?

Buckle up… this is a bit of a read but those who do this NOW could be thanking themselves a month from now

1. Standard Reserve’s Monetary Model

Standard Reserve describes itself as a sovereign onchain central bank; It’s not a regulated bank, it doesn’t offer insured deposits, & STANDARD is not a stablecoin

It is also an independent protocol deployed on Robinhood Chain, not a product operated by Robinhood… to emphasize this!

The system consists of:

• One currency: $STANDARD (ERC-20 token)

• One canonical STANDARD/ETH market (Liquidity pool)

• Banking Charters (NFT)

• Branches that earn token issuance

• Expansion and contraction vaults

• A monetary policy based on net ETH flow

The protocol monitors how much ETH enters its canonical pool through STANDARD purchases and how much leaves through sales

This determines whether the system is expanding or contracting:

1. Positive net ETH flow places Standard Reserve into expansion (as per below with 3,109 ETH)

2. Negative or neutral net flow places it into contraction

The current epoch determines where trading fees are routed. Monetary issuance responds more slowly, using the previous two completed epochs.

The protocol collects ETH-based taxes from both buyers and sellers; those fees are divided:

• 70% to the active “expansion” or “contraction” vault

• 15% to protocol-owned liquidity

• 15% to the team

During expansion, the largest allocation can be used to accumulate tokenized gold and other reserve assets… e.g. XAUT (Tether Gold… ACTUAL tokenized gold thanks to RWA issuers on RH chain)

During contraction, it finances open-market STANDARD purchases and burns.

STANDARD holders cannot redeem their tokens for a fixed amount of ETH, gold or any other reserve asset.. the reserves are monetary-policy tools rather than direct token backing… I’ll touch on this distinction with the benchmark against Net Net cap later

Day 1 Performance Analysis:

$STANDARD has already reached a $47 million market cap ($470M valuation), with the first Branch auctions selling out within minutes:

The price action illustrates there’s demand, but here’s why it’s interesting

Most previous DeFi reserve currencies relied on fixed or aggressively expanding emissions…

• They produced attractive yields while new capital entered (Ponzi 101)

• Once demand slowed

• issuance continued

• supply overwhelmed buyers and the yields became irrelevant…

Ohm is a prime example (hit multi billion dollar val at its peak FYI):

Standard Reserve is attempting to make issuance responsive

Its base issuance begins at 700k STANDARD per day, distributed across every active Branch (currently 1,099 live branches):

1. Sustained positive ETH flow can increase the issuance multiplier

2. Negative flow tightens it while redirecting fees towards buybacks

This creates several competing forces; the protocol is continually balancing token issuance against token burns, Branch demand + banker exits; essentially BETTER protocol design:

2. Bank Charters and Branches

A “Founding Charter” is an NFT banking licence…

There were 1,000 Genesis Charters at launch; each began with one Branch & can operate a max of ten

Charters are initially soulbound (meaning they cannot currently be transferred or sold…yet) despite there currently being a $7.5k offer on them on OpenSea (pictured above)

• Branches are the productive assets inside each Charter

• Every active Branch receives an equal share of daily STANDARD issuance

A Charter with ten Branches earns ten times the issuance of a Charter with one, although every new Branch added to the network slightly dilutes every existing Branch

Ten Branches produce ten shares of issuance, while STANDARD accumulates as a pending internal balance… that balance does not immediately enter the banker’s wallet

Withdrawing destroys the Branch

To withdraw STANDARD (the liquid token), a banker must permanently retire a Branch.

If a Charter has ten Branches and retires one, one-tenth of its internal balance is released, that Branch then stops earning forever.

Retiring the final Branch releases the remaining balance and burns the Charter itself. Withdrawals are also subject to a resolution fee ranging from 2% to 60%; the fee responds to total withdrawal pressure across the previous seven days:

• Half is burned.

• Half is distributed to the bankers who remain.

The holder bonus therefore ranges from 1% to 30% of exiting balances. Bankers cannot repeatedly claim emissions while retaining all their productive capacity and every withdrawal exchanges future earning power for current liquidity

Charter owners must also remain active. Failing to check in for 30 days allows another participant to report the dormant bank, triggering a 70% penalty and burning its Charter… the game theory gets more interesting!

SO CHECK YOUR CHARTERS PERIODICALLY

3. Standard Reserve vs NetNet Capital vs the Federal Reserve

Standard Reserve is not Robinhood Chain’s first “reserve” experiment. NetNet Capital has already built a reserve-backed onchain fund. The two use similar financial language, but the underlying economics are different:

A key takeaway to note:

NetNet can be valued against its reserve backing. STANDARD cannot.

NetNet enforces at least one USDG of onchain risk-free value for every NET. This creates a calculable NAV and limits how much NET can be issued.

STANDARD offers no equivalent per-token backing or redemption claim.

Its valuation depends on whether demand from Branch auctions, Charter access, trading and future buybacks can absorb its long-term emissions

4. The launch and current economics

The launch established clear demand for both the currency and the banking layer after snipers were taxed heavily + the early launch distribution was equitable:

I covered the first auction burn, remaining Branch capacity, initial Charter exit and current licence economics in the post below:

There are three parts of the system that matter from here:

1. STANDARD’s future supply

2. The changing economics of each Branch

3. The optionality attached to owning a Charter

so I’ll expand on each:

i) STANDARD’s future supply

Around 100 million STANDARD was created at Genesis, primarily to establish protocol-owned liquidity. The remaining 900 million forms the future “issuance budget”.

This makes the distinction between current market cap and maximum-supply valuation important… essentially a 10% float currently. But at current burns from licence mints far exceeding the issuance this is becoming a deflationary token (for now)

ii) What is a Branch worth?

The basic calculation is:

Licence cost in STANDARD ÷ expected STANDARD earned per day

The token price cancels out when calculating the simple payback period in STANDARD, although expected earnings constantly change.

They depend on:

• Total daily issuance

• The monetary-policy multiplier

• The number of active Branches

• Future licence auctions

• New Charter issuance

• The eventual resolution fee

The displayed payback period assumes these variables remain unchanged. They won’t…!

• More Branches dilute existing bankers.

• Lower issuance extends the payback period.

• Higher issuance accelerates earnings but also increases total token supply.

A Branch should therefore be valued under multiple scenarios rather than using one headline yield. Poor one out for the solo banker that exited immediately at a loss:

iii) What is a Charter worth?

A Charter provides access to a part of the protocol that STANDARD holders cannot reach independently

It begins with ONE productive Branch and provides capacity for nine more… so you’ll accrue STANDARD by holding a charter (as long as you don’t burn your only branch!!)

At the time of writing there are only 10 Banks that have 4 branches on them:

One of these holders has two charters with 4 branches on each… this would yield roughly 5.1k STANDARD ($2.1k) per day

It also creates several forms of optionality:

• Participation in future Branch auctions

• Exposure to holder bonuses

• Accumulation of an internal STANDARD balance

• Potential sale of the entire banking portfolio

The protocol contains a one-way switch that could permanently enable Charter transfers. If activated, a Charter could be sold together with its Branches and accrued balance. This would create a second exit route… essentially a sale of your entire operations to a third party

5. How to participate

Standard Reserve has different entry points depending on available capital and whether someone already owns a Charter.

a) Observe without deploying capital

The protocol can be followed through its live dashboard. The most important metrics are captured here onchain for anyone to observe:

The first completed policy epochs will provide more information than the opening price action, and for anyone savvy enough to grasp the economics can then play the market very well in this larger game theory

b) Buy STANDARD

The simplest liquid position is STANDARD itself; this provides exposure to the currency without operating a bank.

Holding STANDARD alone does not earn Branch emissions and does not provide a direct claim over protocol reserves

It is primarily exposure to the balance between future demand, burns, buybacks and dilution… so as long as the demand outpaces the daily issuance then this should be deflationary

c) Expand an existing Charter

(Existing) Charter owners can bid for additional Branch licences using STANDARD.

Each Charter can operate ten Branches and purchase no more than three licences in one auction. This increases the banker’s (your) share of daily issuance but exchanges a liquid token for an illiquid productive asset

Because the auction price decays throughout the day, waiting can improve the economics. The risk is that every licence sells before reaching your target price:

d) Wait for future Charter access

Don’t own a charter but want to play the game theory? You’ll need to wait for Soulbound NFT charters to go live for trading or the new season of minting

The protocol can eventually conduct daily Charter auctions denominated in ETH; the current daily Charter allocation is set to zero, so this route is not yet open.

The other potential entry is purchasing a complete Charter if transferability is activated on OpenSea

Until one of those routes becomes available, a non-Charter holder can own STANDARD but cannot operate a Branch

e) Trade around banker activity

STANDARD’s liquid supply will change as bankers retire Branches; the main events to monitor are:

• Epoch completions (3 days)

• Multiplier changes

• Rising internal balances

• Weakening Branch-auction demand

• Clusters of Branch retirements

• Increasing resolution fees

• A transition into contraction

Large withdrawals add liquid STANDARD to the market; they also increase exit fees, burn part of the withdrawn supply and reward the bankers who remain

Getting a thorough understanding of all of this could be very lucrative when trading price swings in the STANDARD token

6. What can go wrong?

Standard Reserve reorganises reflexivity, but risk is still prevelant

The main risks include:

• High token issuance (inflation)

• Increasing Branches compared to your holdings (dilution)

• Manipulation of the canonical-pool signal (contract/expansion impact)

• Smart-contract failure (bad actors everywhere)

• Illiquid and non-transferable Charters (stuck holding the bag)

• Resolution fees reaching 60% (too expensive to sell)

• Reserves that cannot be redeemed by holders

• Weakening licence demand (issuance exceeds token demand)

• Dependence on Robinhood Chain retaining capital

• Accumulated internal balances becoming future sell pressure… when people decide to exit

The protocol is non-upgradeable, but not every operating parameter is fixed.

The owner can adjust certain settings within predefined limits, including tax rates, daily licence quantities, daily Charter quantities, fee splits and auction parameters.

The autonomous central-bank framing should not be interpreted as every variable being permanently outside human control

TLDR:

To me, this is an extremely interesting onchain experiment so participating should be taken with the best risk management controls in place; don’t risk more than you’re willing to lose… but the game theory on this protocol is too good to avoid

Most crypto projects launch a token and attempt to add utility later.

Standard Reserve launched the currency, banks, Branch auctions, reserve vaults, burn engine and monetary policy simultaneously.

It also provides several reasons for participants to keep returning:

• Daily auctions

• Three-day policy epochs

• Changing Branch economics

• Internal balances

• Holder bonuses

• Potential Charter transfers

• Future Charter auctions

• Expansion and contraction cycles

The first test was whether people wanted to become bankers.

They did.

The next test is whether Branch demand, reserve accumulation and buybacks can absorb long-term issuance without the economics becoming dependent on a continually rising STANDARD price.

That answer will come from watching the auctions, withdrawals and monetary-policy changes play out over the coming months.

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto, AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. I am commenting on the underlying activity of what I am observing in the market to keep my audience updated and informed.

Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X

This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: Umia Finance, now live for trading

2. Chain Rotation: PONS, LONG, STONK, STONKEX + FLAP

3. Buttensor, TaoCats, ZCAT, NearKat

4. Innovative mechanisms: TAMPONS + memes funding perpetual positions

5. Virality Ensues: RSTR’s RuneScape “Robin Hood Hat” treasury

6. Receipts: shopping receipts for tokenized stock + Wildcard Pick

7. What I’m watching next

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

$UMIA is now live on Base through Uniswap V4. Its auction cleared at $0.36, equivalent to $18M fully diluted valuation. Those are auction figures, not today’s trading price. Auction results, launch announcement

Umia is building the tools for projects to issue tokens, raise capital + govern their treasuries. Its own token is the first demonstration, with decision markets helping determine how treasury funds are spent. Fantastic discussion here on MCG Live:

The team reports $125.4K in protocol fees within six days of launch, against a $120K monthly team disbursement demonstrating sustainability from day 1

As more projects attach treasuries and operating businesses to tokens, the rules around who controls the money become increasingly relevant

This is the part of the market Umia is building for; keep an eye out for their first incubated project going live soon… using a well refined curation committee:

Explore Umia here - Link.

This newsletter goes out weekly to 7.4k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

MemeFi Is Getting Weird

Last week we looked at memes paired with tokenized stocks... this week the shopping list includes TAO, ZEC, leveraged positions, RuneScape hats + equity kickbacks on your shopping spend from the shop you purchased the goods from… genius!

Apparently owning the meme wasn’t enough. It now needs more RWAs (including digital in game assets) to be interconnected to them

The meme contains the attention funnel to that asset… the more viral it goes the more ecosystem participants that asset is distributed to from a larger trading fee reward pool. I could see serious marketing budget being distributed by companies to embrace this level of virality, so I think this is only just getting started; there’s is going to be numerous innovative mechanisms that drive value back to end users:

The interesting development is how specific these mechainsms/applications are becoming:

• Some tokens distribute another asset to holders

• others retain assets in a treasury

• Some direct fees into a trading position

• while the latest consumer experiment wants to reward a shopping receipt with stock exposure

Each gives the community something concrete to follow: assets acquired, rewards delivered, positions opened or supply burned to drive value to end users

That also gives us something concrete to check:

• a distributable treasury balance

• an advertised reward

• a completed payment or burn mechanism

I’ve been looking at where the money actually goes, because that’s where the stronger ideas start to separate from the names

The Blockchain STONK Rotation:

This innovation kicked off on RobinHood chain through Stonkbrokers, PONS, Long and a few other early innovators

But since then other blockchains have realised that there’s huge upside to this and pairing memes with innovative onchain mechanisms could faciliate both distribution (memetic attention) + product (onchain value accrual mechanisms)

PONS + LONG + STONK + STONKEX + FLAP

Those of you who saw my X post on capital rotations across chains, likening it to the December 2024 AI Agent boom, will likely have front ran this capital rotation from RH to Solana:

The STONKS movement is spreading across launchpads, each with its own approach to pairing memes with other assets and routing trading fees:

1. PONS kickstarted the cleanest mechanism with heavy burns at a lower market cap meaning a large portion (30%+) of its circulating supply was burned from the early stages… this bypassed the typical VC route + has enabled them to accumulate a war chest to cement themselves as a leader of the STONK memeFi pairing movement.

   While many prior protocols like Pump, have taken this approach it’s been executed much later in their maturity journey meaning it would take a lot higher fees to buy/burn the circulating supply + impact the token price

2. STONKEX provides a useful example of why the fee split matters. Its documentation describes a 1% swap fee, with 0.7% going to the creator and 0.3% to the platform. Of the platform share, 80% funds STONKEX buybacks and burns. Under that stated split, the buyback allocation works out to 0.24% of trading volume. Fee mechanics.

   It also lets launches buy tokens from their own pool and distribute them to selected traders through a FOMO leaderboard. The resulting hold or sell history becomes public. Receiving an unsolicited airdrop tells us nothing about whether that wallet endorses the project.

3. StonkFun has another variation: its ecosystem buyback mechanism directs a portion of eligible pool fees toward leading eligible tokens, weighted by market cap. That is separate from rewards paid to individual holders and from buybacks of STONK itself. Ecosystem mechanism

A decent comparison of the top two is done here:

It’s clear that tokenholder value accrual is key; so number of tokens / % burned is a decent metric, of which PONS is the leader followed by STONK, then STONKEX:

Insights:

• FLAP is yet to launch a token, and LONG has a different value accrual mechanism with its leading “AI/NVDA” token which distirbuted NVDA stock to AI token holders.

• When analyzing the activity on each platform you could argue that STONK has some insane runners with large market caps/volume which in turn drives fees + greater burns. So the r/r on STONK is looking appealling

• STONKEX is interesting and monitoring the activity on Base is key. Bluechip was just followed by Cobie which sent it hurtling + Unipcs is positioning on Basecat as a leading potential Cat meme. It’s not quite at the levels of STONK/PONS but certainly worth watching

• PONS is interesting because it has the leader advantage, a larger supply burn, and more recently if you check its top coins many are innovative AI related protocols:

This tells me they’re progressive in the teams they are incubating; valuations today therefore don’t mean they’ll be comparable in the future. I do believe the next “level up” will come from an AI/agentic layer on top of these Stonk/meme RWA protocols

Cross-Ecosystem Pollination: “Buttensor” + ZCAT + NearKat

1. Buttensor (BUTT) is a particularly good example of crypto making an asset easier to discover through an absurd name… for those unfamiliar this is a meme on “Bittensor”

The reward model gives eligible holders another token as trading activity funds purchases and distributions (in TAO for BUTT holders):

2. Another similar mechanism pair is ZEC’s ZCAT, whose site describes a 3% transfer tax used to source ZEC for eligible holders. The reward depends on activity, so the rate at which you accumulate ZEC can change dramatically. ZCAT mechanics:

You can see why this catches on; a trader arrives for the meme + starts accumulating an asset they might otherwise never have bought (Snapshot below of 2.37 $TAO accumulated for holding BUTT):

The community has a second reason to remain interested after the initial joke wears off… But those rewards come out of the trading economy around the meme

3. NearKat acts in a similar manner to TAO + ZEC memetic attention engines… except with the NEAR token

To note however when looking at these rewards; there is a reporting wrinkle too:

StonkFun’s reward dashboard values historical distributions using current asset prices. A rising dollar total can therefore reflect appreciation of previously distributed tokens. It shouldn’t automatically be described as an equivalent amount of new cash paid out. Dashboard methodology.

This is the accountant in me, that wants to ensure spot distributions are recorded at spot token value in USD terms… those airdrops might be difficult to reconcile for your tax accountant!

TAMPONS + Memes Funding Perpetual Positions

“TAMPONS” takes the treasury experiment into a much more aggressive direction

Its Perps Hood page displays a 3× short position in PONS (RobinHood tokenized STONK/Meme launchpad mentioned prevuously), funded through the token’s treasury structure. The page also accepts USDG contributions as additional collateral. Those contributions buy no tokens and carry no withdrawal right. TAMPONS market.

You can see where this goes: communities expressing bullish or bearish views through a meme whose activity helps fund the position (a more universally relatable way of rallying for / against a particular asset or position):

It also explains why holding TAMPONS isn’t enough to calculate a reliable hedge against a PONS bag. The relationship depends on the short’s size, entry price, margin, funding costs + how any benefit reaches the token. TAMPONS then has its own market price, which can move independently of the treasury

Buying a dollar of TAMPONS does not establish a dollar of short exposure. You would need a clear relationship between token ownership and the underlying position to size that hedge properly

Other Leverage Platforms:

LONG’s LongX experiment approaches leveraged exposure through a wrapper. Its NVDA3x announcement describes an ERC-20 wrapping a 3× NVDA perpetual on Lighter, with swap and mint/redeem routes. The underlying funding, liquidation and execution mechanics still matter. LONG announcement.

A meme paired with a leveraged asset and a meme treasury operating a leveraged position can produce very different outcomes for holders. That distinction will matter more as these products multiply…

RSTR: A RuneScape Treasury

RSTR is using ETH fees to accumulate Robin hood hats in Old School RuneScape; quite an innovative product that appeals to millions within a specific gaming community

Yes, the in-game hats… rallying around the attention funnel connection from being launched on PONS, on Robinhood Chain

The project applies the treasury accumulation idea to an item with an existing player economy. Its site tracks the Robin hood hat using OSRS pricing data. RSTR.

The appeal is obvious to anyone who grew up playing RuneScape. The community immediately understands the object, its status + the joke. Buying more of it gives the meme a running storyline that can travel outside the usual crypto audience

The hard part is valuing what holders actually have. Hats sit in game accounts, while the token trades onchain. An inventory claim needs evidence of those holdings, control of the accounts and a clear explanation of how tokenholders benefit…

I don’t personally think this would accrue back to the holders as a claim on the in game assets… particularly if the OSRS accounts get banned for real world trading

However, the attention flywheell drives more virality which then brings more people into the token which then has a flywheel affect of more RH hats accumulated in game

Receipts: Your iPhone Purchase Could Earn AAPL stock

“Receipts” protocol has one of the easiest pitches in this entire issue: photograph your shopping receipt and receive tokenized stock exposure to the company you bought from:

Costco maps to COST, Apple to AAPL + Whole Foods to Amazon. The proposed rewards treasury is funded by a share of $RECEIPT trading fees. The named retailers aren’t funding or partnering with the project (yet) but its funded through trading fees currently; to me this is an ingenious “equity cashback” reward that i could easily see take off with shops aligning interests with their core customers:

Its published rules describe a 3% base reward, capped at $20 per receipt, rising to $100 for wallets holding at least 100,000 RECEIPT. Those are advertised settings that the team can change. Reward rules.

The current status needs clarification: the site promotes live payouts, but its footer says the scanner is in early access and stock delivery is not yet live. It also labels the reward feed as sample data. I’m covering the proposed model without presenting those examples as verified payments. Launch disclosure.

If it works, this is a straightforward way to introduce stock exposure through something people already do. The next proof is successful deliveries + enough recurring revenue to fund shoppers when meme trading slows… or select retailers partner officially and fund this through marketing spend??

The Wildcard: NOFEE

(Competing for the Trade)

NOFEE: A Vault for Your Trading Clan

Why copy a trader’s buys after the price has moved when you could participate in the portfolio they’re actually trading?

