This Crypto AI & Robotics newsletter consists of the following:

• Snippet Partner: Walrus (AI Agent Verification)

1. Anthropic : IPO Benchmark Analysis

2. Anthropic’s $3.4bn Power Bet

3. What $3.4bn actually buys

4. Bitcoin miners become AI landlords

5. Texas politics + the value of existing capacity

6. Crypto’s opportunity + 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’

All this infrastructure is being built to let AI do more. When an agent can spend money or operate a machine, we also need a reliable record of what it did

That’s the focus of our new “WALRUS: Machine on the Record” report:

Walrus, created by Mysten Labs, provides decentralised storage with verifiable data integrity + availability. Walrus Memory extends that foundation to portable agent memory

An application can capture the instructions, approvals, tool calls + results, then preserve a record whose contents can be checked later. Sensitive data needs encryption before storage; Walrus blobs are public by default

Proving a record hasn’t changed doesn’t prove the agent was correct, or that everything was logged. Identity, complete logging + checks on execution remain part of the application

Explore our Walrus research at Khala & Explore Walrus

This newsletter goes out weekly to 7.4k+ subscribers.

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

Anthropic : IPO Benchmark Analysis

With Anthropic preparing for one of the biggest IPOs in history, I wanted to see what recent AI listings tell us about what happens next

I’ve benchmarked eight AI and adjacent IPOs, from SpaceX to Cerebras and Figma, and the results are revealing…

Half now trade below their first-day closing price, despite some spectacular debuts. Figma surged 250% on day one, only to surrender most of those gains:

The takeaway?

AI hype can drive an explosive debut, but valuation and execution determine what happens afterwards. And Anthropic is entering a market far less forgiving than it was even a year ago

1. Anthropic’s $3.4bn Power Bet

An AI company spending $3.4bn on gas generators is a fairly good sign that the next phase of this cycle will look different

That’s the deal Anthropic has reportedly struck to secure 1.7 gigawatts of generation capacity; to put that in context it’s equivalent to generating enough electricity in a year to match the annual use of roughly 6 million homes… insane, I know!

Meanwhile, Riot + TeraWulf have announced roughly $47bn of Anthropic leases across two campuses, payable over many years

The interesting bit is who gets paid along the way. Bitcoin miners accumulated land, substations + power access before frontier AI labs needed them at this scale. Some are now turning that position into long-term rental income

My thesis: the AI opportunity increasingly depends on how quickly capital can become usable compute. Power access matters, but so do delivery dates, financing + the quality of the tenant

2. The IPO: growth has a physical cost

Anthropic’s proposed Nasdaq ticker is ANT, but I wouldn’t build an investment thesis around a rumoured opening price

On 10 November, Reuters reported that indicative IPO details had been circulated to banks and then withdrawn as inaccurate. That makes the final price, proceeds + timetable moving targets

The operating numbers tell us more:

Revenue grew 47% quarter on quarter. Compute + infrastructure expense grew 78%

That doesn’t mean the economics are broken; It means demand growth and the cost of serving it need to be watched together. The GAAP loss also includes a $2bn non-cash charitable contribution charge; it would be misleading to blame the entire profit swing on compute

Here’s a useful valuation sense-check. Annualising Q3 revenue gives $68bn. Assumed equity valuations of $1.2tn, $1.5tn + $2tn would therefore represent roughly 17.7x, 22.1x + 29.5x that revenue base

Those are scenarios, not IPO targets or forward multiples. They make one point: even a lower headline valuation can still require a lot of future growth

Then there’s the commitments. Anthropic disclosed $895.4bn of purchase + lease commitments, substantially related to compute and infrastructure. Around $69.8bn falls due in the 12 months after September 2026; about half the total is expected by the end of 2030, with substantially the rest through 2045

This is a long-duration delivery + funding challenge. It isn’t a bill for $895bn arriving tomorrow

3. What $3.4bn actually buys

According to the FT, reported by Reuters, Anthropic took over commitments for 1.7GW of gas-fired generators from OnePWR Solutions. Neither company had confirmed the report to Reuters

Data Center Dynamics reports that the Caterpillar equipment was ordered two years ago and is expected to become available within the next two years

My read is that Anthropic is paying for greater control over delivery as well as the equipment itself

The headline works out at roughly $2m per MW of generation capacity, or $2,000/kW:

Generators help with one constraint. The site still needs fuel supply, permits, cooling, networking + the equipment to connect everything together

This is why I wouldn’t say the chip bottleneck has disappeared. The constraint varies by project. But owning GPUs is much less useful if you can’t get them installed and powered on schedule

The asset I’m interested in is usable capacity with a credible delivery date

4. Bitcoin miners become AI landlords

This is where the crypto connection gets tangible… thankfully a viable use case outside of stablecoins!