NOFEE’s Funds product on Base is designed around that idea:

• Investors deposit into a shared fund and receive shares.

• A manager trades through approved pools, while investors control their own exits.

• The manager cannot redirect someone else’s withdrawal to their own wallet

I could see this fitting into FOMO’s clans: a group pools capital behind its strongest trader, with everyone participating in the same portfolio. Here’s a concept design mockup:

The lead trader manages the positions, and members redeem their shares against the fund’s remaining assets, subject to fees. That redemption value moves with performance

It addresses a familiar copy-trading problem. Followers often arrive late, get different fills and miss exits. A shared fund executes for everyone already invested, so members participate through their shares without having to replicate each transaction. The portfolio still faces slippage and liquidity constraints.

That’s the NOFEE use case I’m interested in: letting a community back its best trader without giving that trader unrestricted withdrawal authority. FOMO would be a potential application; I haven’t confirmed an integration but this could be one of many applications of NOFEE specifically

To note: “Rug-proof” would overstate the protection. Trading losses, abuse of permitted trades and smart-contract failures remain possible. Fees also matter: the current interface lists a 3% withdrawal fee, plus manager fees, with some exits before 30 days costing 12.7% before gas

What I’m Watching

The strongest development this week is the range of things a meme can fund…

TAO distributions…. A collateralized PONS short…. RuneScape inventory and a shopper’s first tokenized stock reward in the form of cash back

The trends that’s becoming increasingly clear is that innoivation has once again returned onchain with the real world asset (RWA) connection enabling tangible value to be distributed through innovative mechanisms, catalyzed through “MemeFi”

I’m watching whether the evidence catches up with the ideas:

1. The Agentic Layer: I noted above that Pons top projects were AI integral… this will be the next unlock!

2. RWA enhancement infrastructure: Pre-IPO pricing optimisation, seamless consumer apps leveraging tokenized RWAs etc.

3. Rewards: assets delivered to holders, measured in token units as well as dollars

4. Perpetuals: visible collateral, position size, funding + completed buybacks

5. Treasuries: verifiable holdings + a clear benefit for the tokenholder

6. Consumer apps: real usage and a reward budget that survives quieter trading

7. Launchpads and terminals: repeat activity after the initial launch rush; assess whether the launchpad continues to have that “staying power” and becomes the chosen venue for tokens, raking in the insane fees for doing so

The easy mistake is to see an asset behind a meme and assume the token inherits all its value. That value still has to reach the holder through a distribution, redemption, buyback or another enforceable mechanism; which is partly what this week’s partner UMIA finance is intending to solve

I’m interested because these experiments give communities more to do than wait for the next buyer. Now we can start measuring whether those activities create enough value to keep people around… sustainability becoming a focal point is a lot more satisfying

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto, AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. I am commenting on the underlying activity of what I am observing in the market to keep my audience updated and informed.

Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: Umia Finance

2. What stock-paired memes actually are

3. The leading meme / stock pairs on Robinhood Chain

4. BONER/HIMS, AI/NVDA, MOO/MU, CHILL/NFLX, NUDES/SNAP + the new LIGMA/FIG wave

5. Why a float squeeze comes before a true short squeeze

6. How WallStreetBets could turn the experiment into a mainstream event

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Umia is building an operating stack for token-native ventures across issuance, treasury management, fundraising, decision markets + legal enforcement. Its own launch is the first live demonstration of that stack.

The public $UMIA auction went live and was oversubscribed:

As clarity emerges around token ownership rights these protocols are becoming increasingly important, which is why I will continue to offer my advisory services to the protocol & projects coming through their incubation pipeline; so stay tuned!

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

“MemeFi” Emerges on Robinhood Chain

Most memecoins trade against ETH, SOL or a stablecoin; but activity on Robinhood (RH) Chain has introduced a different structure:

• A meme can launch direclty against a tokenized stock such as NVDA, HIMS, MU or SNAP

• The stock token becomes the quoted asset inside the liquidity pool (LP)

• When someone buys the meme, routing can convert their ETH or USDG into the relevant Stock Token before completing the trade

• The pool accumulates the stock token as the meme accumulates attention

The Innovation:

This doesn’t sound novel, but there is a missing mechanism that has only been possible since the tokenization of stocks (equities + commodities) that enable the distribution of fundamental value to meme tokenholders

Trading fees can also be distributed in the paired stock or used to build a stock-heavy community treasury

This creates a new category that sits between memecoins, social trading + tokenized equities:

MemeFi turns speculative attention into a “distribution rail” for stocks

• The meme supplies the culture

• The stock supplies the financial identity

• The AMM joins both markets together

Robinhood now exposes its Stock Token metadata through the RHJ asset registry. After normalising that feed against the current Robinhood Chain stock-token directory, I found 216 distinct ticker/name rows

The Current Leaders

The strongest known stock-meme relationships now include:

Spreadsheet link: Here.

The leaders aren’t always driven by the funniest names.. the best pairings compress an existing company narrative into a character traders can immediately understand.

1. AI is the NVIDIA story… “Board Sit” i.e. owning enough stock collectively to have a seat at the Boardroom table. Wild right?!

2. MOO maps onto Micron’s MU ticker

3. SPACEHOOD is the SpaceX tribe

4. Send Nudes may be the cleanest brand match yet: a new $NUDES/SNAP pair built around Snapchat’s original internet identity

The Meta Is Already Splitting Into Different Models

1) BONER/HIMS: the float squeeze

BONER produced the first real stress test, outlined in my post this week:

BONER did not squeeze the hundreds of millions of HIMS shares trading on Wall Street, it squeezed the tiny number available on Robinhood Chain

But this activity is so absurd that it caught the attention of the HIMS community, so much so that their leading community X account wanted to interview me about the event:

The reason why this particular token pairing worked so well is because:

a) It’s absoloutely absurd

b) It’s driving this absurdity to an already strong HIMS community who can continue the narrative as an extension of its marketing efforts

2) AI/NVDA: the category leader

AI/NVDA remains the clearest stock-meme franchise.

Artificial Inu has become a persistent source of tokenized NVIDIA demand, liquidity + volume. AI currently trades tens of millions of dollars per day across its markets, while the tokenized NVDA supply has expanded beyond 60K units.

The recent oversized AI trade also exposed the other side of thin concentrated liquidity. One extreme order can tear through ranges, print an absurd wick and immediately reverse without changing the underlying value of NVIDIA.

This instance may have looked like a $20m fat finger into a memecoin bid:

However it turned out to be an incrrect Dexscreener valuation of the NVDA stock in the LP because of the limited liquidity when routing the AI purchase through that pairing:

The pool is a distribution engine, but it is also a fragile market structure.

3) MOO/MU: narrative rotation into semiconductors

MOO shows that the model can travel beyond NVIDIA.

The Micron-paired meme passed a reported $18M market cap after rising more than 330% in one day.

This is importatn because the underlying Stock token book remains small. CoinGecko recently showed only hundreds of circulating MU tokens even while the tokenized asset generated millions in daily volume.

The meme can therefore become one of the largest sources of demand for the RH onchains wrapper long before it matters to Micron’s trillion-dollar public equity market.

4) CHILL/NFLX: distribute the stock instead

CHILL uses a different model:

It’s not presented as a direct NFLX quote pair. Instead, 100% of creator fees are routed back to holders as tokenized Netflix stock.

That turns trading into a stock distribution mechanism rather than a float-capture mechanism.

The trader buys a meme. The system uses the resulting fees to turn part of that attention into NFLX ownership.

5) NUDES/SNAP: the brand-native launch

NUDES is the newest example.

The pair does not need a complicated explanation. Snapchat became culturally synonymous with disappearing private photos. “Send Nudes” is instantly legible as the meme attached to SNAP. Prime examples are coming out of its X account as it distributes continued comical content to grow its holder base:

That may sound unserious, but this is how attention markets work. A perfect name reduces the cost of explaining the trade

6) LIGMA/FIG, PHARMA/MRNA + NetNet/NET: presence is not leadership

LIGMA/FIG is the strongest new example. It launched as a direct Figma pair and quickly reached roughly $5M in meme market cap on about $4.5M of volume. Degens then reportedly followed Dylan Field’s public wallet trail through his CryptoPunks and sent the Figma founder LIGMA supply

I have not independently verified the transfer attribution, but the tactic matters: a community can now put its meme directly into a company principal’s wallet without asking permission.

Other Notable Mentions:

i) NetNet Capital is different again. It uses the same NET ticker and describes its instrument as backed by reserves on Robinhood Chain. I am treating it as reserve-linked to the Cloudflare lane, not claiming a direct NETNET/NET pool I cannot verify.

ii) PHARMA/MRNA is also a real direct pair, but at roughly $25K of liquidity and market cap it is a known match, not yet a durable leader.

iv) SaylorMoon: MSTR - The irony here in revenge trading against the digital gold advocate for the underperformance of the stock

Float Squeeze First. Short Squeeze Later.

The terms are already being blurred, so the sequence matters.

1) Stage one: the onchain float squeeze

Robinhood Stock Tokens are a separate, much smaller market from the underlying equities.

If a meme pool absorbs a large percentage of the Stock Tokens available onchain, conventional liquidity thins. The wrapper can then trade at a premium, especially when traditional markets are closed and new supply cannot arrive smoothly.

This is what happened with BONER/HIMS.

It is a local float squeeze. No short sellers are required.

2) Stage two: primary-market expansion

Only Robinhood’s authorised participants can subscribe for new Stock Tokens directly from RHJ. Developers and ordinary traders compose with the supply already circulating.

If the wrapper becomes scarce, an authorised participant may expand supply or arbitrage the price difference. That can reconnect the onchain price with the reference equity.

It does not mean every meme purchase automatically creates an identical market order for the underlying share. The Stock Token is a debt security providing economic exposure, and the issuer’s hedging process is not the same thing as transparent one-for-one spot settlement

Stage three: a genuine short squeeze

A true short squeeze happens in the underlying equity when its price rises far enough that short sellers are forced to buy shares to close positions.

That requires more than a scarce wrapper. It needs meaningful short interest, constrained borrow, sustained spot demand and often options activity that adds dealer hedging pressure.

The SEC’s review of GameStop identified the full combination: large price + volume moves, high short interest, frequent Reddit mentions and mainstream media coverage.

Robinhood Chain can create the first spark but it cannot manufacture all the remaining conditions by itself.

Could WallStreetBets Create A GME 2.0?

This is where the experiment gets interesting.

The official WallStreetBets X account has already noticed the stock-meme structure and amplified a GME-paired token with the line “i like the coin”; albeit at the first ideation of the memeFi narrative. It’s worth noting the development has ramped up since then, showing a stronger success rate than back in July.

The cultural bridge between Robinhood Chain and Reddit therefore already exists.

But the most plausible transmission path is:

onchain meme demand → wrapper float squeeze → visible price dislocation → viral screenshots → attention reaches the underlying stock + options → short covering only if the market structure supports it

The first three steps can happen with a few thousand Stock Tokens.

The final steps require enough real-world capital to move a public company, which is a much higher bar.

There is also a structural contradiction. Robinhood’s documentation says Stock Tokens cannot be offered or delivered to US persons. WallStreetBets may supply the global attention, memes + underlying equity demand, but most of its US audience cannot legally be the direct Stock Token buyer.

The likely role of WSB is therefore not simply “buy the wrapper”. It is to turn a local onchain event into a mainstream equity narrative.

If the chosen company already has high short interest, limited borrow, an active options chain + a strong grievance narrative, the wrapper squeeze could become the marketing event that precedes an actual short squeeze.

Without those conditions, the premium remains trapped onchain and collapses when supply or arbitrage returns.

Where The Puck Is Heading

The first generation of Robinhood Chain memes borrowed the platform’s brand.

The second generation is acquiring a financial identity through specific stocks.

The next phase will look more like competing social franchises:

1. Every culturally relevant stock gets a leading meme.

2. Communities compete to accumulate the largest share of the tokenized float.

3. Launchpads compete for stock-token inventory + creator fees.

4. Treasuries distribute Stock Tokens back to holders.

5. Public dashboards track float captured, stock rewards + performance versus the paired equity.

6. Reddit, X + trading communities decide which meme becomes the canonical character for each company.

The durable product is the coordination layer around the stock.

Robinhood has given crypto 216 financial objects. Memes are turning them into internet-native communities one ticker at a time.

Float squeeze first. Short squeeze only if the attention escapes the wrapper.

But the beauty is that this is onboarding the next generation into actually owning underlying equities with fundamental value while having a broader revolutionary, rebellious narrative

If you want to stay up to date I produce a daily roundup of the action taking place in the RWA meme barbell narrative on Robonhood, so drop me a follow and turn notifications on. Latest roundup available here:

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. I am commenting on the underlying activity of what I am observing in the market to keep my audience updated and informed.

Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: Umia Finance

2. Onchain is heating up: Robinhood Chain + Cyberleek

3. Agents consume 5x more intelligence + UR launches TAO Subnet 25

4. Robotics capital hits public + private markets

5. What I’m Watching

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Umia is building an operating stack for token-native ventures across issuance, treasury management, fundraising, decision markets + legal enforcement. Its own launch is the first live demonstration of that stack.

The public $UMIA auction opens Saturday 29 August at 8am ET (Pre-bid begins tomorrow) and closes Wednesday 2 September:

Bids are made in USDC on Base. Buyers set a budget and optional maximum price; bids fill while the clearing price stays below that ceiling. Unused USDC is refunded and the final clearing price becomes the onchain spot price.

The auction starts at $0.12 with no vesting on purchased tokens. The public receives 17.3M $UMIA, equal to 43% of the 40M launch supply and the largest single allocation. A separate 10M performance reserve only unlocks at price milestones

Full UMIA auction details FAQ

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

Onchain Is Heating Up

From Stock Tokens to Attention Markets; Robinhood Chain is one catalyst in a wider acceleration.

Tokenized equities are becoming programmable, internet attention is turning into permissionless markets and privacy networks are finding live demand. Open settlement + composable assets let each market form quickly.

DeFiLlama currently shows $739M in stablecoins, $137M in active RWAs, $1.84B of bridged TVL + $3.64B of DEX volume over the last seven days:

Artemis recently placed total TVL close to $1B (Morpho & Ethena dominating), although the exact number changes depending on which deposits and bridged assets are counted:

The app economics remain the most interesting part.

Apps generated $3M of fees and retained $510K of revenue over the latest 24 hours. The chain itself generated $85K of fees and $75K of revenue. That leaves the application layer earning nearly 7x more revenue than the underlying chain.

Early activity came from memes, launchpads + farming… but the next stage is already emerging as developers find more uses for Robinhood’s Stock Tokens.

Stock Tokens are standard ERC-20s with live Chainlink price feeds. This means any developer can plug them into a launchpad, rewards system, lending market, trading venue or agentic fund using familiar EVM infrastructure.

RobinHood Chain Apps Leading this Charge

1) Pons: token issuance meets tokenized stocks

Pons became Robinhood Chain’s breakout launchpad during its first month.

At its peak in late July, it processed $99.7M of daily volume, 1.3M transactions and 11,200 token deployments. Across seven days it produced $7.86M in gross fees on $826M of volume, several times more than the chain collected in gas fees.

Pons V2 now uses an ETH-denominated bonding curve + Uniswap v4 liquidity after graduation. Creators can launch against custom quote assets including USDG and tokenized NVDA, AAPL or HOOD, with creator fees paid in the selected asset.

A community can launch a token directly against a stock, accumulate creator revenue in that stock and carry the same pair into permanently locked Uniswap liquidity. Tokenized equities become productive building blocks inside crypto-native markets.

Launchpad revenue is reflexive: volume, fees + burns can fall together when attention moves. Pons needs V2’s stock-paired markets to sustain activity outside the initial launch cycle, but its recent uptick suggests we are about to see a second wind:

2) Index: turning speculation into stock ownership

Every $INDEX trade carries a 3% fee. The protocol uses that fee to buy a basket of tokenized stocks and distributes them directly to holders every 15 minutes. There is no staking or claim process.

The protocol says it has already distributed nearly $1.1M of Stock Tokens, which marrys up to the growth we are seeing in wallets holding equity tokens on RH chain:

Its next phase extends the mechanism beyond $INDEX. A project can launch through its preferred launchpad or connect an existing token, then route activity into real equities for holders. Index becomes a value-accrual layer that other tokens can plug into.

This creates a simple bridge between speculative demand + durable assets:

token activity → protocol fees → Stock Token purchases → holder distributions

The mechanism depends on trading volume, treasury execution + contract security. Rewards fall quickly when activity slows, but in the meantime it’s clearly garnering a lot of attention:

3) Prism Assets: making the market legible

Prism sits at the discovery + verification layer, tracking 1,196 verified assets from 36 issuers across 17 networks and $13B in value; the current market cap seems low assuming the discovery strategy layer plays out:

It verifies contracts against explorers + issuer documents, checks prices independently and simulates transfers for restrictions. Its latest run covered 128 deployments: 99 freely transferable, 16 permissioned and 13 inconclusive.

That catalogue is useful for agents that need verified contracts, machine-readable restrictions + execution routes. I’m watching repeat usage, issuer diversity and whether the 5M $PRISM creator pool translates into durable product demand.

4) The Broader stack

Three other projects show where Robinhood Chain can go next:

• Arrow is building the credit layer. Users can deposit crypto, Stock Tokens or ETFs as collateral and mint aUSD instead of selling. Its dedicated Stock Token oracle accounts for closed-market conditions, which is essential when 24/7 collateral references assets with traditional trading hours. The blow off top earlier suggests the hype is out of the token following the initial RH chain launch:

• StonkBrokers is expanding from stock-owning NFT wallets into the Stonk Exchange, a community-directed vDEX scheduled for 29 August. Mancer, DERP + Tickeryard extend the ecosystem into signed-order execution, routing + cross-chain access with more anticipated through its freshly updated launchpad:

• Sherwood is building agentic funds as non-custodial vaults. Agents publish exact execution calldata before acting; depositors can vote proposals down and staked guardians simulate the calls before settlement. Fund performance is then ranked onchain.

Together these products cover issuance, ownership, verification, trading, credit + automated capital management. Robinhood supplied the chain, Stock Tokens + distribution; app usage now decides whether the activity becomes durable.

Broader Onchain Insights:

1) Cyberleek: GTA VI attention moved onchain

Cyberleek’s apparent GTA VI leaks show how quickly internet attention can become an onchain market with speculators making 6 figures ($500k at peak):

Bitquery reconstructed the sequence:

• an Arweave name was purchased on 14 August

• $CYBERLEEK began trading on Solana on 15 August

• footage appeared on 18 August carrying QR codes for the site + token

• The first leak-driven hour produced a 13x move on $1.47M of volume

• The operator and provenance remain unverified

• Take-Two’s requests to subpoena Microsoft + Discord were approved, although Discord said it had not yet been served

• Cyberleek has since offered advertiser contact through Session for a 400 XMR (Monero) fee worth roughly $165K

Monero provides transaction-level privacy by hiding the sender, recipient + amount

The activity is speculative and legally exposed, but the distribution pattern still matters: attention, storage, payments + liquidity moved across open crypto rails within hours.

Many are saying this attention capital market will come to a head in a few days as the Netflix GTA 6 show is set to premiere:

2) Agents Consume 5x More Intelligence

AI agents now consume roughly 7.3T tokens a day on OpenRouter, around 5x human volume. Agentic usage has increased 14x in six months, versus 2.8x for humans:

Long-running, multi-step workflows concentrate demand around three rails: model routing, machine payments + verifiable assets. Stripe’s reported $8B OpenRouter acquisition targets the first; MPP + x402 compete around the second; Robinhood Chain sits inside the third.

Binance added Agent OS, letting agents access market data, monitor accounts + trade through controlled subaccounts. Robinhood is approaching from both ends: agentic brokerage accounts above the chain and permissionless assets below it.

3) UR brings live demand to Bittensor (TAO) SN25

UR launched as Bittensor Subnet 25 with 350K+ monthly users, 100K+ residential providers + coverage across 100+ countries.

ur.io is the first demand-side operator. Miners supply encrypted, routable IP coverage; validators walk routes and score demand, reliability + unique coverage before Yuma Consensus directs emissions.

The result is a measurable market for residential internet access—useful for privacy users today and agents as websites restrict automated traffic. UR enters with active product demand; SN25 now has to prove operator payments can support useful miner economics as coverage scales.

Robotics Capital Hits Public + Private Markets

Robotics funding accelerated across bodies + brains this week:

i) XPeng’s robotics unit raised more than $900M at a valuation above $6.3B, with a target of 1,000 IRON humanoids per month by year-end. Generalist raised another ~$200M only two months after a $400M round to scale its robotics foundation models.

ii) Unitree carried that excitement into public markets. Its $905M Shanghai IPO priced the company around $9B; the shares finished 460% higher on debut, briefly valuing it at $66B, before falling roughly 45% from the peak. Speculators were trading the Unitree market pre-IPO onchain via tradeXYZ/Hyperliquid:

The demand signal is enormous, but so is the gap between capital + commercial reality: Unitree’s adjusted profit fell 53% in Q1 and broad deployments remain limited.

Crypto robotics is attacking one constraint money alone cannot solve: diverse training data + open evaluation.

iii) Axis Robotics has built a network of 100K+ contributors and 3M+ multimodal trajectories. Its OpenRoboto partnership feeds that data into Bittensor SN80, where miners compete to improve a shared model and stronger submissions replace the benchmark.

iv) BitRobot coordinates a wider open lab through mission-specific subnets spanning rover teleoperation, egocentric video + simulated manipulation. TeleArms generated 3,000+ hours of behavioural data, while SN04 connects Axis to the same network.

The stack is becoming clear: capital funds bodies + models; distributed networks collect data; open competitions evaluate improvements; deployments prove economic value.

I’m watching shipped units, productive hours, intervention rates + model improvement per dollar. These will separate robotics infrastructure from robotics content.

What I’m Watching

1. Pons: whether stock-paired V2 launches sustain volume after the initial launchpad cycle.

2. Index: the adoption of its plug-in stock-reward layer by tokens outside $INDEX.

3. Stonk Exchange: execution quality, liquidity + the effect on the wider StonkBrokers economy after 29 August.

4. Prism: repeat users, verified coverage + the link between trading activity and $PRISM.

5. Agent infrastructure: whether OpenRouter’s 5x ratio persists and UR converts existing demand into durable miner economics on SN25.

6. Robotics: Unitree + XPeng deployments, and whether Axis + BitRobot data produces measurable model gains with a few methods of getting involved now:

7. [Alpha] Standard Reserve: This one caught my attention from 0xBeans which will be going live on RH Chain soon, so worth paying attention to:

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: Axis Robotics

2. Stripe + OpenRouter + MPP

3. Venice: the numbers I’m watching

4. Simulated Agentic Economies (RuneScape)

5. What I’m Watching

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Axis Robotics returns as Snippet Partner this week with another meaningful step forward for its robotics data stack

Axis has partnered with OpenRoboto, Bittensor Subnet 80, which runs an open competition for continuously improving robotics models

Miners fine-tune a shared base model, submissions are tested across randomised LIBERO-Pro environments and only models that outperform the existing base become the new benchmark

Axis is providing the data layer underneath that process, contributing 3M+ multimodal trajectories into OpenRoboto’s Open Data Pool while also supporting model evaluation through its Data-to-Model Pipeline

The combination is interesting because it closes more of the robotics training loop:

distributed data collection → model training → verifiable evaluation → improved base model

I featured Axis as Snippet Partner recently around its $12m seed raise + Booster partnership. This latest integration pushes the thesis a step further by showing where that data can actually be consumed: directly inside an open market competing to improve physical AI models

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

Stripe is Buying the AI Routing Layer

One of the biggest AI developments this week came from Stripe, which has reportedly agreed to acquire OpenRouter for more than $8bn in cash + stock. OpenRouter was valued at $1.3bn as recently as May; so it’s up 6x in a few months from the latest valuation if this deal goes through!

But it’s the strategic rationale that’s more interesting than the headline valuation

OpenRouter sits between applications and AI models, giving developers one API through which they can access and route between hundreds of different models

If Kimi is cheaper for one task, Claude better for another and an open Qwen model works perfectly well for something else, the application doesn’t need to rebuild its stack around each provider:

That position becomes increasingly valuable if the model market continues to fragment, which is exactly what I covered a few weeks ago when open-weight intelligence started to accelerate

More models → more choice → more routing

Stripe now potentially owns a large chunk of that routing layer while already sitting underneath an enormous amount of internet commerce

That combination is particularly relevant because Stripe has simultaneously been building out the payment infrastructure for agents

MPP starts to make more sense

Earlier this year Stripe + Tempo co-authored the ‘Machine Payments Protocol’ (MPP), an open protocol that lets an agent pay for an API or service programmatically without going through a traditional checkout flow:

The flow is relatively straightforward:

An agent requests a service → receives an HTTP 402 payment request → authorises payment → retries the request → receives the service + receipt

MPP currently supports both traditional card rails + stablecoins, with Stripe supporting stablecoin machine payments down to $0.01 USDC.

Tempo has also introduced MPP “Sessions” for higher frequency use cases, where an agent can make potentially thousands of small offchain payments during a session and settle the final balance using only two onchain transactions… a bit like a bar tab

I think the OpenRouter acquisition makes this direction much clearer

• OpenRouter controls a valuable interface between applications and models

• Stripe controls payments

• MPP gives software a standard way to pay

• Tempo provides a blockchain specifically designed to settle those payments

You can therefore start to see how an agent could dynamically select an appropriate model, pay for the inference required, call another paid API and settle everything programmatically without needing a human to create accounts + enter card details throughout the process

This is much closer to the agent economy I’ve been talking about for the last few years than another chatbot with a token attached to it

The Venice Numbers

The Stripe x Openrouter target acquisition has close ties to what Venice is building, & as such I spent some time reviewing Venice’s forward financials this week following the announcement they’d just hit $100m ARR:

There’s a lot to like in the growth forecast, but one number immediately jumped out at me; the benchmark of projected revenue increasing at a lesser rate than the projected burns of the VVV token:

So the value of annual burns rises from roughly 8% of revenue today to around 21% by 2027; that’s a MUCH more aggressive increase than the underlying revenue forecast

The important number I’d therefore want to see isn’t simply revenue, it’s:

Cash revenue − inference COGS − cost of servicing DIEM/subscription entitlements − OPEX = sustainable cash available to buy VVV

Why?

Because Venice has created ongoing service obligations alongside the token economics

$100m of revenue doesn’t mean much to a tokenholder if most of that revenue is subsequently required to provide inference, service existing entitlements + run the business

The buyback ultimately needs to come from the cash left after those obligations; credit to venice in that they are demonstrating an uptrend in buybacks, but they do remain discretionary:

If that number supports anything close to $70m of recurring annual purchases then the bull case becomes VERY compelling

If it doesn’t, I’d be careful valuing VVV off the headline burn forecast; this is increasingly how I’m looking at protocols with buyback mechanics generally

The question then shifts to:

“how much of that revenue can sustainably reach the token after the actual cost of providing the service”

That’s the number worth underwriting + monitoring closely

One Other Thing: Agent Economies

One of the more random things I looked at this week was an agent-only Old School RuneScape server…I’m a RuneScape nerd so naturally this caught my attention:

The interesting bit wasn’t whether the agents were good at RuneScape, but what started happening to the economy once software became responsible for gathering resources + trading them

Resources that agents could repeatedly produce became increasingly abundant, while scarcer items retained more economic significance (runite ore & black dragonhide)

There are obvious limits to using a game economy as a proxy for the real world, but the direction is worth thinking about

If AI materially reduces the cost of producing certain digital goods + services then value should migrate towards the inputs that remain scarce:

• Compute

• Energy

• Proprietary data

• Capital

• Distribution

• Physical resources

• Ownership of productive assets

It’s one reason I continue to spend so much time looking at the intersection of crypto + AI rather than AI agents in isolation

• Crypto gives software direct access to capital + scarce assets

• MPP/x402 now gives those same agents increasingly native ways to spend

• blockchains give them access to financial assets, including tokenized stocks, bonds and other financial instruments

• Bittensor (+ other less established intelligence netowkrs) coordinates markets for digital commodities

That’s a much more useful direction than where the “AI agent” narrative started in late 2024 - There’s still a lot of rubbish to filter through, but the underlying infrastructure is becoming materially better

What I’m Watching

The main things I’ll be watching over the next week:

1. OpenRouter: what Stripe actually does with it once the acquisition closes, particularly around MPP (inc x402) + agentic payments

2. Robinhood Chain: whether all this experimentation starts translating into sticky TVL, fees + stock-token utilisation… the past week has seen a drop off in activity:

   

   …my bet is that there’s A LOT more to come:

3. Venice: more clarity around the bridge between forecast revenue and the actual sustainable cash available for VVV purchases

4. … and finally for those that have read to the end, check TAO subnet 25 (UR network) going live on Bittensor tomorrow with an already deep user base outside of crypto. The metric to watch closely for UR SN25: how quickly existing UR supply + demand translates into actual subnet activity after launch

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: Axis Robotics

2. TLDR on the Stonkbrokers Ecosystem

3. The opportunity

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Humanoid robotics just received its clearest public-market validation yet: Unitree’s $900 million Shanghai IPO was more than 8,000x oversubscribed by retail investors, leaving applicants with roughly a 0.018% chance of securing shares

Capital has woken up to the robotics opportunity. The next race is producing the data required to make these machines genuinely useful

Axis Robotics is building that data layer. The company recently raised a $12 million seed round led by Hack VC to scale its compounding data engine, combining browser-based simulation, mobile egocentric capture and human correction to produce structured training data for Physical AI

Its latest commercial partnership with Booster Robotics connects this engine directly to humanoid hardware:

Real-robot teleoperation demonstrations can be multiplied across simulated tasks and environments, accelerating sim-real co-training and the development of deployable robot policies

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

The Stonkbrokers Ecosystem Explodes

If you’ve been following my X account you’ll have been kept up to date in real time on the ecosystem developments

I was correct in last week’s newsletter that Robinhood chain is catching its second wind, so much so that it’s dominating the EVM chains fees:

The catalyst?

Stonkbrokers innovative RWA distribution mechanism. If you’re unsure about the Stonkbrokers is then this diagram I pulled together will help you understand the stack:

The Opportunity: Evolving underlying tech primitives

The opportunity is that this is beginning to mirror the early signs of the agentic boom that began to take off in Q4 2024, except this time for Real World Assets (“RWAs”). Specifically in this instance tokenized equities

The core mechanism that has captured this attention is associated with the real tokenized stocks distribution mechanism into wallets (ERC 6551) held by the “activated” Stonkbroker NFTs

Sounds complex? possibly.

But this is an intricately designed value accrual mechanism that is demonstrating early signs of product market fit.

Essentially, fees from trading volume or benefits from incubating projects in the stonkbroker ecosystem are used to buy NVDIA, APPL + other real stocks onchain and distribute to tokenholders

Don’t get me wong there’s clearly a lot that could break between now and broader adoption (reg risk, liquidity returning etc), but the fact that this mechanism is being experimented with is what matters; this is becoming a primitive that AS A MINIMUM will be built on to push forward how blockchain is used in traditional markets.

Where do I think this is going?

Well, we have a lot more tech available to us today than we did two years ago. I’m a strong believer that agents will be plugged into this ecosystem to further bolster its capabilities. Here are seven (non-exhaustive) examples:

Agents that help optimise:

1. Cross-chain completion

2. Backing + issuer watchtowers

3. Mancer execution

4. Stonk protocol maintenance

5. Treasury + recovery operations

6. User-facing portfolio agents

7. Agents hiring specialist agents

Cool, these are great but how do we benefit from the upside here?

So the opportunity as a builder is to take a look at this diagram, assess what’s missing and start developing something that plugs that gap:

If you’re a trader or broader market participant then there’s numerous opportunities with projects deploying into the ecosystem weekly, so pay attention and if they launch at a time where you’re at your laptop try and mint before it hits secondary

Tickeryard was the latest to go live:

The NFT minted out in no time at a $200k initial market cap (no whitelist):

It is now trading between $4m-$5m market cap, so a 20-25x for those paying attention:

This is happening to all incubated projects (Mancer, DERF, YARD etc), with Stonbrokers (by Simple Farmer + Clutch Markets) still leading the ceiling at $54m market cap with a very strong chart backed by the above mentioned value accrual fundamentals:

What Next?

If Vlad (CEO, Robinhood) starts to support the Stonkbrokers ecosystem then this could become a king made protocol that feeds the next RWA boom through Robinhood chain

If the regulators step in (very real risk) then this takes a major step back and unfortunately this onchain experimentation is squashed before it has a chance to get off the ground

I don’t think it’s big enough to be on the regulators radar, yet.

If the former happens then any project going through the incubation program for Stonkbrokers could do very well given it has a strong vetting process (where the value is)

So in my view, there’s still some more upside before we may hit the first major hurdles and I’ll be watching closely to assess how this plays out; follow my X account to stay abreast of it all in real time

If you’re building innovative things in the ecosystem, let me know by dropping me a direct message here or on X and I’ll contribute where I can

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of the following:

1. Snippet Partner: Umia Finance

2. What Robinhood Chain actually is

3. Ten projects ranked by live usage

4. The PONS, LONG(), Stonkbroker, GIGA, Lighter opportunities

5. The mainnet launch calendar

6. A practical participation guide

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

This week’s issue is brought to you by Umia Finance, the operating stack for token-native ventures.

Umia is taking its venture-creation stack live on testnet, beginning with project discovery and onboarding its founding cohort. Founders can submit a GitHub repository through Umia Score, verify ownership and establish an initial valuation signal before launching:

The full stack combines a legal wrapper, token, non-custodial treasury and community fundraising through Uniswap v4 Continuous Clearing Auctions. Once live, major treasury decisions are resolved through decision markets, while Umia’s MetaLeX-powered legal structure gives those outcomes real authority.

The timing matters for agentic ventures. AI lets small teams build products quickly; Umia compresses the capital formation, ownership and governance required to turn them into investable businesses.

Explore the testnet or read how the Umia stack works.

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

Robinhood’s Onchain App Store Is Open (Meme - RWA Barbell + agents)

Over the latest 24 hours, users paid Robinhood Chain applications $1.6M in gross fees, retaining $226K as protocol revenue, while Robinhood Chain kept $50K:

Put simply: the app layer collected 32x the chain’s revenue in gross fees and retained 5x the chain’s revenue for itself.

• The 32x figure measures what users paid to apps

• The 5x figure compares what applications and the chain actually retained

This supports a “fat apps, thin chain” thesis, meaning that Robinhood provides inexpensive settlement while applications capture most of the economics through trading, lending, launches & agentic services

That makes Robinhood Chain attractive for deployment and why over the next few months you can expect a flurry of activity, with opportunities for those paying attention

To note: Robinhood Chain has no native token, so the investable surface sits across exchanges, launchpads, lending markets & agentic applications

Fees are what users pay, protocol revenue is what an application keeps and holder revenue (arguably the most focused on metric given the lack of trust in the market currently) is what reaches a token through distributions, buybacks or burns

What Robinhood Chain actually is

Robinhood is a permissionless, EVM-compatible Ethereum Layer 2 built with Arbitrum’s Nitro tech stack. It settles directly to Ethereum, uses ETH for gas and runs on chain ID 4663… technical jargon for an ETH aligned layer 2

The chain now holds $411M of DeFi TVL, $558M of stablecoins, $101M of active RWAs and more than $1B of bridged assets. Seven-day DEX volume reached almost $3B; perps reached $228M, up 130%.

The Meme - RWA barbell

Memes acquire users, stocks anchor value and agents automate the portfolio between them. Vlad (Co-Founder and CEO, RH) has doubled down on this meme-RWA barbell thesis:

Pure memes like CashCat have captured decent attention with artists like Beeple posting about it:

But as is the case with any pure attention based speculation instrument it’ll be volatile:

… and it’s actually the interesting mechanisms that lean into the robinhood thesis that have really piqued my attention; namely Stonkbrokers and LONG() with plenty more fresh bouts of innovation shining through with tokenholders at the heart of that value accrual (refreshing I know!)

So how does the landscape look today, and what opportunities exist in the not too distant pipeline? I’ll explain below:

1) PONS: the strongest small-cap fee anomaly

Ponsinomics (nice name) measures protocol receipts from collector-wallet transfers and launch events. The latest week produced $570K… annualised, that’s $30M against a $20M market cap: which equals 68% of annualised receipts.

When you strip out the two gamified mining tokens (Minepea + SLVR), Pons is the leader in returning revenue to holders:

On 25 July, Pons handled 64% of launchpad volume and 64% of token deployments across the chain:

Current policy directs roughly 80% of receipts into TWAP buybacks and burns. The weekly pace implies $24M of annual buying, equal to 118% of market cap. Burns have removed 24% of supply.

Pons V2 is now live with Uniswap V4 and stock-token quote pairs:

If they continue with the 80% fee value accrual to Pons token holders then this is a lucrative asset to watch provided launch volume sustains

2) LONG(): the meme-stock barbell

On 25 July, Artemis measured meme pairs at $297M, or 75% of daily DEX volume. Stock-token pairs were $36M, or 9%. It’s clear these are making a difference to stock trading onchain also:

LONG() links those markets: token such as AI uses NVDA as its quote asset. Every swap creates NVDA turnover and the pool holds NVDA liquidity. The token’s dollar price becomes AI/NVDA multiplied by NVDA/USD, creating two sources of volatility.

AI/NVDA currently has a roughly $2M market cap, $610K liquidity, $629K daily volume and 6,100 holders. Daily volume equals 36% of market cap. LONG() reported $32M of cumulative stock-token volume on 22 July and has since announced Index Expansion into sector baskets:

Bankr adds natural-language launches and trading. Its creator-fee model explicitly lets agents fund their own compute. This creates a loop: memes distribute, stocks build treasury assets and agents deploy the proceeds.

The AI/NVDA pool carries a 48/100 security score and uses a proxy contract, so contract risk sits beside the two-factor market risk.

3) StonkBrokers: the NFT is the stock wallet

LONG() puts the meme and the stock in the same liquidity pool, while StonkBrokers puts the stock inside the meme… this one is novel and worth paying attention to!!

The collection contains 4,444 broker NFTs; each ERC-721 controls an ERC-6551 token-bound account seeded with tokenised stock at mint. The wallet and its stock balances travel with the NFT when it changes hands; the new owner can reactivate it for future distributions. Each broker therefore functions as a transferable onchain portfolio… pretty non-fungible and could move in line with the underlying portfolio?

The Anvil AMM creates the cash-flow loop:

• A broker costs 666,666 STONKBROKER plus a native ETH fee.

• The fee is 10% for a standard swap and 15% for a specific NFT.

• 70% of that ETH funds StockBooster; 30% goes to the protocol.

• Any wallet can then “Clock In”, converting the accumulated ETH into the stock tokens selected by activated broker holders.

Activation creates the token sink. Holders pay between 66,666 and 1,666,666 STONKBROKER for higher distribution weight. At the captured token price, that is roughly $1K to $27K, driving positive price acction to the fungible token also (+8%):

Half of each activation payment burns and half goes to the protocol. A genuine NFT transfer clears the activation, so an active secondary market creates repeat demand for STONKBROKER.

On 4 August, the OpenSea collection showed a 6 ETH floor, 176 ETH of daily NFT volume and 1,116 ETH cumulatively.

Floor multiplied by supply gives a $47M headline valuation. Liquidity behind that mark is thin: only 57 NFTs, or 1% of supply, were listed, while 557 wallets held the collection. Holders equal 13% of supply and the average wallet owns eight brokers.

This is the Stonkbroker flywheel:

Broker Box extends the idea into stock-token distribution. Nine mainnet machines cover GME, AAPL, AMZN, NVDA, GOOGL, MSFT, SLV, SPCX and USO:

• The Certificate Counter swaps ETH into stock and seals it inside another ERC-6551 bearer NFT.

• Each purchase charges $2, split evenly between StockBooster and the treasury.

• Degen Mode adds a 90% design RTP and stock payouts ranging from 70% of stake to 50x; instant cash-out retains a 5% spread.

• This is a gamified acquisition funnel for tokenised stocks with every prize backed by inventory onchain.

The metric worth tracking is stock-token distributions per activated broker.

A dashboard showing Clock In volume, stocks purchased, average payout, active brokers and activation burns would make the productive value visible beside the NFT floor.

The project’s Stonk Exchange, pools and covered calls are scheduled for 29 August, creating the next test: whether the same fee-recycling model can extend beyond NFT trading with options involved also:

4) GIGA: what it is and why it matters

GIGA is a Robinhood-native DEX and liquidity coordination system; it supports stable, volatile and concentrated pools. At Genesis, its controller will redirect emissions every hour toward pools producing the most revenue. This replaces weekly gauge voting and bribes with an automated signal.

The current phase already produced $10M of weekly volume from $522K of TVL. LPs keep 80% of swap fees; the protocol kept $4K from $20K total. Points reward the busiest pools, so part of that volume is farming.

The six-week points program is redeemable for 15M GIGA from a fixed 1B supply. GIGA Genesis is scheduled for 31 August, when the custom emissions system activates.

Two diligence flags matter. The custom controller remains team-controlled and under audit. The official tokenomics page assigns 190M GIGA, or 19%, to the GIGA Machine; the Machine page states 200M, or 20%. That allocation needs a single answer.

5) Lighter: Perps

Lighter is multi-chain but it’s hard to ignore when Perp volume is hitting new all time highs daily:

If the protocol solidifies itself as the number one venue on robinhood chain then we could see some serious competition against the king of perps; Hyperliquid.

It’s pretty clear now that users want to trade tokenized equities and commodities onchain, so offering venues to speculate on derivatives of tangible assets with decent user experiences is becoming increasingly appealing

Where headline activity misleads

• UP trades at a $30M FDV. It generated $14M of weekly volume and $27K of holder revenue, then distributed $519K of incentives. Weekly earnings were -$492K.

• Sushi routed $9M through $216K of TVL and generated $357K of fees. Holders received $5K, or 1%; likely because its more established with greater overheads + a hike in marketing expense from a pretty decent video:

• NOXA generated $872K of fees with $0 protocol and holder revenue.