Riot’s Corsicana announcement describes a 910MW critical-IT lease, with approximately $28bn of revenue over its initial term. Its 10-Q identifies Anthropic as the tenant

TeraWulf’s Justified campus lease covers approximately 401MW of critical IT capacity and $19bn over an initial 20-year term

The two contracts total $47bn and 1,311MW of critical IT capacity. That capacity is a different measure from the generator deal’s 1.7GW of generation, so adding them together would be misleading

Read past the 99% margin

Riot expects a roughly 99% net operating income margin on the powered-land lease because the tenant carries most operating expenses. It also estimates around $5.5bn of development capital, including reimbursements to the tenant

Both numbers belong in the same conversation. Property-level NOI comes before financing costs + corporate overhead and doesn’t deduct the initial construction spend

Its disclosed base rent is $130/kW/month, rising 3% annually. Applied to all 910MW at the starting rate, that is approximately $1.42bn a year before escalation. It is not current rent: the campus is delivered in blocks, and the contract’s later-phase start determines the ultimate term end

The broader lesson is that a large contract value needs to be reconciled to when cash arrives, what must be spent first + how much debt sits ahead of shareholders

Google’s credit makes the structure more interesting

Google has agreed to support certain Anthropic obligations at TeraWulf’s Justified campus. The 8-K makes the limits clear: each backstop becomes effective when the final data hall under its corresponding lease commences, and there are termination conditions

If a covered default occurs, Google can assume the lease or, in specified circumstances, pay a termination fee that entitles it to an equity interest in the project company

This can make the project easier to finance. It does not make construction risk, timing risk or equity losses disappear

It also connects more of the AI infrastructure chain to the same small group of counterparties. A collection of different ticker symbols can still leave you exposed to one tenant’s ability to pay

There is a separate implication for bitcoin. Riot sold 20,264 BTC in the first nine months of 2026, primarily to fund data centre development. The pivot can create value for a miner while also putting BTC into the market

5. Texas: scarcity helps only if you can deliver

Texas governor-elect Gina Hinojosa has called for a moratorium on data centres until legislators put new protections in place. The Texas Tribune reports that she takes office on 19 January 2027, following an existing Abbott-era pause affecting environmental permitting + grid connections

If new supply becomes harder to approve, an existing operational site may become more valuable. But a project that still needs permits, construction or a connection can become harder to deliver, even if the owner already has land and a customer

I wouldn’t assume that every signed lease is protected from future restrictions. And generating power on site still leaves environmental, fuel-supply + local approval questions

A scarcity premium only helps shareholders if the project can reach the point where it earns money

6. Crypto has three distinct opportunities

i) Price discovery before an IPO

Entropy’s Anthropic market on Hyperliquid lets traders express a view before a public listing. Its oracle provider RedStone describes a blend of order-book information + external references, weighted by available liquidity

That’s useful, but a perp is a derivative exposure. It doesn’t put Anthropic shares in your wallet or give you an IPO allocation

The interesting test is whether a price recorded before the final terms predicts the eventual valuation. That requires matching timestamps, valuation definitions + share counts, then accounting for funding and trading costs. A raw chart of two differently defined prices won’t settle the question

ii)A market for the cost of compute

Injective’s Super Intelligence Compute Index, launched on 26 October, tracks a weighted basket of GPU rental prices:

The weights are fixed and the published index uses a trailing seven-day average. This creates a way to trade compute-price exposure, but a basket of rental listings won’t track every company’s actual bill. Hardware mix, location, service terms + funding costs all matter

Owning that exposure also doesn’t reserve a GPU. The financial market and the physical capacity remain separate

iii) Selling useful compute through open networks

SubConnect estimates that revenue-generating Bittensor subnets reached roughly $70m of annualised revenue, up from about $50m a month earlier, according to its 4 November update