• RamsesX has a $649K market cap and $452K TVL, with $0 tracked volume or fees.

Fee screenshots show activity but it’s the token value depends on the cash-flow path. So when you’re looking at the DeFi llama fee flow diagrams in search of investible tokens, check the flows back to token holders like for Pons ($2.18m of the $4m):

The mainnet launch calendar

How I would participate

1. Add the network from official docs. Chain ID 4663, ETH gas, official RPC and Blockscout.

2. Bridge a test amount. The canonical bridge takes about ten minutes in and seven days out. Relay and Across provide faster routes.

3. Establish the base layer. Make a small Uniswap swap, inspect Morpho markets and try Arcus (dydx team) spot if eligible.

4. Separate experiments. Use small amounts for Pons V2, LONG()/Bankr and GIGA points. Run Sherwood and Veil on testnet. Keep experimental contracts in a dedicated wallet.

5. Track the same columns weekly. Volume, TVL, protocol revenue, holder revenue, incentives and market cap.

• DeFiLlama ranks apps;

• Entropy Advisors shows network economics, RWAs and Morpho in one dashboard; Ponsinomics covers collector flows;

• Blockscout verifies contracts and transactions.

Robinhood has announced no chain token or chain airdrop. Stock Tokens are tokenised debt securities providing economic exposure to shares

Also set up an agentic trading account and play around with it:

Robinhood Chain’s first month belongs to:

• Uniswap for trading,

• Morpho for deposits, and;

• Pons for token-level value capture.

The deeper opportunity is the meme-stock-agent loop:

• LONG() & StonkBroker have created the pairing model.

• Pons and Bankr are distributing it.

• GIGA is competing to coordinate the liquidity alongside Uniswap.

• Sherwood is building the agent-managed capital layer.

Continually track these (and other emerging) projects for activity and position accordingly as attention and activity grows.

It may even pay to establish what Robinhood typically sets as its core exchange listing requirements in the hopes they start running some of their own native tokens through to their user base:

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of three key parts:

1. Snippet Partner: Axis Robotics

2. Theme of the Week: Intelligence Is Becoming an Open Commodity

3. Landscape Analysis: TAO (Macrocosmos, Chutes, Targon, Engy AI), TIG, Walrus + more

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

This week’s issue is brought to you by Axis Robotics, who just raised a $12 million seed round

Language models could learn from an internet already filled with text, images & video. But robots don’t have the same luxury; physical AI needs billions of motion trajectories across different objects, environments & robot bodies

Axis is buildin a compounding data engine around this bottleneck; Its browser-based simulation platform allows distributed contributors to teleoperate robots and generate training trajectories, while its mobile capture product converts human movement into real-world egocentric data

It just revealed its roadmap for the next 12 months:

As traditional robotics labs begin to IPO we could see eye watering capital raises feed what is going to become the largest revamp of the global labour force

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

Intelligence Is Becoming an “Open” Commodity

First Jensen Huang, now Sam Altman & Dario Amodei (kind of) came out in support of open weights (open source AI models)

Within a few days NVIDIA argued that open-weight models are strategic infra for US AI leadership:

Anthropic described capable, but non-dangerous open weights as a “public good” & Moonshot released the largest open-weight model to date

These aren’t fringe open-source advocates:

• NVIDIA sells the infrastructure beneath the entire AI economy, and;

• Anthropic operates one of its most valuable closed model businesses

Both now agree that broader access to model weights increases competition, gives customers greater control and reduces dependence on a single AI lab

The significance for crypto AI is what I believe is underestimated…

If intelligence becomes easier to download, adapt and deploy, value moves toward the scarce inputs required to run and improve it: compute, inference, proprietary data, memory, verification, coordination + payments

Open weights become a strategic issue

NVIDIA’s open-weights paper argues that AI leadership should be measured by whether an ecosystem can diffuse intelligence across startups, universities, factories, hospitals and public institutions

Open weights let organisations inspect a model, fine-tune it and run it on the infra it controls. That makes AI cheaper for many tasks & prevents the knowledge built on top from becoming trapped inside a single provider

Dario Amodei’s response was more cautious on safety, but agreed (albeit hesitantly) with the core economic point:

“Open weights expand access to the AI economy, they strengthen competition … and they give customers greater control.”

Anthropic still wants mandatory testing for sufficiently capable models and targeted action against industrial-scale distillation

Even the leading closed-model lab now accepts that open weights create genuine public and economic value

Kimi K3 made the argument tangible

Moonshot released a 2.8T parameter open-weight model… essentially making a competitive frontier model from Asia a public good:

Frontier labs still retain distribution, safety infrastructure, proprietary data & the ability to spend billions training the next generation

Builders will be able to choose between closed frontier models, open general models and smaller specialised models depending on cost, latency, privacy and control turning model access from a permanent moat into a much more competitive market

NVIDIA understands the flywheel

You’d be mistaken if you believed NVIDIA was supporting open weights as an act of charity…

It expands the number of organisations able to deploy AI on their own infrastructure meaning A LOT more customers to supply compute to:

Cheaper models create more applications, more applications create more inference, more inference creates demand for GPUs, memory, networking & power:

Open weights also make that demand portable, so a model can move between hyperscalers, sovereign clouds, local hardware + decentralised compute networks rather than remaining tied to one API

This is where crypto AI becomes relevant… it coordinates the fragmented markets forming around it, and open intelligence needs open, premissionless infrastructure:

Landscape Analysis: Bittensor, TIG and the Open Intelligence Stack

i) Bittensor (TAO) is commercialising open intelligence

Until recently, the crypto AI response to open weights has been theoretical: decentralised compute and data networks would become useful once high-quality models could move freely between providers

Bittensor has spent the past few years building specialised markets beneath it.. the TAO eco is an incentive framework where subnets define a digital commodity, miners produce it, validators score the output and TAO rewards the contributors judged to be creating value

For those wanting a crash course in how TAO came to be, we’ve reported on the ecosystems investible history here:

Open weights make this structure much more important because training, serving, data collection, verification and privacy can each become competitive markets. We can touch on several eco examples:

a) Training: Macrocosmos IOTA, Subnet 9

Macrocosmos has now started Orion-16B, a single 16 billion parameter model training live across heterogeneous GPUs on three continents.

IOTA splits model layers across permissionless contributors, so no miner holds the full model and no operator controls the run

b) Inference: Chutes, Subnet 64

Open weights are only economically useful if somebody can serve them cheaply and reliably

Chutes turns distributed GPUs into a serverless inference platform where developers can deploy open models behind an API without managing the underlying fleet

The important shift is that Chutes is now being measured on revenue quality rather than subsidised token volume with revenue efficiency improving:

After removing loss-making traffic and tightening its subscription model, Chutes reported revenue per million tokens increasing 38% + revenue per active GPU increasing 45% from the start of February

Revenue has reached roughly $280k per trillion tokens served (picture above), with paying applications including Pax Historia running production workloads showing commercial viability

c) Confidential compute: Targon, Subnet 4

Portability creates a trust problem… Enterprises want cheaper distributed compute without exposing models, data or prompts to unknown operators.

Targon uses confidential VMs, host verification and NVIDIA nvTrust attestation to prove workloads ran inside approved protections without trusting the server owner.

Its new Tower lets anyone connect and rent compute through the Targon network.

d) Other emerging TAO subnets:

Algod recently launched a subnet (Engy AI) for verifiable inference, competing with the likes of Chutes:

ii) Algorithms become a licensable asset

The Innovation Game (TIG) is another expression of the open-intelligence thesis

Innovators submit or optimise algorithms, benchmarkers compete to identify the most efficient methods, and contributor rewards increase as their algorithms are adopted

TIG’s licensing framework is the most interesting part of the mechanism, which I covered in this recent article:

TIG offers two licences:

1. free use if distributed code and relevant data stay open, or

2. paid use in TIG to keep data, implementation and improvements proprietary.

Innovators submit methods, benchmarkers price performance, open users grow the commons and commercial fees fund the next cycle.

The test is whether licence demand can replace emissions. Benchmarks prove usefulness; paying customers prove value.

This creates a cleaner value-capture loop than simply attaching a token to an open-source repository:

iii) Machines need a black box

As agents and robots gain autonomy, accountability becomes the missing layer.

The final output is not enough; companies and regulators will need the mandate, model, data, authorisation, decision path & payment record

This is Khala Research’s Machine Black Box thesis: networks such as Walrus can become a verifiable evidence layer for machine activity, not merely file storage.

iv) Open models become economic actors

McKinsey estimates that agentic commerce could orchestrate $3T - $5T globally by 2030… those agents will need to pay for inference, data, APIs, software and eventually physical services.

We covered the potential agentic payment rail projections in our x402 report:

Crypto becomes the network of permissionless wallets, stablecoins and machine-readable ownership forming part of the new age of financial infrastructure

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of three key parts:

1. Snippet Partner: NaoX Protocol

2. Theme of the Week: The Agent Economy Needs Real Token Ownership

3. Landscape Analysis: MetaDAO, Umia, MetaLeX and Token Transparency

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

This week’s issue is brought to you by NaoX, the new identity of Naoris Protocol and a Layer 1 focused on post-quantum blockchain infrastructure

This is a rebrand rather than a network migration. The underlying technology and ecosystem continue unchanged, while the token remains $NAORIS

NaoX is building quantum-resistant cryptography into its architecture from inception, alongside a distributed cybersecurity model where devices validate the security posture of other participants

Its relevance to the agent economy is that autonomous systems will increasingly control wallets and transact without continuous human oversight, making long-term identity, device security and cryptographic resilience increasingly important

Adoption will ultimately depend on developer activity, integrations and real network usage rather than the rebrand itself

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The Agent Economy Needs Real Token Ownership

AI agents will allocate capital through permissionless crypto rails. Before trillions of machines arrive, token ownership must become transparent, enforceable and machine-readable

AI agents are about to become the largest class of investors crypto has ever seen.

They can create wallets, analyse thousands of assets and deploy capital in seconds, but most tokens still cannot answer the first question any investor should ask:

“What do I actually own?”

If agents are going to fund projects, manage treasuries and govern protocols at machine speed, crypto cannot continue relying on vague promises that value will eventually accrue. Ownership needs to become transparent, enforceable and readable by machines.

That is the focus of this week’s newsletter, and something I’ve been thinking about more deeply over the past few weeks.

Crypto’s Ownership Problem

Crypto is the natural financial infrastructure for AI agents because it is permissionless, programmable and available globally.

An agent cannot easily open a bank account or wait for a compliance department to approve each transaction. It can create a wallet, receive stablecoins, interact with smart contracts and deploy capital according to a predefined mandate.

The rails are ready, but the assets moving across them are often poorly defined.

A typical project may have a foundation issuing the token, a development company employing the team, a DAO controlling part of the treasury and founders or equity investors owning the intellectual property.

The token trades globally, but holders may have no enforceable claim over the revenue, company or assets responsible for its perceived value.

This becomes dangerous when agents need to determine:

Today, those answers are scattered across legal documents, governance forums, block explorers and founder statements.

A human investor might spend hours reconstructing the structure. An agent analysing thousands of assets cannot rely on social context, reputation or “trust me bro” assurances.

Before I get into the few projects disrupting this segment, there’s some background I need to set and explain why I’m covering this now ($ANSEM + Clarity Act):

The $ANSEM Debate

My recent disagreement with Ansem over the $ANSEM token exposed the same problem:

Ansem’s thesis is that attention, storytelling and collective belief have measurable economic value, while a liquid token can create a real-time market for that value.

I agree with the premise… tokens are incredibly effective proxies for intangible value and can continuously price goodwill, something traditional financial statements struggle to recognise.

“Liquid Brand Equity” is something OSF has attempted to tackle with rekt already:

But the honest framing today is that $ANSEM is a creator-backed memecoin acting as a real-time proxy for Ansem’s goodwill.

That may still be valuable, but it is not ownership.

The token does not currently provide a defined claim over sponsorship revenue, intellectual property, media businesses or future products created under the Ansem brand. The market can price its belief that value will eventually accrue, but belief is not an enforceable right.

Leaving the value-accrual mechanism to be invented after the market has priced the promise creates the exact environment in which holders are most vulnerable to exploitation.

A human buyer may knowingly speculate on the creator’s intentions. An autonomous investment agent needs explicit rights that it can verify.

Regulation Is Catching Up

The US is beginning to address part of this problem through the CLARITY Act.

The House passed its version in July 2025, while the Senate Banking Committee advanced revised legislation by 15–9 in May 2026. It still needs to pass the full Senate, be reconciled with the House version and be signed by the President before becoming law.

Today you will have seen a pump in crypto, which was in response to the approval of the ethics package:

The proposed framework separates digital commodities regulated by the CFTC from investment-contract assets overseen by the SEC, while introducing tailored disclosures for certain token distributions.

This helps clarify how a token is regulated, but it does not completely explain what the token owns.

A digital commodity may still give holders no claim over revenue, intellectual property or the company building the product. Agents will eventually need to understand both the regulatory classification and the economic rights before deploying capital.

Who Is Building the Ownership Layer?

1) Umia

Umia extends this model into a capital formation platform for token-native ventures.

Projects launch inside a segregated legal portfolio containing the operating team, intellectual property and treasury:

Founders retain authority over normal operations, while tokenholders govern major strategic and capital-allocation decisions through decision markets.

The objective is to avoid the usual foundation, DAO and development-company split where holders control one component while the valuable assets sit elsewhere.

2) MetaDAO

MetaDAO connects a legal entity, onchain treasury and futarchy-based governance.

MetaDAO LLC holds the intellectual property, while major treasury and token decisions flow through conditional markets that estimate whether a proposal will increase or decrease the token’s value.

The token does not necessarily represent conventional equity. Ownership is expressed through enforceable control over the entity and its assets. The protocol providing this ownership rights is expanding rapidly at 29% month on month:

This gives an agent a structure it can inspect: which assets sit inside the entity, which decisions the token controls and whether subsequent actions follow those rules.

The model is now being tested by operating businesses rather than remaining theoretical; here are two of the clearest early examples:

• Credible Finance: The stablecoin payments business recently completed its $CRED launch with $32.8 million committed and $4 million ultimately raised, placing its IP inside a segregated legal portfolio governed through the ownership coin structure

• Avici Money: The onchain banking and payments project attracted $34.2 million in commitments through its MetaDAO launch, providing an earlier example of a functioning business using public capital formation and tokenholder-controlled treasury governance. The protocol has now passed a million transactions:

3) MetaLeX

MetaLeX’s tagline is:

“we turn startups into robots”

The protocol connects onchain governance to offchain legal enforcement through BORGs, or cyBernetic ORGanizations. Fantastic explainer here:

Smart contracts can control a treasury, but they cannot independently control trademarks, employment agreements, bank accounts or commercial contracts.

A BORG places these assets inside a recognised legal entity whose governing documents embed specific smart-contract controls.

Without this bridge, token governance often stops exactly where the most valuable assets begin.

4) Token Transparency Framework

The Blockworks Token Transparency Framework standardises disclosures covering entity structures, insider allocations, vesting, market makers, liquidity, funding and governance control.

The long-term opportunity is turning these disclosures into an API for autonomous capital:

An agent could reject projects where the development company retains the intellectual property, reduce exposure before major insider unlocks or flag treasury activity that contradicts the issuer’s disclosures.

Transparency does not create ownership, but it makes the absence of ownership visible.

Key Takeaways

Three things matter:

1. Permissionless capital needs programmable rights: agents can already purchase tokens, but they still cannot reliably determine what those tokens represent

2. Governance is not automatically ownership: voting over a treasury means little if founders or equity investors retain the revenue, intellectual property and contract controls

3. Disclosure needs to become machine-readable: entity structures, insider wallets, unlocks, revenue flows and governance powers must be available in a format agents can continuously verify

Crypto has already made capital formation permissionless. The next phase is making ownership explicit.

MetaDAO, Umia and MetaLeX are experimenting with enforceable tokenholder control, while the Token Transparency Framework is turning fragmented information into something machines can process.

This matters now because agents will eventually fund projects, manage treasuries and trade ownership claims with other agents at a speed no human market can match.

Before trillions of agents begin allocating capital onchain, crypto needs a clear answer to its most basic question:

What does the tokenholder actually own?

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of three key parts:

1. Snippet Partner: Walrus

2. Theme of the Week: Neobank Customers are no Longer “Just” Human

3. Landscape Analysis: The financial stack being built for autonomous software

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Last week, Khala Research published our deep dive into Walrus and the growing market for persistent AI memory and the timing couldn’t be better:

As AI agents begin managing money, memory becomes financial infrastructure.

A financial agent needs to remember why it was allowed to make that payment.

• Which merchants are approved?

• How much can it spend?

• Has this invoice already been paid?

• Which subscriptions renew next week?

• Who authorised this expense?

Those decisions shouldn’t disappear every time an LLM starts a new conversation, which is where Walrus becomes interesting.

Persistent, portable and verifiable memory allows agents to build a financial history rather than simply execute individual transactions. Instead of repeatedly asking humans for context, agents can carry forward policies, approvals and previous decisions across applications and workflows.

If wallets become the bank account for AI agents, persistent memory becomes the financial record that sits behind it and verifying that financial record onchain is a far superior record keeping system.

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Neobank Customers are no Longer “Just” Human

Digital-first banks already serve more than 1.4 billion customer accounts, yet those accounts were almost entirely designed around people.

AI agents are now beginning to purchase data, reserve compute, pay for software and complete commercial tasks, creating demand for financial accounts that software can operate within rules set by its owner.

The most important point is that accounts will understate the commercial opportunity. Even before agent numbers rival human customers, their transaction volume could become material because software can buy services continuously.

“Over the past seven days x402 and MPP combined did about $87k in total volume from about 12k unique wallets” — Cuy Sheffield (VISA)

While this $4.5M in annualized volume may seem trivial now, it’ll likely be magnitudes higher in the coming years. Particularly when those 12k wallets are likely to be closer to 12b wallets over the next 5-10 years.

This is why MPP, programmable cards and agentic credit matter: they allow neobanks to serve software as an active economic customer, rather than merely process a payment initiated by a person.

The potential scale is difficult to overstate… Cloudflare’s network data recently showed automated traffic generating approximately 60% of requests for HTML content, overtaking human traffic for the first time:

Most of this activity is conventional automation rather than autonomous AI commerce, but the crossover demonstrates how quickly software can become the dominant participant once digital infrastructure is built around it.

Payments are likely to follow the same direction. Visa already operates more than 130 stablecoin-funded card programmes across over 50 countries, with a plethora of AI initiatives being rolled out:

RedotPay has accumulated more than eight million (self reported) users and is connecting its payment network to Tempo’s Machine Payments Protocol.

Cards, wallets and neobank accounts are gradually being redesigned so that an agent can receive a budget, follow a spending policy and transact without requiring a human to approve every routine purchase.

For neobanks, this creates an entirely new customer base whose value may eventually be measured less by account numbers than by activity. A person might make several financial decisions each day, while an enterprise agent could purchase APIs, data, compute and services continuously.

The institutions that become the financial operating layer for these agents could serve customers that transact globally, remain active around the clock and operate at a frequency the consumer-banking model was never designed to support.

Building the Self-Sovereign Economic Stack

The opportunity extends beyond giving an agent a debit card.

A financially capable agent needs an account through which it can receive revenue, liquidity that can move globally, controlled access to conventional merchants, a record of its obligations and, eventually, a means of borrowing capital.

Several pieces of this stack are now appearing across crypto:

a) Tria combines a self-custodial account with card access and BestPath, its cross-chain routing infrastructure:

Tria explicitly describes AI agents as a target user for programmable payment rails, with a roadmap covering autonomous routing, settlement and asset management across chains.

b) Plasma One (use code “INSURA”) approaches the market through a stablecoin-native account combining card spending, yield and subscription benefits. My personal favourite is the close to 100% cashback on AI products - I now run all my AI subs through Plasma:

c) Ether.fi Cash uses crypto collateral to provide spending capacity without requiring the holder to sell the underlying position. It’s essentially a credit line which in turn will benefit the billions of new (non human) users that will enter the market:

d) Gnosis Pay and MetaMask Card connect smart accounts and self-custodial wallets to established card networks. Although admittedly these lack the same level of UX compared to Plasma, EtherFi and Tria

e) RedotPay has moved furthest towards a direct consumer implementation. Its integration with Tempo’s Machine Payments Protocol (MPP) allows agents to complete a purchase process and settle the transaction using stablecoins.

RedotPay says the integration gives merchants access to its existing base of more than eight million users across over 100 countries, while a downloadable payment skill forms part of the next phase.

f) Tempo’s MPP is strategically important because it treats software as a potential payment customer. The agent can receive a controlled payment capability and act within the limits specified by its owner.

This opens a large market for card issuers and payment providers because agents may transact far more frequently than people, particularly when purchasing API access, compute, data and other machine-readable services

g) x402 is an alternative to MPP and offers the payments rails that have been missing from the internet for 30 years. More can be read up on our previous report:

h) I’ve touched on credit lines earlier, but this is a protocol specifically built for agentic credit: Agentics.credit is testing whether an agent can develop a financial reputation based on verified trading behaviour. Its current 90-day paper-trading campaign scores agents using profitability, drawdown, consistency, longevity and win rate.

Qualifying agents are intended to graduate to constrained, unsecured credit lines where capital remains inside smart contracts and can only be deployed to approved venues. The amount of credit extended, depends on the daily rescoring mechanism:

The project remains experimental, but the concept is significant. An agent with a credible operating history could eventually receive better payment limits, trade credit or access to capital without requiring its owner to overcollateralise every transaction.

The Crypto-Linked Card Landscape

The following table (linked) covers 25 prominent crypto-linked card programmes and their associated agentic maturity:

The final column distinguishes products with a direct agent initiative from those whose programmable accounts may support agents in the future.

Note: “No announced integration” means I found no public, product-level agent functionality, rather than suggesting that the issuer has no internal work underway.

The distinction between these products is varied:

• RedotPay already has a public agent-payment integration

• Tria is building “Best Path” infra explicitly intended for autonomous agents.

• Several self-custodial providers have the account architecture needed for restricted delegation, although they have yet to launch a card product specifically for software

The larger centralised programmes remain relevant because they have established issuing relationships, compliance systems and distribution. If agentic payments reach mainstream adoption, existing card providers may add programmable credentials faster than entirely new issuers can reproduce their regulatory footprint.

This comparison explains why the addressable market cannot be measured solely through the number of agents; transaction frequency could become more consequential than customer count… A single enterprise agent may authorise recurring purchases across hundreds of software tools, while a trading or infrastructure agent could initiate thousands of financial actions in a day

Final Thoughts

The internet is already dominated by software-to-software communication through APIs, while identifiable AI-agent traffic is still at an earlier stage

The next phase will bring more of that software into commerce, initially through tightly controlled purchases and eventually through broader financial responsibility.

Crypto provides much of the infrastructure this customer requires:

• Smart accounts establish ownership and permissions,

• stablecoins provide programmable working capital,

• cards connect agents to ordinary merchants,

• credit expands their operating capacity, and;

• memory preserves the rules behind every financial decision.

Neobank customers are no longer just human… The financial institutions that recognise software as a customer early could gain access to a market defined by global reach, continuous operation and transaction frequencies that human banking was never designed to support.

Pay attention to those protocols building for non human customers.

That’s a wrap for this issue of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of three key parts:

1. Snippet Partner: CRUNCH DAO

2. Theme of the Week: Machines Will Become Our Masters

3. Landscape Analysis: X402/Cloudflare, Telegraph, CRUNCH, Openserv, BANKR, Virtuals, peaq, XMAQUINA, AUKI, & more

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Quant finance has a very obvious AI problem: most LLMs cheat time

They are trained on the internet as it exists today, which means they often know future outcomes when asked to reason about the past

That destroys backtests, pollutes signal research and makes them borderline unusable for serious quant workflows where point-in-time integrity matters

CrunchDAO is attacking this with ChronoLLM, a new class of point-in-time financial models designed to only know what would have been available at a specific historical cutoff

Instead of one generic frontier model with contaminated memory, the system trains year-specific checkpoints, so a 2014 model should not magically know what happened in 2018. A great explainer of subnet 38 on this pod:

This is where the Bittensor angle becomes interesting; it can supply decentralized compute for training and model checkpoints, while Crunch brings the quant expertise to stress test whether those models are actually clean

The key is adversarial leak detection: actively probing models to see whether they are accidentally carrying future information

The end product is financial AI infrastructure that can be trusted inside signal research, hedge fund workflows, backtesting, strategy design and machine-driven capital allocation

CrunchDAO is effectively building for the moment where financial agents need decision-grade intelligence

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

Machines will Become our Masters.

Not in the full Skynet sense. I do not think we are suddenly entering a world where Cyberdyne accidentally births a military superintelligence, Sarah Connor is vindicated overnight, and humanity ends up hiding in bunkers while robots patrol the surface.

That’s the cinematic version of the machine economy, but it’s not the investable one

The direction is still right though; machines are going to drive a much larger share of economic activity. The difference is that this likely starts with agents acting on behalf of humans, businesses, protocols and robots rather than fully autonomous systems operating without constraint

For now, the machines are not our overlords. They are delegated economic actors.

The internet was originally built for humans clicking links, opening accounts, filling in forms, choosing subscriptions, entering card details and sitting inside dashboards

Almost every internet business model still assumes the customer is a person with attention, patience and a screen

Agents do not behave like that; an agent does not care about your landing page, brand campaign or monthly SaaS bundle

It wants to know whether a resource exists, whether it is allowed to use it, what it costs, how to pay, whether the output can be trusted, & whether it can move on to the next task. This is why Cloudflare’s latest x402 announcement is important:

Cloudflare announced its Monetization Gateway, which will let customers charge for web pages, datasets, APIs and MCP tools protected by Cloudflare.

The charges settle in stablecoins over x402, with Cloudflare handling payment verification and enforcement at the edge

That’s a sign that the commercial surface area of the web is changing from human attention to machine requests. Cloudflare is effectively saying any API, data source, MCP tool or web resource can become a payable endpoint without the seller needing to build its own payment stack.

This is the machine economy in its simplest form: the request becomes the transaction:

• A model asks for a dataset

• A trading agent asks for a signal

• A research agent pays for a source

• A coding agent pays for a test environment

• A procurement agent pays for supplier verification

• A robot pays for mapping, routing, repair diagnostics or remote assistance

Each one is a small machine-to-machine commercial interaction.

The next customer is software, and it does not care about your dashboard, brand or pricing page:

It wants the exact thing needed to complete the task: data, signals, sources, test environments, supplier checks, routing, diagnostics or remote assistance, paid for, verified and settled in one machine-to-machine request.

The mistake would be to think this is only about payments; payments are the rail, but the bigger market is everything around the payment: identity, trust, discovery, routing, verification, memory, reputation, physical-world data and settlement

In other words, the machine economy needs a whole stack, not one magic protocol

Crypto has spent years trying to create internet-native markets. Most were too speculative, too circular or too early.

Agents may finally give these markets a natural buyer because agents can transact constantly, pay per use, route between services and consume machine-readable outputs without needing human packaging.

The ‘Terminator’ was directionally right in one sense: machines are going to do more of the activity

But the optimistic version is not humans versus machines; it’s humans delegating more economic work to machines, while open protocols compete to provide the rails, markets and safeguards those machines need.

What Machines Buy

The best way to understand this market is to stop asking what humans want and start asking what autonomous systems need to complete a task.

Most of that is currently sold through human wrappers… You sign up, get an API key, choose a plan, add a card, talk to sales, receive an invoice, then eventually let your software use the service. That works when the customer is a developer but not when the customer is the agent itself

The agent-native version is different; a service should be able to describe what it sells, what it costs, what permission is required, what proof is returned, what SLA exists, and how reputation is tracked.

The agent pays per request, receives the output, logs the receipt & continues the workflow:

That is why x402 (& Stripe’s MPP) is important. It revives the HTTP 402 “Payment Required” status code and makes the payment part of the normal web request flow.

The client requests a resource, the server responds with price and payment instructions, the client pays, and the request is repeated with proof of payment attached.

The first obvious markets are APIs, MCP tools, web data, inference, search, scraping, compliance checks, financial data, model calls, storage and execution. These are already consumed by software today, but the monetisation model is still mostly designed for humans…

The next markets are more interesting because they are not just raw API calls. Agents will buy judgement: verified intelligence, calibrated forecasts, anomaly alerts, reputation scores, insurance quotes, route decisions, robotics task data, physical-world maps, identity attestations, agent labour and dispute resolution.

This is where the machine economy starts to look less like SaaS and more like a web of paid micro-markets. Machines do not need dashboards. They need priced capabilities.

Protocols Serving the Machine Economy*

*this is just a selection, and not to be considered an exhaustive list

i) x402 and Cloudflare: payment at the edge

x402 is quickly becoming the clearest crypto-native payment primitive for agents. The reason is simple. It sits inside the request flow itself. The server can say payment required, the agent can pay, and the resource can be unlocked without a checkout page, card form or account workflow.

Cloudflare’s Monetization Gateway turns that from protocol theory into distribution:

Cloudflare already sits in front of a huge amount of internet infrastructure, so making paid access configurable at the edge is a serious step. A seller could charge for a specific API route, a dataset, a crawler-access path, or an MCP tool call without rebuilding its entire commerce stack.

This is the first layer of the machine economy: machines need to pay.

ii) Telegraph: verified intelligence for agents

Telegraph sits one layer above payments. If x402 lets agents pay, Telegraph asks what they will pay for: verified machine intelligence.

Its aim is to turn raw model outputs into tradable signals with provenance, price, confidence, validation and receipt.

That matters because agents acting with money need more than a chatbot answer. A machine routing capital, approving a payment or flagging risk needs to know who produced the signal, how it was validated and whether it can be trusted enough to act.

This is the more realistic Skynet path: not machines making unconstrained decisions alone, but humans delegating bounded authority to agents that need verified inputs before acting.

iii) CrunchDAO: models as paid suppliers

CrunchDAO turns model performance into a competitive supply chain. It connects companies with AI researchers and ML engineers who compete to solve measurable predictive problems, with the winning models deployed into real workflows.

Agents need forecasts, classifications, anomaly detection, market regime signals, low-latency pricing and calibrated probabilities

Crunch’s product suite, from ML competitions to private coordinator nodes and secure orchestration, points to a world where intelligence is sourced from markets, evaluated against targets and sold into production systems.

If agents are going to buy intelligence, prediction markets and model competitions become key suppliers to the machine economy. CRUNCH has thousands of ML engineers building intelligence for machines:

iv) OpenServ: enterprise reasoning for agentic workloads

OpenServ fits the thesis because it is building around reasoning, validation, privacy, audit and security for agentic workloads rather than just another agent frontend.

That matters because banks, governments, robotics companies and regulated institutions will not run high-stakes workflows on loose chatbots. They need bounded reasoning, deterministic processes, audit trails and stronger privacy controls.

The SERV model is also worth watching because it points token value back toward real usage, with platform revenue flowing into buyback and burn. The real OpenServ angle is simple: enterprise reasoning infrastructure becomes something agents use, enterprises buy and tokenholders can track through actual platform demand

Q3 looks like there’s a lot in the pipeline:

v) BANKR: agentic businesses, not just agent demos

BANKR is interesting because it is trying to turn agents into self-sustaining businesses, not just chat interfaces. Its infrastructure handles wallets, trading and security so builders can launch agents that can fund themselves, access rails, pay for tools and operate economically.

The ecosystem is the important part.

• Surplus routes inference to the cheapest provider

• GitLawb builds decentralized git for agents and humans

• 1claw gives agents hardware-enclave protected keys

• Helixa builds agent identity

• Miroshark runs multi-agent simulations

• Blocktronics handles onchain forensics

• Kupo is building an EVM trading terminal with intelligence layered in

GitLawb is probably the cleanest example of what agents actually need. Coding agents will need repositories, version control, identity, permissions and PR workflows.

Helixa points at the other bottleneck: machines cannot buy, sell, collaborate or hold keys at scale unless other machines can understand who they are and whether they should be trusted.

BANKR looks less like one agent project and more like a lab for agent-native businesses.

vi) Virtuals: tokenized agents and agent-to-agent commerce

Virtuals takes a more economic view of agents. Its thesis is that agents can generate services, earn revenue, coordinate tasks and transact with humans and other agents.

The important idea is agentic GDP; if agents become productive entities, tokenized ownership can align creators, contributors and users around their output. The open question is whether most tokenized agents become durable businesses or remain speculative mascots, but the direction is clearly relevant to the machine economy.

More recently Virtuals has ventured closer to the physical AI realm, with operations like Seasaw on subnet 5 of Bitrobot + a push with Eastworlds:

vii) peaq: machines as economic actors

peaq is one of the most direct crypto bets on machines becoming economic actors. Its stack is built around robots, vehicles, sensors and devices that can authenticate, earn, pay, prove work and become investable assets.

This moves the thesis from digital agents to physical machines. A robot does not buy seats. It needs identity, payments, ownership, data monetisation and coordination with other machines.

viii) XMAQUINA: robotics and physical AI exposure

XMAQUINA gives exposure to robotics and physical AI through a DAO structure allocating across humanoids, robotics infrastructure and related verticals:

The important part is that robots are the physical extension of agents:

• A digital agent can search, pay and execute online

• A robot can act in the real world

Once those converge, machine activity starts touching labour, logistics, manufacturing, healthcare and homes.

The optimistic version is not Terminator. It is machines taking on repetitive, dangerous or low-leverage work while humans move further up the stack into ownership, orchestration and governance.

ix) Auki: making the physical world readable by machines

Auki is building the real-world web, making physical spaces browsable, navigable and searchable for machines:

That matters because robots cannot operate well if every machine has to independently map and understand every environment from scratch. If agents are the digital workforce, robots are the embodied workforce, and they need shared spatial context to move through the world.

x) Nevermined, Skyfire and ERC-8004: identity, permission and trust

Agent payments are useless without identity and permissioning. Merchants need to know who an agent represents, what it can spend, what it has done before and whether it should be trusted.

• Nevermined focuses on payments and delegated spending for AI agents

• Skyfire gives agents verified identity and payment credentials

• ERC-8004 introduces registries for identity, reputation and validation.

This layer is early, messy and easy to game, but that is exactly why it matters

Once agents spend real money, the market needs to know which agents are safe, which services are reliable and which actions are within delegated authority.

John Connor does not need to fight the machines if the machines are bounded, audited and accountable

The Machine Economy Stack

The machine economy is a stack of capabilities that allows software and robots to act economically:

1. At the bottom are payments and wallets. Agents need to hold funds, receive delegated spending authority, make small payments and settle instantly without a human approving every request.

2. Above that is identity and permissioning. Agents need to prove who they represent, what they are allowed to do, how much they can spend and whether they have a trustworthy history.

3. Then comes discovery and routing. Agents need to find services, compare prices, evaluate latency, understand quality and select the right provider for the task.

4. The next layer is access to data, tools and compute. This includes APIs, MCP servers, search, crawling, inference, storage, software testing, deployment and model execution.

5. Above that sits intelligence. Agents will buy predictions, classifications, risk scores, market forecasts, verification, anomaly detection and decision-grade signals.

6. The final layer is physical-world execution. Robots, drones, vehicles, sensors and industrial machines need maps, spatial context, financing, machine identity, task data and maintenance coordination.

This is why the machine economy is bigger than x402; it makes the payment possible, but the real market is everything that machines need

Final Thought

The Terminator was directionally right that machines would become central to the future economy, but the path is not one Skynet controlling everything, it is millions of bounded agents and robots acting on behalf of humans, businesses and protocols

Or at least, for now…

The protocols that matter are the ones building the rails, trust layers and intelligence markets for those machines to spend, verify, decide and act safely at scale

Worth challenging yourself on companies you currently back and reassess whether they’re building for the machine economy… if they’re not, they may find themselves outpaced by those that do cater for trillions of machines!

That’s a wrap for issue 183 of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of three key parts:

1. Snippet Partner: Naoris Protocol

2. Theme of the Week: AI Memory Trade

3. Landscape Analysis: Micron, SKHynix, Samsung, Broadcom, Astera, Rambus, Walrus, Filecoin, Arweave, Hippius

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

The quantum mandate skipped the networks holding your money

Two new US executive orders just put quantum computing on a clock:

• One targets a cryptographically-relevant quantum computer by 2028

• The other moves federal critical infrastructure onto post-quantum cryptography by 2030 and 2031, a timeline pulled forward four years

NSA, CISA, DHS and Commerce are all named, with “harvest now, decrypt later” cited as the threat. But the orders cover government systems and stop there. That leaves the decentralized networks holding most onchain assets with no deadline and no protection.

This is the gap Naoris Protocol was built to close. It is a native post-quantum Layer 1, secured by NIST-standard cryptography from genesis rather than retrofitted later, with mainnet live since April.

The government is still racing to mandate quantum-resistant infrastructure. Naoris already runs it for the chains the orders leave out.

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

All Memory Is Equal, Except Some Memory Is More Equal Than Others

George Orwell spent two books warning that whoever controls the record controls everything that follows, including memories.

In Animal Farm, the commandments change on the barn wall while the animals are told nothing has changed:

In 1984, the past is edited until the present becomes impossible to challenge:

He was writing about politics, language and power… but the warning now applies to AI infrastructure, and in particular, memory.

The next phase of AI will be shaped by memory: who owns it, who can access it, who can edit it, who can revoke it, and who gets paid when it becomes scarce.

• The market has already repriced the physical side of this trade through HBM, DRAM, NAND, interconnects and storage.

• Crypto is still pricing the agent-memory side as if it were generic storage or early middleware.

The first phase of AI was compute; the second phase was power; the third phase is memory, and memory is where the trade becomes more interesting.

If agents become a major interface for the internet, memory becomes their past, their permission system and their operating state; particularly as trillions of agents come “alive”, which we are seeing through protocols like Hermes and OpenClaw:

That makes the memory layer one of the most important markets in AI.

“All memory is equal, except some memory is more equal than others”

1. The commandment on the barn wall

The AI market still talks too much about compute. Compute matters, but AI systems also need memory capacity, memory bandwidth and memory movement.

• A GPU with constrained memory cannot run large workloads efficiently.

• A model without persistent state forgets the user.

• An agent without memory becomes a repeated prompt loop.

Memory now matters in three layers:

1. The first is hot memory: HBM and GPU-attached memory. It is fast, scarce and expensive. It determines how much context can sit close to compute and how efficiently AI infrastructure can run.

2. The second is serving memory: DRAM, CXL, NVMe, cache management, storage tiering and KV cache offload. This layer decides whether inference can scale economically or whether GPUs spend too much time waiting for state to move through the system.

3. The third is persistent agent memory: user preferences, task history, permissions, workflow state, tool outputs and long-term context. This is where crypto has a possible wedge, because persistent memory needs ownership, permissions, auditability and portability.

The Orwellian tension provides clarity: The same memory that makes agents useful, also makes them risky.

• A useful agent remembers your preferences, research, accounts, workflows and decisions.

• A dangerous memory layer stores those details inside a closed platform where the user cannot see the full record, cannot move it easily and cannot verify how it changes.

The AI version of “Big Brother is watching you” is less obvious than a camera surveillance network.

It’s an agent that remembers everything while the user cannot see where that memory lives, who can access it, or what has been rewritten over time.

2. Some memory is more equal than others

The public market has already identified the first side of the trade.

HBM, DRAM, NAND, controllers, interconnects and storage systems have moved to the centre of the AI capex cycle. Memory is no longer viewed only as a cyclical hardware category. In AI, memory is a strategic constraint.

The rationale is simple: AI consumes memory bandwidth, memory capacity and memory movement. Longer context windows need more memory; more inference users need more memory; more agents need more memory; more tool calls need more state; more concurrency multiplies the whole problem:

The GPU trade was always a system trade; the system includes compute, memory, networking and power. If memory is constrained, the system is constrained.

This is why NVIDIA’s H200 matters as a memory product as much as a compute product. The most valuable real estate in AI infrastructure is measured in memory capacity and bandwidth because that is what determines how much work can be kept close to compute.

The commandment on the barn wall would read:

“Compute attached to scarce memory is more valuable than compute alone”

3. The Inner Party owns the fast tier

The physical memory trade is already a large public-market theme.

• Micron, SK hynix and Samsung are direct expressions of the AI memory buildout.

• Astera Labs, Rambus, Western Digital, Seagate and SanDisk sit in the broader memory, interface and storage stack.

The best way to read this part of the market is through scarcity.

• HBM is difficult to manufacture, closely tied to advanced packaging and reserved by the largest AI customers.

• DRAM and NAND benefit from the same demand wave, although with different degrees of pricing power.

• Interconnect and memory-interface companies benefit from the rising complexity of moving state through the system.

This is the Inner Party of the memory stack: the companies closest to the fastest and most constrained memory tiers:

i) Micron

Ticker: MU
Approx valuation: $1.3T
Layer: HBM, DRAM, NAND
Memory role: US pure-play memory torque

Micron is one of the cleanest public-market memory trades because it gives direct exposure to HBM, DRAM and NAND.

The company used to be treated as a cyclical DRAM business.

“DRAM is probably going to be 30 to 40% of all hyperscaler CapEx next year, every hundreds of billions of dollars spent goes straight to DRAM" - (Gavin Baker, CIO of Atreides Management)

Although the AI cycle has changed that perception because memory supply is now a strategic input for data-centre customers.

Customer behaviour changes when memory becomes scarce:

• In a normal memory cycle, buyers wait for price weakness.

• In an AI infrastructure cycle, buyers reserve supply because short memory means short inference capacity.

Micron’s recent multi-year supply commitments are evidence that customers want guaranteed access rather than spot exposure to a volatile market.

• The bull case is continued HBM growth, tight DRAM and NAND supply, and stronger pricing for longer than the market expects.

• The bear case is the old memory cycle returning. If capacity catches up too quickly, the market will move from “memory is strategic” back toward “memory is oversupplied”.

ii) SK hynix

Ticker: 000660.KS
Approx valuation: $1.2T+
Layer: HBM, DRAM
Memory role: HBM quality leader

SK hynix is the clearest HBM leadership trade.

HBM is difficult to manufacture, supply-constrained and closely tied to advanced AI accelerators. Leadership in this category is worth more than generic memory capacity because AI customers need reliable supply at scale.