If you want to dig further into Bittensor’s incentive mechanisms for distributed compute, then we’ve previously produced reports on this:

What I want to see next is revenue that remains attractive after power, hardware, support + token incentives. Aggregating spare capacity may help some workloads, but a distributed marketplace doesn’t automatically reproduce the networking and reliability of a tightly integrated training cluster

For agents and robotics, the useful benchmark is cost per successfully completed task, alongside latency + reliability. Cheap GPU-hours are only an advantage if the workload finishes well

7. What I'm watching next

My base case is that more miners pursue AI leases and financing structures become an increasingly important differentiator. I would change my mind if the backlog keeps growing while delivery and cash collection fail to follow

I’m less interested in guessing ANT’s first-day move than in businesses that can convert scarce capacity into durable cash flow. That includes power infrastructure + equipment, selected miner developments and the emerging markets for compute

The entry price still matters. Being right about rising demand doesn’t guarantee a good return if the valuation already assumes perfect execution

The question I keep coming back to: who can turn a promised megawatt into a paying customer, and what does it cost them to get there?

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: Umia Finance + Slop Cash

2. What The Simpsons Got Right About This Week’s Hacks

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

AI makes code cheaper to produce, increasing the importance of careful review + ongoing maintenance… very topical given this weeks hacks!

Slop Cash (Umia’s first announced external project from elizaOS creator Shaw Walters and Drew Pierson) is building a funding system around that work

Projects fund monthly pools that reward merged code changes and accepted reviews. Results face a 14-day review period before funders authorise payments from wallets they control:

As AI expands attackers’ capabilities, funding useful software maintenance becomes increasingly relevant. Slop’s impact will depend on the quality of contributions and review.

Its treasury rules hold auction proceeds in a smart contract, give teams a defined monthly allowance and require a decision market for additional spending. Proposed Slop team allocations combine time-based vesting + price milestones, with project assets held under the governed legal entity.

$SLOP governs Slop Cash’s treasury; code acceptance and contributor payments remain separate. I like the focus on verifiable work + controlled spending, although smart-contract, governance and execution risks remain. Sustained funding and useful contributions will be the real test.

This newsletter goes out weekly to 7.4k+ subscribers.

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

What The Simpsons Got Right About This Week’s Hacks

In a 1995 episode of The Simpsons, Lisa’s fiancé makes a call from his wristwatch. Scenes like that earned the show its reputation for predicting the future. This week it was Homer’s fridge that made me do a double take.

The 2023 episode “It’s a Blunderful Life” opens at Thanksgiving, 60 years in the future. Lisa tells her family why they light candles and takes them back to the week Springfield lost power. Marge opens an empty fridge, certain that Homer has eaten everything. He’s buried the food in the snow to keep it cold, as a backup plan.

Several incidents this week show eerily similar connections:

• On Monday, a supermarket in Sjöbo, Sweden, said someone had set its fridges and freezers to 50°C overnight. Staff were throwing food away. Last week I wrote about backup power for the house; a generator could keep those freezers powered, but it couldn’t fix the setting

• Four days earlier, attackers had taken roughly $6.3bn from Binance’s hot wallets (that’s the largest hack in crypto history, the next was ByBit for $1.5bn which was also only last year).

• On Sunday night and into Monday, emergency apps in Ukraine and Russia sent false warnings, causing concerns over misinformation on a global scale

• Chainalysis, which traces stolen crypto, says an intruder used what it calls “offensive agentic techniques” in a separate breach. The Swedish freezers did exactly what their controls told them to do. If a compromised account sends a warehouse robot into an occupied aisle, I’d want the person standing there to have a stop button that works even while the attacker is logged in

We still date stories by saying they happened ‘during the first COVID lockdown’. That phrase brings back the queue outside the supermarket or the neighbour who left shopping on the doorstep.

I suspect we’ll live through outages that become reference points in much the same way. Decades from now, someone might tell their grandchildren about the week the card machines went down and the shopkeeper who trusted them to pay later

What happened at Binance

Binance dates the compromise to 20:05 UTC on 29 October. Attackers took approximately $6.3bn in token assets from its hot wallets, and the exchange suspended withdrawals while it investigated and secured affected infrastructure:

On 31 October, the FBI attributed the theft to North Korean activity it calls “TraderTraitor”. That attribution applies to Binance. It does not identify who was behind the refrigeration attacks or the emergency-app compromises.