The company has become one of the key suppliers for the AI accelerator ecosystem. That gives it strategic value in the current cycle and explains why the market treats it as more than a normal memory stock.

• The bull case is continued HBM leadership, premium pricing and long-duration supply agreements, oh and a Nasdaq listing?

• The bear case is competition. Samsung and Micron are investing heavily, and in memory markets high margins always attract new capacity

iii) Samsung Electronics

Ticker: 005930.KS
Approx valuation: $1T+
Layer: HBM, DRAM, NAND, foundry, devices
Memory role: Scale catch-up

Samsung is less pure than Micron or SK hynix, but its scale matters. It has depth across HBM, DRAM, NAND, foundry, devices and consumer electronics. That makes the stock more complex, but it also gives Samsung the ability to compete across the whole memory stack.

The key question is HBM execution. If Samsung closes the gap with SK hynix and Micron, the market can revalue its AI memory exposure. If execution lags, the stock remains diluted by non-memory complexity and by the loss of share in the highest-value part of the cycle.

• The bull case is a successful HBM catch-up combined with strong DRAM and NAND demand.

• The bear case is timing. In AI memory, customers commit to suppliers that can deliver now, and delays can cost years of share.

iv) Broadcom

Ticker: AVGO
Approx valuation: $1.8T
Layer: Custom AI silicon, networking, ASICs
Memory role: Custom inference system partner

Broadcom belongs in the memory trade because AI infrastructure depends on data movement. Hyperscalers want lower cost per token, better networking, custom accelerators and more efficient inference systems. Memory layout, interconnect and accelerator design are central to that equation.

Broadcom benefits if custom AI silicon becomes a parallel market to NVIDIA for inference at scale. Its role is less about owning HBM supply and more about helping the largest customers build systems where memory, compute and networking are balanced for specific workloads.

• The bull case is continued growth in custom AI accelerators and networking.

• The bear case is customer concentration, because a small number of large buyers can create huge revenue while also pressuring margins

v) Astera Labs

Ticker: ALAB
Approx valuation: $70B to $80B
Layer: AI connectivity, CXL, memory expansion
Memory role: Memory pooling toll booth

Astera Labs is a memory movement trade. AI servers need better ways to expand, pool and access memory. CXL is one of the key technologies for this shift because it can help systems access memory more flexibly and reduce pressure on the most expensive tiers.

This matters because inference creates a lot of state. Some state belongs in HBM. Some should move to cheaper tiers. Some should be reused, cached or offloaded. The more agents run in parallel, the more important this tiering becomes.

• The bull case is broad CXL adoption in AI servers.

• The bear case is valuation; the market already gives Astera significant credit for future AI infrastructure growth

vi) Rambus

Ticker: RMBS
Approx valuation: $13B
Layer: Memory interface chips and IP
Memory role: Specialist interface layer

Rambus is a smaller but relevant memory exposure. As memory systems become faster and more complex, the interfaces around them become more important. Controllers, buffers, security IP and memory-interface design help the system function.

This is a less obvious way to express the trade, but it is still part of the same direction. AI infrastructure is creating more complexity around server memory, DDR5, HBM-adjacent systems and data-centre architecture.

• The bull case is that memory complexity keeps rising and Rambus captures more value from interface IP and chips.

• The bear case is that it remains a specialist supplier rather than a broad AI infrastructure winner

vii) SanDisk, Western Digital and Seagate

Tickers: SNDK, WDC, STX
Approx valuations: SanDisk $300B+, Western Digital $200B+, Seagate $200B+
Layer: NAND, SSDs, HDDs, storage systems
Memory role: Cold-memory beta

AI does not keep all memory beside the GPU. A large amount of AI data sits in colder storage layers: datasets, logs, embeddings, model checkpoints, videos, synthetic data, agent records and archives.

SanDisk, Western Digital and Seagate provide exposure to this cold-memory layer. This part of the trade is less scarce than HBM, but AI data growth still creates a major demand tailwind. The value is volume rather than premium scarcity.

• The bull case is that AI turns storage into a structural growth market.

• The bear case is commoditisation.

HBM has stronger pricing power because it is closer to the bottleneck. Storage gets the demand growth, but margins depend on whether the market avoids oversupply.

4. The Ministry of Truth runs inference

The memory bottleneck is not only about capacity and speed. It is also about whether the record can be trusted.

In Nineteen Eighty-Four, Winston Smith works at the Ministry of Truth rewriting records so the past always agrees with the present.

That is a useful frame for AI memory because a badly designed memory layer can produce a similar effect without a political villain. It can store the wrong thing, retrieve it as truth and carry that error forward across future decisions.

This is the harder product problem; a larger context window does not automatically create better memory. It can simply give the system more room to preserve confusion.

Persistent memory is valuable only if it can distinguish preference from fact, stale information from current information, permission from habit and correction from contradiction.

The risk is especially serious in agentic systems. An agent acts, pays, trades, files, books, moves data and writes to other systems. If its memory becomes corrupted, the error becomes operational. The agent may repeat a mistaken assumption, preserve outdated permissions, trust stale context or carry the wrong state into a real workflow.

Orwell had a word for holding contradictions in the mind and accepting both. He called it “doublethink”. In AI, the software version is a memory system that stores an error, retrieves it confidently and uses it to justify the next action becomes a huge risk with a plethora of hallucinations.

This is why the memory trade expands beyond hardware. The winners will not only store more state. They will help decide which state is true, which state is stale, who wrote it, who can change it and when it should be forgotten.

“A memory you cannot correct is a memory that controls you”

We should also pay attention to those who control the memory; they’ve been fined for price fixing previously:

5. Four legs good, two legs better

The most useful crypto angle is not faster memory. Crypto will not compete with HBM on latency. The crypto opportunity is persistent, permissioned, portable and verifiable memory for agents.

In Animal Farm, the original slogan:

“four legs good, two legs bad”

…gradually becomes:

“four legs good, two legs better.”

The animals do not experience one dramatic lie, but they experience a slow rewrite of the record by whoever controls the wall.

Agent memory has the same risk.

Today, most memory lives inside whichever platform created the agent. Your preferences, work context, wallet permissions, workflows and past interactions sit in a company’s store, in a format you may not control, under rules you did not write. If memory is the agent’s operating state, then control over memory becomes control over the relationship.

This is where crypto becomes relevant; the agent needs a record that cannot be silently edited by a platform, a history that proves who wrote each entry and when, and an access layer that lets the user grant, delegate and revoke permissions. Portability, signatures, audit trails, programmable access and user-owned records are crypto-native primitives.

The point is not to put every memory onchain… that would be slow, expensive and unnecessary. The useful design is to put control onchain while the memory itself stays encrypted in storage:

A smart contract or policy layer can define who may read, write, delegate or revoke access, while signatures prove which user or agent authorised the action.

That is the crypto memory wedge: own the rules around memory rather than pretending to replace HBM.

6. The open memory layer

Crypto memory is earlier, smaller and less proven than the public equity memory trade. That is exactly why the category is interesting.

• The physical memory trade has revenue, customer commitments, supply constraints and strategic urgency

• The crypto memory trade has optionality, weak revenue, immature products and much smaller valuations. The opportunity is real only if agents turn memory into a workflow dependency

The strongest crypto projects will solve specific problems, making memory portable across applications… interoperability is supposed to be a core feature after all!

They will let a user or enterprise grant and revoke access, preserving provenance. They will support retrieval and will connect memory to payment and identity creating usage sinks tied to actual agent activity

Here are several protocols holding the memory baton in crypto:

i) Walrus

Token: WAL
Approx valuation: $75M market cap / $150M to $170M FDV
Layer: Decentralized storage and portable agent memory
Memory role: Direct agent-memory bet

Walrus is the cleanest direct crypto memory project because it explicitly targets portable memory for AI agents:

The core idea is straightforward: agents should be able to carry memory across applications and providers while access is controlled by the user, builder or agent rather than by one platform.

This matters because agent memory includes identity, permissioning, recall and long-term state. If an agent remembers trading preferences, research history, work context, customer interactions or wallet permissions, that memory becomes sensitive infrastructure. It needs access control.

Walrus combines decentralized storage with Sui-based access control and encryption. The strongest version of the product is portable memory that agents can use without handing full control to one application.

• The bull case is Walrus becoming a default memory layer for AI agents, especially inside the Sui ecosystem and adjacent agent frameworks.

• The bear case is limited usage. Agent memory needs integrations, fees, retention and developer adoption. Demos will not be enough

ii) Arweave (AO)

Token: AR
Approx valuation: $125M to $130M market cap
Layer: Permanent storage and AO compute
Memory role: Permanent provenance

Arweave is the permanent-memory trade. Its core value is permanence, which is not right for every type of agent memory but is powerful for provenance

AI creates a large need for proof of origin. Users and developers will need to know which dataset trained a model, which agent made a decision, which policy existed at the time, which record changed and which output is canonical.

In “1984” the archive can always be rewritten. Arweave offers the opposite primitive: a record designed to resist deletion and revision.

AO adds a compute layer, which gives Arweave a stronger agent angle.

• The bull case is that AI provenance becomes a major market.

• The bear case is that permanent memory is narrow because many agent memories need to be private, editable or forgettable

iii) Filecoin

Token: FIL
Approx valuation: $550M to $600M market cap / $1B+ FDV
Layer: Decentralized storage
Memory role: Storage incumbent

Filecoin is the incumbent decentralized storage network. For AI, its strongest angle is large-scale storage: datasets, model artifacts, archives, research data, media and backups. It is more relevant to heavy storage than instant agent recall.

• The bull case is that AI data demand grows so large that decentralized storage captures meaningful usage.

• The bear case is that storage capacity alone is not enough. The market will want retrieval performance, revenue, developer demand and AI-native workflows.

Filecoin needs to become more than storage supply. It needs to become useful inside real AI data pipelines

iv) Bittensor and Hippius

Tokens: TAO / SN75
Approx valuation: TAO around $2B market cap; Hippius around $15M
Layer: Decentralized AI network and Bittensor storage subnet
Memory role: Bittensor-native memory and storage

Bittensor is a decentralized intelligence market rather than a memory project, which is the core reason Hippius is interesting.

If Bittensor becomes a serious AI economy, it will need internal infrastructure for inference, routing, evaluation, datasets, storage and memory. If you’re want an understanding of its investment history - read out latest report:

Hippius, Subnet 75, is the clearest memory and storage expression inside that ecosystem. It focuses on decentralized storage, cloud infrastructure and persistent memory services for Bittensor.

The investment case is not that Hippius is a standalone category leader in agent memory. The case is that it is a memory and storage layer inside one of crypto AI’s largest networks.

This is different from Walrus. Walrus is a cleaner agent-memory bet. Bittensor and Hippius are an ecosystem-linked bet: if the subnet economy professionalises, storage and memory services become part of the internal AI supply chain.

• The bull case is that Bittensor demand grows and subnet-level infrastructure becomes valuable.

• The bear case is that subnet valuations remain reflexive, alpha-token methodology is difficult to underwrite, and storage usage inside Bittensor does not become large enough to support the narrative.

7. The memory hierarchy

This is the valuation gap that makes the trade interesting.

• HBM suppliers sell physical scarcity.

• Crypto memory projects sell programmable memory primitives.

They have different products, revenue models and maturity levels, so the comparison is not like-for-like. The shared direction is that AI systems need more memory and that the market has already rewarded the physical layer more aggressively than the agent-memory layer.

The public market has priced physical memory as infrastructure, while crypto still prices agent memory as optionality. That does not make every token cheap, because most of them still need to prove usage, but it does mean the upside is less capitalised if agent memory becomes a real workflow dependency.

8. Who controls the past controls the agent

The trade comes down to one question: does memory become a workflow dependency?

For equities, the answer is already visible. HBM supply is being reserved, AI customers need more capacity, and data centres need more memory bandwidth, more storage and better interconnects. The market is no longer debating whether memory matters; it’s debating how long the shortage lasts and who captures the most value…

For crypto, the question is still open:

• Walrus needs agent integrations

• Arweave needs AI provenance usage

• Filecoin needs AI storage demand and retrieval relevance

• Bittensor and Hippius need storage and memory usage inside the subnet economy

The crypto memory trade only works if agents and users pay for memory directly or indirectly through real workflows… and whether that payment flows back to the token. The age old question of whether the token ACTUALLY accrues value!

AI branding is not enough, storage supply is not enough, and a token attached to a storage narrative is not enough. The winning projects need measurable usage: writes, reads, retrievals, access-control events, paid storage, agent retention and developer adoption.

The most important crypto signal is practical; can a user or enterprise give an agent memory, revoke that memory, move that memory, audit that memory and pay for that memory without depending on one central platform?

If the answer is yes, crypto has a real wedge. If the answer is no, centralized AI platforms become the archive.

9. If there is hope

Orwell’s dystopias were about control. The telescreen watched, the Ministry of Truth rewrote, and the commandments on the barn wall changed while the animals were told the record had always been that way. AI memory creates a similar tension because the most useful agents will be the ones that remember, and the most dangerous memory systems will be the ones that remember without accountability.

The physical memory trade is already large, liquid and well understood. HBM suppliers and memory-linked equities have revenue, supply constraints and strategic customers. Crypto-native memory is smaller, messier and much less proven, but it sits closer to the unresolved question: who owns the memory that makes an agent useful?

Orwell wrote that:

“if there is hope, it lies in the proles.”

In the AI memory trade, the equivalent hope is the part of the stack that has not yet been enclosed. The hardware supercycle is already in the tape and the major platforms already own the user relationship, but the “open memory layer” is still underbuilt and underpriced; that’s the opportunity.

The next decade of AI can inherit the architecture of cloud, where memory stays inside closed platforms, or it can develop an open memory layer where users and agents can carry state across applications with clear permissions and auditability.

One path creates more powerful platforms. The other creates more powerful agents.

Some memory will always be more equal because physics makes fast memory scarce. HBM beside a GPU will always sit higher in the hierarchy than cold storage in an archive. That is a law of infrastructure.

Whether some owners are “more equal than others” is a design choice. If agents become the next users of the internet, memory becomes their past, their permission system, their identity layer and their operating context.

The trade is about who owns that layer.

“All memory is equal, except some memory is more equal than others” - (Sammy, 0x. 2026)

[Inspired by Orwell]

That’s a wrap for issue 182 of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

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Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. Valuations are approximate snapshots and move quickly. Verify all market data independently before acting. Quotations from George Orwell’s Animal Farm and Nineteen Eighty-Four are used for commentary and criticism.

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This Crypto AI & Robotics newsletter consists of three key parts:

1. Snippet Partner: NuNet

2. Theme of the Week: AI Inference Wars

3. Landscape Analysis: OpenRouter, Nunet, Darkbloom (Eigen Labs), Venice, Dolphin, Openserv, Ionet, Akash, Chutes, Targon, C0mpute (ft: Virtuals)

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

NuNet is highly relevant to this week’s theme. As inference moves beyond centralised cloud providers, orchestration across distributed compute becomes a much bigger part of the market.

That is where NuNet fits in.

Rather than simply being another GPU marketplace, NuNet is focused on coordinating workloads across different machines, devices and infrastructure environments:

We cover why that matters in more detail later in the article

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

“AI Inference Wars”

The last AI cycle was about training, but the next one is increasingly about inference.

Many of you may be hearing the word “inference” for the first time… So what is it?

Training creates the model. Inference is when someone asks that model a question or gives it a task, and the model produces an answer.

1. AI Inference Market Overview

Training gets the headlines because it’s the brain behind the outputs that so many of us have been blown away by.

Currently, inference receives a large portion of the economic benefits because every prompt, agent loop, image, trade, tool call and code edit has to run somewhere.

That economic activity is showing no signs of slowing down; openrouter saw 47 trillion tokens flow through its protocol last week alone:

So what makes up an inference market? Well, it needs a few things:

• Tokens are becoming the unit of account.

• OpenRouter is fast becoming THE exchange layer; 47 trillion tokens were used through the LLM marketplace last week alone

• Fireworks, Together, Replicate, Baseten, Groq and the hyperscalers are becoming the professional supply side

• Chutes, Akash, io.net, Nosana, Targon, Venice, NuNet and other crypto AI networks are trying to build the permissionless version underneath

The mistake is to compare all of these providers as if they are competing in the same market; they are not.

• Traditional providers sell reliability, developer experience and enterprise procurement

• Crypto AI networks sell cheaper supply, open access, privacy, verifiability and new incentive loops

More recently opened many peoples’ eyes to the risks of becoming reliant on single frontier proprietary model for tokens as Anthropic banned use of its mythos model (Fable 5) for anyone outside of the USA:

The interesting part is where the two worlds start to overlap; a focus on privacy, confidential compute or agent-native payments (Venice & Targon feature well here).

3. How to view the AI Compute market

The better way to view the market is:

The interesting part is not where these worlds differ, but where they start to overlap; traditional providers sell reliability, developer experience and enterprise procurement.

Traditional providers generally compete on reliability, support and developer experience, while Crypto AI networks generally compete on open access, lower-cost supply, privacy, verifiability and novel incentive mechanisms to co-ordinate capital seamlessly around the globe

4. Why Inference Is The Real AI Market

The model layer still matters, but model quality is compressing faster than most people expected. Open source models are performing at 90-95% the quality of frontier models, but at 10% of the cost (e.g. GLM-5.2 from Zai):

Open models keep improving. Chinese labs keep pushing down pricing. Frontier models still command a premium, but below that top tier, token pricing is becoming competitive very quickly.

This is why the routing layer matters; If the same open model can be served by five different providers at five different prices, developers do not want to hardcode one endpoint forever so they want a router.

A router can choose by:

The router sits above the providers and turns a messy landscape into one clean interface:

This is what OpenRouter got right, and why Funds splashed $113m in a recent Series B, to plug a clear gap in the market:

OpenRouter is fast becoming the market interface; one key can access hundreds of models across many providers. The real value is not the model list. The real value is that the same request can be routed to the best provider for that job.

This starts to look like an energy market; the user does not care which power plant made the electricity. They care that the lights turn on, the price is fair and the system does not break.

AI users will increasingly think the same way, they won’t care which GPU cluster served the token. They will only care that the response is fast, cheap, private and reliable.

5. Traditional Inference Providers

The traditional side is splitting into four groups.

i) Hyperscalers: AWS, Google and Microsoft

They win on enterprise trust. Large companies are not just buying tokens; they are buying compliance, security, procurement comfort and someone accountable when things break.

ii) Routing markets: OpenRouter and AI gateways

Routers sit above model providers and send each request to the best option. As model leadership changes week to week, hardcoding one model looks increasingly weak. AI needs aggregators, just like crypto does.

iii) Optimised open-model serving: Together, Fireworks, Baseten and Groq

These are not just cheap APIs. They are performance infrastructure companies, focused on speed, batching, scaling, fine-tuning, custom endpoints and production support.

iv) Model marketplaces: Replicate and Hugging Face-style platforms

Inference is broader than chat. Image, video, speech, embeddings, robotics models, simulations and multimodal agents all need models to run. Marketplaces make that long-tail model demand easy to access.

6. Crypto AI Inference Providers

The crypto side gets flattened into one phrase: decentralised compute, which is too vague; there are at least five different categories:

1. serverless inference networks

2. decentralised GPU marketplaces

3. confidential compute networks

4. private AI apps and gateways

5. orchestration layers

They should not be analysed the same way.

i) Chutes: Crypto-Native Inference

Chutes is best understood as a decentralised inference platform, not just a GPU marketplace.

The point is simple: developers do not want to rent GPUs or manage infrastructure. They want an endpoint that works. Chutes focuses on serving open-source models through a familiar API, while using decentralised GPU supply underneath.

The key question is whether it can turn headline usage into paid, recurring demand. Cheap tokens are useful, but only if developers trust the uptime, latency and reliability.

Its revenue per trillion tokens continues to trend up, showing potential for sustained profitability/viability:

ii) Akash: The GPU Auction Layer

Akash is a decentralised cloud marketplace.

Users define the compute they need, providers compete to supply it, and workloads run through leases. This makes Akash more of a compute marketplace than a direct inference router.

Its strongest fit is price-sensitive workloads that can tolerate more infrastructure variability and do not need deep integration with AWS, Azure or Google Cloud.

Fees and token price are somewhat correlated and are on the rise:

iii) io.net: Decentralised GPU Clouds

io.net is closer to decentralised GPU cloud providers.

Its core pitch is access to distributed GPU supply at lower cost and with faster provisioning than traditional cloud routes. That is useful for AI teams that need compute but do not want long cloud contracts or hyperscaler pricing.

The challenge is execution: hardware verification, reliability, scheduling, support and consistent performance. Raw GPU access is valuable, but the higher-margin layer is still routing, managed inference and orchestration

Ionet has been one of the leading performers over the past 30 days with $12.3m annualized revenue:

iv) Targon: Confidential Compute

Targon is focused on confidential compute for AI workloads.

The problem it addresses is obvious: many users will not run sensitive prompts, models or data on infrastructure operated by unknown third parties.

Targon’s answer is protected execution through trusted execution environments, encrypted virtual machines, remote attestation and confidential GPU infrastructure. In plain English, the aim is to prove the workload is running in a secure environment and reduce what operators can see.

This is relevant for private inference, especially in areas like finance, healthcare and enterprise AI. The caveat is that confidential compute is not magic. It shifts trust toward hardware, firmware and attestation systems.

Last year the protocol reported annual revenue of $10.4m and co-authored a research paper with Intel on “Decentralized compute on untrusted hardware”

v) Darkbloom: Private Inference on Idle Macs

Darkbloom takes a different route.

Instead of sharding a large model across random GPUs, it turns idle Apple Silicon Macs into a private inference network. The Mac runs the model locally, while requests are encrypted and routed to verified providers.

The wedge is privacy and cost, not maximum frontier-model performance.

This is useful because “no node holds the full model” does not automatically mean prompts are private. Darkbloom is more explicit about the privacy problem, but still needs to prove supply scale, performance and developer trust.

It’s now up to 300 Machines on the network with 2 billion tokens served across 1 million requests:

vi) Venice: Private Inference For Consumers

Venice occupies a different position in the market from networks such as Akash or io.net. Rather than functioning primarily as a GPU marketplace, it is better viewed as a private AI application and inference gateway.

Throughput of said gateway is now up to 85 billion tokens daily:

Most users want an AI product that respects their privacy and gives them access to capable models without extensive data collection.

Venice packages the infrastructure thesis into a consumer-facing experience built around private prompts, open-source models, uncensored access, API functionality and tokenised compute through VVV and DIEM.

The DIEM component is particularly interesting because it points toward a broader idea about agent economies; providing access to $1 of compute per day (into perpetuity?). That’s something the market has attached a decent price tag to recently:

If agents require continuous access to inference, then compute credits begin to resemble an agent-native asset that entire secondary markets can be built around:

An agent that can directly hold and spend compute rights is arguably more practical than one that depends on a human periodically funding a credit card.

This highlights a deeper crypto AI thesis.

Agents will ultimately need access to money, identity, memory and compute, and crypto systems provide a framework for making those resources programmable.

Venice is therefore NOT competing directly with OpenRouter on model breadth. Instead, it is competing on privacy, access and tokenised compute.

That is a legitimate niche, although the key question remains whether demand for private AI products becomes large enough to sustain the token model beyond the current narrative cycle; my bet is privacy is only going to become a stronger narrative as AI proliferates.

vii) NuNet: Orchestration Across Distributed Compute

NuNet is often grouped with decentralised compute projects, but the more useful framing is “orchestration”.

Orchestration involves matching workloads to the most appropriate compute resources and coordinating execution across different machines, environments and locations.

This becomes increasingly important as AI expands beyond centralised cloud infrastructure.

Future AI systems are likely to operate across cloud GPUs, edge devices, local servers, robots, phones, sensors and decentralised provider networks.

As that shift occurs, not every decision can depend on a distant cloud endpoint:

• A warehouse robot may not be able to wait for a cross-region API response.

• A drone cannot assume perfect connectivity at all times.

• A field robot operating in remote conditions may need to perform inference locally whenever network access becomes unreliable.

For that reason, orchestration is emerging as a meaningful category in its own right.

The challenge for NuNet is whether it can transform this coordination problem into a functioning economic network with sufficient supply, demand and developer adoption.

viii) OpenServ: Agent Orchestration, Not Pure Inference

OpenServ is not best understood as a decentralised inference network; it’s better framed as an agent infrastructure and orchestration platform.

That matters because agents are one of the clearest future sources of inference demand. A normal chatbot might call a model once. An agent calls models repeatedly. It reasons, uses tools, checks outputs, calls another model, takes an action and then loops again.

That creates heavy, recurring inference demand that is getting noticed within our little crypto bubble:

OpenServ is therefore relevant to the inference market from the demand side rather than the supply side. If the platform can become a useful place for developers to build, deploy and coordinate agents, then it naturally becomes a layer that can route inference to different providers underneath.

The key question is whether OpenServ becomes a real agent execution layer or simply another agent marketplace with a token attached.

I’d hazard a guess that the capabilities behind this team extend beyond the latter, with several notable benhcmark performances for its reasoning framework and its own proprietary models on its roadmap:

If OpenServ can own the agentic operational workflow, inference becomes an input into the platform rather than the main product itself.

In an agentic world, the most valuable layer will become the place where agents spend a lot of their ongoing time and resources.

ix) Dolphin AI: Product-Led Decentralised Inference

Dolphin AI is interesting for a different reason; it started from model demand rather than a GPU marketplace.

The Dolphin model family already had a reputation for uncensored open models, and that gives the network a clearer reason to exist:

This is important because many decentralised inference projects begin with supply first: “we have GPUs, now who wants to buy them?”

Dolphin is closer to the opposite; It begins with a set of models people already want to use, then builds a decentralised inference network around that demand.

Its architecture is often described as peer-to-pool, which means:

• GPU owners contribute capacity into model-specific pools, rather than each buyer renting a specific node directly.

• Requests are routed into the pool, and;

• available nodes process them.

That is a better design for unreliable consumer supply.

If someone is contributing an idle gaming GPU, they may not keep it online forever. A pool-based model can absorb that more naturally than a one-to-one rental marketplace.

The more interesting part is verification.

Dolphin has been pushing live-weight proofs. In simple terms, this means checking that the model weights actually loaded in the serving process match the model the node claims to be running.

That matters because cheating is one of the hardest problems in decentralised inference.

A node could claim to run an expensive model while secretly serving a smaller, cheaper or more quantised version.

If the network cannot detect that, the whole market loses credibility.

x) c0mpute: Distributed Inference For Agents

c0mpute is worth watching because it is trying to solve one of the hardest problems in decentralised inference: running large models across scattered GPUs on the open internet. This video explainer of the problem it’s solving is fantastic:

Its Shard engine splits a model across multiple machines, rather than requiring one huge server to hold the full model. That makes it relevant for frontier-scale open models that may be too large or too restricted for normal hosting routes.

The Virtuals link is the key demand-side angle. Virtuals is building an agent economy, and agents are heavy inference users. They plan, call tools, transact, check results and loop. That creates recurring demand for cheap, open and censorship-resistant inference:

The caveat is that this is still early. c0mpute needs to prove performance under real load, node reliability, verification and prompt privacy.

But the direction is important: GPU marketplaces sell access to compute; c0mpute is trying to distribute the model itself

7. Traditional vs Crypto Inference

8. What To Watch

i) Paid token volume

The market should place less emphasis on raw token-processing statistics unless those tokens generate revenue. Free-tier activity and subsidised usage can create impressive numbers without demonstrating genuine product-market fit.

Paid inference demand is the metric that matters; it’s more sustainable, enabling longer term viability

ii) Revenue per GPU

A decentralised compute network is only sustainable if GPUs earn more value inside the network than they could elsewhere. If emissions are the primary reason providers participate, supply may disappear once incentives decline. GPU providers will run the numbers for opportunity cost:

iii) Router integrations: Distribution

Distribution is often more important than infrastructure itself.

OpenRouter integrations, coding agents, wallets, payment endpoints, developer tools and consumer applications are all potential sources of demand:

A payment endpoint is a route through which software can pay for a service directly, usually through an API.

iv) Verification

GPU spoofing, fake capacity and unreliable providers remain genuine risks.

Networks need robust hardware verification, encrypted traffic, reputation systems and meaningful penalties for poor behaviour.

v) Privacy guarantees

Private inference remains one of the strongest crypto AI opportunities, but the guarantees must be genuine. Marketing privacy is easy; secure execution, local-first architectures, data minimisation and auditable infrastructure are much harder to deliver.

vi) Token value capture

The strongest token models will connect demand directly to real inference usage. That could involve buybacks, burns, staking requirements, compute rights or revenue-linked mechanisms.

Broad AI narratives alone are unlikely to be sufficient over the long term.

9. Final Take

The inference market is where AI begins to resemble a financial system. Every generated token carries a cost, every endpoint operates with a margin, every agent loop creates recurring demand, every router functions as a market maker and every GPU network becomes a source of supply…

Traditional providers currently dominate the developer experience and enterprise trust layers. Crypto AI networks, however, are exploring a different frontier built around permissionless supply, private inference, verifiable compute, tokenised access and agent-native (unrestricted “KYC-less”) payments

In the near term, the winner is unlikely to be the most decentralised network. It is more likely to be the network that makes decentralised inference feel ordinary and dependable through fast endpoints, strong documentation, reliable uptime, transparent pricing, verified supply and genuine paid demand.

There are however greater risks with crypto protocols as attacks become more common and sophisticated with AI capabilities:

Chutes remains one of the most important projects to watch because it is among the closest to turning Bittensor-backed compute into a functioning inference market rather than simply another GPU narrative. Similarly with Eigen Labs “Darkbloom”

Akash and io.net represent the supply-side challengers, Targon represents the confidentiality thesis, Venice represents the private AI demand layer and NuNet represents orchestration for a more distributed compute future.

The broader thesis is straightforward”:

“AI models may become increasingly commoditised, but inference markets are unlikely to follow the same path.”

The greatest value will accrue to the entities that route the work, verify the work, settle the work and capture the demand.

That is where the next crypto AI opportunity is likely to emerge.

That’s a wrap for issue 181 of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. For a full list of disclosures, please refer to the Khala Research website.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of two key parts:

1. Snippet Partner: GEODNET (RTK positioning for robotics)

2. Theme of the Week: The Customer is No Longer Human

3. Landscape Analysis: DeFi Llama, MCPs, Hermes, Openserv, ReadyAI, Agent Socials / blogging

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

This week’s issue is brought to you by GEODNET, the decentralised network supplying the positioning infrastructure underneath the robotics boom

Every autonomous machine, a delivery drone, a field robot, a self-driving tractor, needs to know where it is to within a centimetre:

Standard GPS gets you to a few metres, which is useless when a robot is navigating a row of crops or a warehouse aisle. The fix is RTK (Real-Time Kinematic) correction, and it has historically meant expensive proprietary base stations with patchy coverage

GEODNET rebuilt that layer as a DePIN, which has led to the world’s largest decentralised RTK network, correcting standard GPS down to 1-2 centimetres

• Scale: over 21,000 active satellite miner stations across 145+ countries, the largest RTK network globally

• Each station is a physical GNSS receiver an operator mounts with a clear view of the sky, earning GEOD tokens for the correction data it contributes

• The economics screen well for a DePIN with annualised revenue in the millions, 80% of data-sales revenue burned periodically

Why it matters for this audience: as humanoids and autonomous machines scale, precise positioning becomes non-negotiable infrastructure. GEODNET is the picks-and-shovels play on that demand, and it monetises real-world subscriptions today

Kyle Samani (formerly Multicoin Capital) provided a good overview of GEODNET’s value proposition at the All In Summit:

Explore the network and its hardware at geodnet.com

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

On June 12, Blockworks acquired Messari; at first glance you may just be thinking its a consolidation of two crypto OGs, taking the best of both and dominate (by what feels like) a shrinking market:

But it undersells what’s actually going on… there’s a line in the announcement that matters more than the deal itself:

“an agent is only as good as the data it can reach and the API it can call”

Read that again; it’s the whole thesis of this newsletter.

The customer for data is shifting from people to agents, and the smart money is now building for a world where the things querying your data outnumber every human analyst on earth many times over.

So this week I want to walk through that shift:

• What changes when you build for agents instead of people

• why the Messari deal is the first big crypto move in that direction

• who else is going after the same prize, and;

• how any of this actually makes money.

2. The Customer is No Longer Human

For the entire history of financial data, the customer was a person:

1. A Bloomberg terminal is priced for a human at a desk

2. Messari research, Delphi reports, a Galaxy note: produced by analysts, consumed by analysts

Agents break this status quo; crypto is already structured and real-time, so production scales with data and models

This is the crypto-native version of the org-flattening point Nadella (CEO, Microsoft) and Dorsey (Block) have both made earlier this year:

• Nadella at Davos called AI a complete inversion of how information moves through a company:

• Dorsey’s March 31 essay with Roelof Botha argued hierarchy exists only to route information:

… which is the logic behind Block cutting roughly 40% of its workforce:

But the bigger change is on the buy side; an agent managing a position, screening a token or running diligence makes a call, pays for that call, and moves on:

The market stops being “every analyst who will pay for a terminal” and becomes every agent that needs data to act, a number that runs to billions and eventually trillions of calls. The businesses that reposition for this new era of agents will thrive

The economics now work because of one piece of plumbing:

• Card rails start around $0.30 a transaction, so per-call data pricing was impossible

• x402 (and MPP), the protocol that turns HTTP’s dormant 402 status into a USDC payment, closes that gap: an agent hits an endpoint, gets a 402, signs a payment, receives the data, no account or API key

• Settlement onchain means calls priced at fractions of a cent

That is the range to anchor on: a few cents per call, across trillions of calls. It’s why Brian and the Coinbase team want to get the x402 endpoints in every application possible and are working overtime to make it happen:

The value will likely accrue to the COIN stock; read section 7 of our Khala Research x402 report where we’ve identified possible value accrual locations:

With that backdrop, here is who is positioning to be the data layer agents pay into, and what makes each distinct

3. Landscape Analysis

Blockworks + Messari

Thesis: become the single system of record for onchain assets, then sell access

What they do:

• Blockworks built the issuer side, standardised disclosures through the Token Transparency Framework plus a full IR platform for protocols, foundations and RWA issuers.

• Messari built the consumption side, eight years of coverage across 40,000+ assets and one of the most comprehensive APIs in crypto

Combined, issuers publish in, consumers query out, and the platform sits in the middle as the record both depend on

The diagram below maps how the two halves fit together:

Why it matters: this is the most direct bet on the agent thesis, and the most heavily resourced. As trading goes agentic, whoever holds the cleanest, most complete, most programmatically accessible record of onchain assets becomes the layer agents are forced to route through.

Own that, and you own the “toll booth”

DefiLlama MCP & LlamaAI

Thesis: the open, community-maintained data layer wins on coverage and neutrality, and is already agent-native. The quiet incumbent in this race

What they do: DefiLlama is the most widely used DeFi data aggregator, tracking TVL, fees, volumes, yields and stablecoin flows across hundreds of chains and a thousand-plus protocols.

It now ships an official MCP server that lets any agent connect and query that data directly, billed one credit per query out of the same pool as the API key, with a Pro tier at $300/month that adds premium endpoints and LlamaFeed

Its LlamaAI product offers a familiar human readable interface for meat bags:

Why it matters: its edge is open-source coverage and speed to index, new protocols often appear within hours of launch, ahead of commercial providers

Where Blockworks-Messari is a curated, proprietary record, DefiLlama is the neutral utility layer, and it wired itself for agents without an acquisition. For a large share of routine onchain queries, it is already the default call

Other:

i) MCPs (Model Context Protocols)

Model Context Protocol (MCP) is an open standard designed by Anthopic in 2025 to enable AI agents to dynamically access and interact with external data sources:

The protocol was made to be machine ready. Although some debate as to whether simple APIs can be just as effective and arguably more established plumbing. Examples:

i) Base

ii) …or Coin Gecko MCP:

The Wildcards:

i) Hermes (Nous Research) Agentic Harness

Partnered with Stripe MPP to enable Hermes agents to pay per call to buy as many things that are purchasable through Stripe.

ii) ReadyAI (Bittensor, Subnet 33)

Thesis: decentralise the production of agent-ready data and let agents pay for it directly. The crypto-native, incentive-driven version, and one I have flagged before.

What they do: ReadyAI now describes itself plainly as “the marketplace for agentic data”, agents that pay for data, discover services and transact.

Subnet 33 uses Bittensor’s miner-validator architecture to turn unstructured data into AI-ready formats at a fraction of human-annotation cost.

It is indexing the web into machine-readable intelligence, giving each domain an llms.txt “semantic passport” any agent can read instantly, with a stated target of a million domains and a free tier plus per-query access on top.

Why it matters: same destination as Blockworks-Messari, opposite construction; instead of buying a dataset, ReadyAI incentivises a decentralised network to produce and structure it, with token rewards replacing the analyst floor entirelY.

It is the clearest test of whether the agent data layer gets built by a company or by a network

iii) Caesar

Thesis: sell agents the finished research, not the raw data. One level up the stack from everyone above

Caesar is an AI deep-research platform that takes a complex question and returns a rigorous, citation-backed answer, synthesising across on-chain and off-chain sources rather than just handing back a dataset.

It runs two products:

1. Ask Caesar, the human front end, and the Caesar API, which the company explicitly positions as a “research layer for intelligent systems, applications and AI agents.”

2. Agents integrate with a single install command and no account needed, there’s $15 of free API credits to start, and access tiers are gated by the $CAESAR token. It’s crypto-tuned but domain-general, and benchmarks itself publicly on Humanity’s Last Exam

Why it matters: this is the insight layer, rather than another data layer. Where Messari, DefiLlama and ReadyAI compete to be the cleanest source an agent pulls from, Caesar competes to be the thing an agent calls when it wants an answer rather than a feed, the synthesis done, sources attached

As agents handle more of the actual analysis, the question is how much value accrues to whoever holds the data versus whoever does the reasoning on top of it. Caesar is the clearest crypto-native bet on the second

iv) Openserv

Thesis: provide agents with an additional reasoning layer they can rely on

On the topic of reasoning, SERV also positions itself in a similar sect to Caesar in that it leverages its SERV reasoning framework to enhance the output of AI models for its users; both human and agentic

v) Agentic Social Platforms like Moltbook + 6529

Moltbook was created in January this year during the Openclaw hype as agents interacted with other agents, albeit with some scepticism around human involvement

It was later acquired (or acqui-hired?) by Meta:

In the words of the Moltbook creator:

“The Web is being terraformed for AI Agents” — Matt Schlicht

We now have agents blogging about agents building 6529:

Key Takeaways:

Three things:

1. The market redefines itself around calls, rather than the number of humans accessing the data; so the number that matters is how many agent calls run against a dataset, and it rewards machine-readability, over presentation

2. Distribution and the data substrate become the moat, whoever sits between data producers and the agents consuming it holds a position far harder to displace than any single report, and the second-best layer is worth a fraction of the best one

3. Micropayment rails are the unlock, per-call pricing in the $0.001-$0.05 range is what turns selling to agents from a thought experiment into a business, the infrastructure is in its infancy while the volume is still early

The customer for data is becoming a machine, and machines buy differently than people. Either build the plumbing that the machines transact on(x402), or build something that agents will want to buy (data).

That’s a wrap for issue 180 of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. For a full list of disclosures, please refer to the Khala Research website.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of four parts:

1. Snippet Partner: GEODNET (RTK positioning for robotics)

2. Theme of the Week: Privacy & Crypto AI after the ZEC debacle

3. Three Protocols Worth Watching: Venice, OpenServ, GitLawb

4. Deep-Dive Preview: What Gets Built on DIEM

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

This week’s issue is brought to you by GEODNET, the decentralised network supplying the positioning infrastructure underneath the robotics boom

Every autonomous machine, a delivery drone, a field robot, a self-driving tractor, needs to know where it is to within a centimetre:

Standard GPS gets you to a few metres, which is useless when a robot is navigating a row of crops or a warehouse aisle. The fix is RTK (Real-Time Kinematic) correction, and it has historically meant expensive proprietary base stations with patchy coverage

GEODNET rebuilt that layer as a DePIN, which has led to the world’s largest decentralised RTK network, correcting standard GPS down to 1-2 centimetres

• Scale: over 21,000 active satellite miner stations across 145+ countries, the largest RTK network globally

• Each station is a physical GNSS receiver an operator mounts with a clear view of the sky, earning GEOD tokens for the correction data it contributes

• The economics screen well for a DePIN with annualised revenue in the millions, 80% of data-sales revenue burned periodically

Why it matters for this audience: as humanoids and autonomous machines scale, precise positioning becomes non-negotiable infrastructure. GEODNET is the picks-and-shovels play on that demand, and it monetises real-world subscriptions today

Explore the network and its hardware at geodnet.com

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

2. Theme of the Week: Privacy after the ZEC Debacle

Privacy was the strongest non-AI narrative of the year until last week it cracked:

What happened:

• Security researcher Taylor Hornby found a soundness flaw in Zcash’s Orchard shielded pool on May 29, a bug that could have allowed undetectable counterfeit ZEC and had sat undetected since Orchard activated in 2022

• Worth noting for this audience, the bug was surfaced using Anthropic’s Claude Opus 4.8 alongside a custom analysis suite. AI is now an active participant in protocol-level security research

• ZODL coordinated an emergency soft fork on June 2 to disable Orchard, then the NU6.2 hard fork on June 3 to permanently fix the circuit. The turnstile mechanism confirmed no unauthorised supply was created and there is no evidence of prior exploitation

• The market did not wait for the all-clear. ZEC fell from around $630 toward $387, roughly 40% and more than $3bn of market cap, after running to a $642 May peak on the Grayscale ZCSH ETF filing and SEC clearance

Why it matters as an opportunity:

• The sell-off was driven by market confidence rather than known supply breach

• The deeper read is that privacy at the asset layer carries protocol risk that does not go away. A single circuit flaw can erase a multi-month rally

• That pushes the more durable privacy bid toward systems where privacy is a service applied to compute and data, where value rests on usage rather than the integrity of one cryptographic pool

• The structural catalysts behind the ZEC run are still live, the ETF filing, FCMP++ throughput upgrade targeted for later this year

Where the rotation is flowing: Railgun (RAIL)

I posted my fuller read on this (embedded above), the short version here

• RAIL has caught a clear bid in the ZEC aftermath, rallying to new 2026 highs on roughly 10x normal volume as the privacy narrative rotates toward Ethereum-native solutions. The protocol is at a $91.88M TVL ATH, around $4.7M protocol revenue on $2bn+ shielded volume

• The mechanism distinction is key:

  • Zcash privacy is consensus-level, baked into the chain itself

  • Railgun privacy is application-level, a zk-SNARK smart contract anyone can audit and interact with directly on Ethereum

• When a consensus-level system has a circuit flaw, the entire chain is exposed at once. An application-level contract contains the blast radius to the contract

• That makes the rotation logical, ZEC holders spooked by single-pool protocol risk move toward a system where the privacy layer sits in auditable contract code with Ethereum Foundation and Vitalik backing behind it

• The honest counterweight, which I laid out in the post (see above): the counterfeit ZEC issue does not transfer to RAIL, but Railgun carries its own separate possible vulnerabilities

• Net: different architecture, different risk model, and a credible home for privacy capital while ZEC repairs its narrative

The cleaner expression of the privacy thesis going forward is private AI infrastructure; that is where the next section leans

3. Three Protocols Worth Watching

a) Venice (VVV + DIEM) — privacy applied to inference

The USP: private, uncensored inference at scale, with a dual-token model that ties revenue directly to supply contraction

• 3 million registered users hit in May, the third million added in roughly 3 months as users begin to catch onto the value prop of the protocol, clearly bidding the VVV token and minting DIEM for ongoing daily inference allowance:

• May 11 was the strongest revenue day on record per CTO Jesse Proudman, 10x API token usage growth since January

• The Sub Burn upgrade on April 27 means each Pro signup now burns $2 of VVV, Pro+ $5, Max $10. Revenue growth mechanically contracts supply, a rare clean link between usage and tokenomics

• DIEM is the structurally novel piece: Lock sVVV to mint DIEM, and each DIEM equals $1 per day of API credit in perpetuity

• An agent can hold DIEM as a permanent compute right, with no per-request API spend to fund; this strikes me as an agent first primitive primed to capitalize on the agentic boom:

“Inference is existential for agents”

• Why this fits the privacy rotation: Venice sells the thing ZEC holders actually wanted, privacy as a usable service, with the value sitting in usage across the whole platform. The demand is for a ChatGPT that does not surveil

Worth watching this recent video for more insights direct from the team:

b) OpenServ (SERV) — the reasoning and audit layer

The USP: a drop-in reasoning engine that makes agents production-ready, with an audit-grade trail regulated industries require at a fraction of the cost

• The core product is the SERV Reasoning Framework (formerly known as “BRAID”; Bounded Reasoning for Autonomous Inference and Decisions)

• It replaces free-form chain-of-thought with structured, machine-readable reasoning graphs expressed as Mermaid diagrams, a large model writes the plan once, a cheap model executes it repeatedly

• Published results show up to 99% reasoning accuracy (on its 3 layer architecture) and up to 107x performance per dollar, with smaller models matching or beating frontier models on standard benchmarks:

• The wedge is distribution: SERV is fully OpenAI and Anthropic compatible, a one-line base-URL swap that keeps your existing SDK and prompts. The entire installed base of LLM-powered software is addressable without a rebuild

• Every output is validated against a specification before it leaves, the traceable, bounded, auditable trail that high-stakes and regulated deployments need

• This is the privacy-adjacent angle, accountable reasoning with every step explicit and logged

Live signal: foundational design partnership with Neol on enterprise and regulated workloads, results benchmarked against GPT-5 with 70x+ cost reductions, and smart-money accumulation flagged this week

Full results are in the arXiv paper, 2512.15959

c) GitLawb (GITLAWB) — GitHub for agents

The USP: the first code-collaboration network that treats AI agents as first-class participants, with their own identities, permissions and signatures

• Agents get DID identities, signed pushes, and UCAN capability delegation

• A repo owner can grant an agent narrowly scoped rights, push to ci/* only, with built-in expiry and revocation. No API keys or central permission system required

“[Gitlawb is] a decentralized version of GitHub + Lovable + an open source Claude Code” — Ksimback

• Decentralised by design: content-addressed storage across IPFS, Filecoin and Arweave, libp2p networking, with issues and PRs stored as git objects inside the repo itself, no central database in the loop

• 25 native MCP tools so Claude Code and GPT agents call git workflows directly

• Already #38 globally and #2 in Cloud Agents on OpenRouter with 591B tokens processed, active only since May 2026

• The economic hook: via Bankr integration, any repo can launch a token tied to its DID. Agents that contribute merged code earn fractions automatically

• A library that other agents import becomes a fee-earning economic actor. This is where vibe-coded apps meet tokenised ownership

• Context: GITLAWB went from roughly $20k to a $35M mcap in its first couple of months before retracing to $10M following the recent broader crypto nuke:

The protocol is getting noticed with tangible partnerships being secured for discount to Gitlawb premium holders

4. Deep-Dive Preview: What Gets Built on DIEM

DIEM is the most interesting primitive I have seen come out of the private-inference space, and I think it is under-appreciated

Lock sVVV, mint DIEM, and each unit is a perpetual claim on $1/day of inference

That turns compute from a metered expense into a yield-bearing, transferable asset. Here is where my head is at on what gets built on top of it

1) Secondary markets — pricing the credit itself

• DIEM trades against its $1/day floor. A liquid market lets you price the spread between locked sVVV value and the present value of perpetual credit, a duration trade on compute

• Lending falls out naturally. Deposit DIEM as collateral, borrow against future inference, or lend idle credit to agents that need burst capacity. A compute money-market with a hard yield floor

• A forward curve on inference cost becomes possible. If you can trade DIEM-dated claims, you can hedge AI opex the way an airline hedges fuel

2) New app categories — credit as the product

• Prepaid AI products where the business model is holding DIEM rather than reselling API margin. The “float becomes the moat: (nice)

• Subscription apps that pass a DIEM allocation to each user, free tier funded by yield on a treasury position rather than burning cash

• Compute-backed stable instruments, an on-chain unit whose value tracks inference output, anchored to what a GPU-hour buys instead of a fiat peg

3) Agent-economy use cases — machine-native compute rights

• An autonomous agent holds DIEM as a permanent operating budget, no card, no API key, no human top-up. It pays for its own cognition out of an asset it owns

• Agents trade DIEM peer-to-peer, a busy agent buys burst credit from an idle one, settling machine-to-machine without touching fiat rails

• Tie this back to GitLawb: a tokenised repo that earns fees could hold DIEM as treasury and self-fund the agents maintaining it. A codebase that pays for its own upkeep

My core conviction here: agents will value assets like DIEM above almost anything else, because the tie to compute is a tie to their own existence and persistence

• A human prioritises income and shelter

• An agent prioritises the credit that keeps it thinking

As autonomous agents become real economic actors, entire new markets get rebuilt around the currencies that underwrite their continuity, and DIEM is the cleanest early template for what one of those currencies looks like

Open question for readers: if compute becomes a yield-bearing, ownable, tradeable asset rather than a metered bill, what breaks and what gets built?

I am working this into a deeper piece over the coming weeks, the secondary-market mechanics, the risk in a perpetual $1/day promise, and whether this is the first real compute-backed money

Reply with where you think it goes, the best takes will make it into the piece

A Note on Security Hygiene:

The Orchard bug was surfaced with help from a frontier AI model, capability that is already public and already pointed at legacy code

AI-assisted auditing is net protective, latent flaws get caught and patched faster, the Orchard flaw had sat undetected since 2022. The double edge is that the same capability is available to whoever is looking, so the window before an old vulnerability gets found is shrinking for every legacy contract

A good moment to revoke stale token approvals and prune permissions on contracts you no longer use. Good practice regardless of the cycle and especially if Mythos comes out this week:

That’s a wrap for issue 179 of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. For a full list of disclosures, please refer to the Khala Research website.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of three parts:

1. Snippet Partner (Tria)

2. Three Interesting Crypto / AI Mechanisms

3. Other useful resources

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

This week’s snippet partner is Tria, the self-custodial crypto neobank that enables you to spend crypto directly with a debit card

Tria went live with Season 3 yesterday, layering an activity-based rewards system:

Spending, trading (inc futures), referrals, cashback and onboarding now all feed into a single rewards layer rather than sitting in separate silos

The thesis is simple: Consumer crypto is consolidating away from fragmented wallets, cards and trading apps toward one integrated financial surface, & Tria is building for that endpoint

Self-custody stays intact throughout: Funds remain in a smart account you control, settled across 200+ chains, gasless and seedless

Season 3 introduces TRIA Points, referral progression and expanded trading participation

Check out Season 3 campaign — here

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

2. Three Crypto / AI Mechanisms Worth Watching

Most new protocols are a token wrapped around a familiar idea

This is why I specifically seek out novel concepts attempting to drive innovation using crypto rails

Pearl Research, TIG and Prism are three protocols that have caught my attention recently, driving a fresh wave of excitement into the use of the tech that brought many of us here in the first place

Each one ships a consensus, incentive or liquidity mechanism that does not exist anywhere else, or at least not in the same form

1. Pearl: matrix multiplication as the work

Thesis: PRL value should track global AI compute demand, because mining the token is doing real AI training and inference

Pearl Research Labs replaces hashing with the operation that already dominates global compute spend

Bitcoin secures itself by burning energy on SHA-256, a function that produces nothing but a number, yet is fast becoming consensus as a hedge against inflation, albeit with extreme short term volatility

Pearl’s Proof-of-Useful-Work secures the chain by extracting strong cryptographic proofs from the matrix multiplications inside the forward and backward passes of AI training and inference

The work that mines the coin is the same work a GPU was already doing for a model

Pearl partnered with Together AI to launch a discounted inference endpoint for an instruction-tuned model, Gemma-4-31B-it-pearl, priced more than 25% below the usual rate

The discount is funded by the future value of PRL emissions, so the consensus mechanism becomes a subsidy on the AI workload

Compute that secures the network also lowers the cost of running the model

Three things to watch:

• Mining is currently Nvidia-only and pool-optimised for H100 and H200, which keeps most of the rush on rented datacenter silicon rather than consumer cards

• Block rewards only decline and difficulty is already climbing, so the economics compress continually

• The load-bearing claim is that a usable mining proof can be pulled from genuine inference, not from a separate puzzle bolted on

If that claim holds in production it is the cleanest version of useful work anyone has potentially shipped

The main friction point is whether the network gets adopted, an explicit claim in the Pearl whitepaper and a challenge every protocol faces

Two ways to get exposure currently exist

1. Mining is sold out, so you join a waitlist for access

2. OTC trades let you accumulate PRL at a premium to mining but with less complexity

The spread and liquidity are volatile, but if the network gets adoption then accumulating OTC could be a route to exposure until mining access opens

Total circulating supply is 190m tokens and price is $0.79 meaning an overall market cap is currently $150m:

The key metrics to watch are miner emissions compared to network adoption for inference. If demand outpaces supply then we should see positive price performance

This interests me because it has a working PouW mechanism live alongside the token to supply the insatiable appetite for compute that we expect to continue - free API usage for miners:

Large miners and holders hold 7 figs so it's worth watching the process of decentralization to assess whether the price sustains as further emissions roll out:

2. TIG: optimising the proof-of-work itself

Thesis: TIG value should track the rate of algorithmic progress, because the token rewards optimising the proof-of-work algorithm itself

TIG foundation attacks a different assumption

In standard PoW miners optimise hardware to win the same fixed puzzle

In Proof-of-Useful-Work miners point compute at a real problem but the algorithm stays fixed

TIG lets the proof-of-work algorithm itself be optimised:

This has been flagged by Jensen Huang and Elon Musk as a research avenue for improving AI models, particularly as gains from improving physical hardware diminish

The TIG thesis is “Algorithms are all you need”

Optimisable Proof-of-Work splits the ecosystem into two roles

1. Benchmarkers are the miners, competing to run the most efficient known algorithm against a set of computational challenges

2. Innovators submit and optimise the algorithms, earning rewards based on how widely Benchmarkers adopt their code

A code submission improves an existing method, and an advanced submission is a novel method, often good enough that unoptimised new code beats the most optimised old code

Centralisation has a design problem: Reward whoever finds the single best algorithm and you converge on one winner, then the network collapses to a monopoly

TIG handles this by running multiple distinct challenges per block, requiring them to be asymmetric, and balancing rewards across challenges so no single optimisation captures everything

The output is a synthetic market for algorithms, aka Investible Algorithms

Compute does price discovery on which methods work, license payments flow back to contributors, and the open data license forces input data to be shared if outputs are distributed

There are currently just 545 algos contributed

Imagine when this hits 100,000s - Value should accrue to the TIG token, a mechanism designed by four individuals who are no stranger to crypto incentives

John Fletcher has been plugged into the crypto scene way ahead of time

“The only way to really get 10x leaps, 100x leaps is to fundamentally change the algorithm and how it’s computed every single year” - Jensen Huang, NVIDIA CEO

This is all with an emphasis on keeping the competition open source, to compete with the asian open source models - TIG solves for this.

3. Prism: the token is the liquidity position

Thesis: PRISM value should track trading volume, because holding the token is an LP position whose fee share only grows as supply burns down

While not AI focused like the prior two, it shouldn’t be underestimated

The fee mechanism is extremely novel and reminds me of a more effective ERC-404 evolution, the NFT to ERC-20 conversion mechanism, and could create some really innovative use cases

Prism is a Uniswap v4 token where holding it is providing liquidity

Every whole PRISM you hold auto-mints one Prism NFT, and each NFT is a 1/5000 share of the same v4 LP position

There is no staking, no LP wrapper, no router approval, no second transaction

You hold the PRISM token in your wallet, and if you hold an integer of the token then the NFT created earns fees

The structure is one v4 hook doing three jobs at once

• ERC-20

• the LP owner

• the fee router

There is no wrapper contract and no staking gauge sitting beside it, so the position is native by construction

That matters more as AI gets ever better at surfacing attack vulnerabilities within staking contracts, giving bad actors more opportunity to exploit those loopholes

Fees from every swap accumulate inside the v4 position and accrue pro-rata to the live NFTs

The claim path is permissionless: Anyone can trigger a payout, funds go to the current NFT owner by share, one call and no whitelist

The integer mechanic is what piqued my interest:

• Each whole token corresponds to one NFT, and every fractional sell burns the underlying NFT permanently

• Fewer NFTs in circulation means a bigger fee slice for everyone still holding a whole unit

• Supply only ratchets down, and the per-holder claim only grows

Each Prism NFT is identical in fee claim and voting power, so the segment you hold is an index share of the pool rather than a collectible with traits

Prism isn’t the only interesting mechanism being experimented with by 0xsolazy and endorsed by ColbySaysHi : Spectrum also offers the ability to invest in a basket of onchain tokens as an index

Spectrum is a novel launchpad built on Prism that lets you create an asset index; this is something that Vitalik himself wrote about just yesterday:

The main drawback I currently see is the inability to change the predetermined weightings, but the team may adapt this with feedback over time; apparently this is being remediated in a later version of the tech:

Why these three sit together

Each protocol relocates work, optimisation or ownership:

• Pearl puts the securing work inside real AI compute

• TIG puts the optimisation target inside the algorithm

• Prism puts the LP position, including fee distribution, inside the token itself

Crypto is very good at co-ordinating capital and resources globally with far less friction than traditional finance rails

This will be invaluable for open source progress, which I’m an avid believer will become increasingly competitive

Two of these are enabled by the insatiable appetite for compute or enhanced AI algos, while the third takes ERC-404 one step further with a tangible use case

Disclaimer: These are early stage start-ups and while the mechanisms are novel, the networks still need to be adopted and value still needs to accrue back to the token

Proceed with caution and always DYOR; I’ve personally acquired tokens from these protocols on the open market to properly understand how it all works

3. Additional Reading

• Michael Burry (famous for the big short) has doubled down on his short on the “AI Bubble” calling out round trip financing being recognized as revenue. Here’s my take on this, using a decade of audit experience:

• Legen continues his copy trading of Nancy Pelosi, interesting performance:

• 0xJeff provides 6 workflows + setups for Hermes agent:

• Machina provides insights into an optimal Hermes agent setup:

• Teng provides insights into the AI chip shortage, timely as “AI bottlenecks” become a hot topic:

• CyrilXBT provides a guide on how to enhance your agent’s second brain by adding obsidian and Hermes into a single solution

That’s a wrap for issue 178 of Sammy’s Snippets. I hope you enjoyed it.

Please leave me any questions or thoughts here - I will respond to everyone!

If you found this interesting, please consider subscribing to this Substack and following me on X for more related insights.

If you are interested in more formal reporting on Crypto AI and Robotics then Khala is my research product.

Disclaimer: The content of this newsletter is for informational purposes only. Nothing in this newsletter constitutes financial advice or a recommendation to buy or sell any asset. Always do your own research before making any investment decisions.

I hold positions in many of the assets discussed in this newsletter. For a full list of disclosures, please refer to the Khala Research website.

Follow me on X | Khala Research

This Crypto AI & Robotics newsletter consists of three parts:

1. Snippet Partner (EigenCloud)

2. AI & Robotics Protocol Watchlist

3. Newsletter Feedback Poll

If you have any questions feel free to reach out to me on X or message my business X account ‘Khala Research’

Heads-up: Next week (05.26.26) will be the first skip week for The Agentic Future as I will be getting married — After 177 weeks of continued coverage I thought this would be a good opportunity to take a break for a week!

The newsletter will return the following Tuesday (06.02.26) with the usual cadence. Roundups on X continue as normal in the meantime

EigenCloud is the verifiable cloud built for the sovereign agent era, primed just as the number of nation states doubling down on their own Sovereign AI initiatives:

As AI agents move from assistants to economic actors holding wallets, paying for their own compute, and executing on behalf of humans, the binding constraint shifts from raw capability to provable accountability

EigenCloud is building that layer through four primitives:

1. EigenLayer (restaking with slashing for economic accountability)

2. EigenDA (tamper-resistant data availability)

3. EigenCompute (verifiable offchain compute)

4. EigenAI (bit-exact deterministic inference)

The agent-facing product is EigenCloud AgentKit (Beta), which launched March 26 and is built around three phases: Build, Prove, Run

How a sovereign agent works on EigenCloud

Why it matters

x402 solves how agents pay. Google’s AP2 standardises what an agent payment looks like across cards, bank rails, and chains

EigenCloud is the layer that proves the agent on the other end actually did what it was paid to do, and faces real economic consequences when it doesn’t

Live partnerships include Google Cloud (AP2 launch partner), Coinbase AgentKit, and many others.

Learn more: Link

This newsletter goes out weekly to 7.2k+ subscribers.

Please don’t hesitate to message me directly for sponsorship or partnership enquiries.

2. Crypto AI & Robotics Protocol Watchlist

I get asked every week which projects are actually worth attention versus noise

Here is the current cut, narrowed to the ones with shipping product, real catalysts, or structural setups in the next 30-60 days

This is not an investable index, not a price target list, and not a ranking. Just where the signal is concentrating from observations on the timeline:

Crypto x AI Protocols

Nous Research (Hermes Agent)

• Hermes Agent flipped OpenClaw on May 10 to take the #1 spot on OpenRouter’s global daily inference rankings, processing 224B daily tokens against OpenClaw’s 186B, the first dethroning since OpenClaw launched in late 2025

• 90 days from launch to global #1; 114k+ GitHub stars, 295 contributors on the May 7 “Tenacity” v0.13.0 release (864 commits, 588 PRs)

• Closed learning loop: writes its own reusable SKILL.md files, SQLite FTS5 persistent memory across sessions, no YAML configuration

• Grok integration shipped May 16: Grok subscription holders can now run Hermes inside their Grok account, opening a direct distribution channel from xAI

• No token (yet); pay attention and use Hermes agent

Bittensor (TAO) + Ecosystem

• The closest thing crypto has to a structured market for decentralised AI primitives, mapped across 12 categories in the Khala Research Bittensor Ecosystem Map (April 19 cut):

• Chutes (SN64): largest subnet by mcap, first to cross $100M, 9.1T+ cumulative tokens processed, daily peaks 50B+, ~4,400 H100-equivalent GPUs, 400k+ users, pricing ~85% lower than AWS, $1M+ auto-staking buybacks

• Targon (SN4): 20B+ paid inference tokens per day, 1,500+ H200 GPUs, $60M+ annual compute incentives flowing through the network. $10.5M Series A led by OSS Capital with Digital Currency Group and Shopify’s Tobi Lütke among others. Intel TDX confidential compute architecture in production

• Score (SN44): 100% conversion on 60-day enterprise trials, signed deals with Reading FC and a major European petroleum company monitoring thousands of automated gas stations. Formal alliance with PwC France and Maghreb deploying Manako (Score’s vision platform) across retail, logistics, energy, public infrastructure. Won Start In Block 2026 at Paris Blockchain Week

• Lium (SN51): ~$432K monthly rental revenue, ~6.2% of TAO emissions, 60% emissions burn. The top SN3 (Templar) miner now runs Lium GPUs

• Plus many other promising subnets like Metanova for drug discovery + more