Binance has committed to covering the losses. Its 2 November update says approximately 95% of the affected assets have been replenished, with ten named assets fully backed and verification available for its 31 October reserves snapshot. Withdrawals began reopening in phases on 1 November, with further assets and networks still being restored.

Binance can replace the assets it lost before it can safely resume withdrawals. For a customer with a payment to make, recovery means being able to send the money. The timetable for that mattered just as much as the promise to cover the loss.

The technical investigation also remains incomplete. Binance’s 30 October statement said an initial lead involved a compromised laptop, but investigators had found additional breach points. At that stage, it reported no evidence that private keys had leaked.

For me, the practical lesson is to understand how much depends on one provider being available. The same question applies to an exchange, a bank account or the infrastructure running a business: how long could I function if access stopped today?

Six scenes from Springfield

The blackout in “It’s a Blunderful Life” ; she tells the story of the power outage at Thanksgiving 60 years later with several scenes that mirror some of the real life events taking place today:

Two more crypto theft reports

Four days after the Binance hack, Andy flagged two more cases involving assets held on exchanges. One was at Brazil’s Mercado Bitcoin. The other concerned an unnamed EU organisation with accounts at several exchanges:

Mercado Bitcoin suspended deposits and withdrawals on 2 November after acknowledging a security incident. Hypernative’s initial analysis identified more than $40m in suspicious transfers across at least ten blockchains. The exchange said customers had suffered no losses and no data had leaked, but had not confirmed the amount taken.

ZachXBT now puts the suspected theft from the EU organisation’s accounts at more than $133m. His 2 November alert put the figure above $106m. On 3 November he traced over $27m in additional withdrawals, some of which took place after his first alert. The account holder’s identity and how the accounts were accessed remain unknown

The attacks that reached beyond finance

Late on 1 November, Ukraine’s Air Alert app sent false warnings involving radiation, chemical threats and fighting in Kyiv:

Russia’s emergency ministry app was also compromised, with false nuclear warnings reported overnight. The Record’s reporting includes notifications received by its own reporter and statements from the app operators and authorities.

These were compromises of public-facing applications. Authorities said broader warning infrastructure remained operational; Russia’s ministry also confirmed that its main warning system was unaffected. There is no evidence here that missile-launch controls were accessed.

Marge hears a storm forecast and knows which job she wants done before it hits… In Kyiv, people received a warning on a phone app they normally use to decide when to take shelter. A convincing false alarm can cause confusion in the moment and make the next genuine warning harder to trust.

The supermarket incidents are more tangible still.

• On 2 November, refrigeration problems linked to apparent cyberattacks affected retailers in Sweden, Belgium, Luxembourg, Ireland and Estonia.

• Stores discarded chilled and frozen food. The Sjöbo shop said its equipment had been set to 50°C. AP’s reporting

The BBC’s video report covers food being dumped across several European countries. Al Arabiya’s footage from Belgium brings it down to the shop floor, where staff are throwing away stock after cooling systems were disrupted. For customers, the consequence is an empty cabinet. For the retailer, it is an immediate loss of stock and sales.

France had already experienced a refrigeration attack in October, before both Binance and last week’s newsletter. CERT-FR’s alert, first issued on 12 October, identifies compromised remote controllers and recommends removing exposed management interfaces from the public internet, securing remote access and updating equipment.

Homer’s improvised fridge works because he pays attention to the thing he actually needs to keep cold. I’d want the same check in a shop. A thermometer with its own alarm could tell staff the food was warming up even if the main control panel was carrying out its instructions exactly as sent.

Where AI enters the picture

The clearest direct connection in this week’s disclosures comes from Chainalysis.

On 30 October, it said an external actor had used offensive agentic techniques to gain unauthorised access earlier in the month:

Customer user IDs, names, email addresses and organisational roles were accessed. It reported no evidence of access to investigation content, authentication credentials or session tokens, and explicitly said the incident was unrelated to Binance.

The company has not published enough detail to establish which model or tools were involved, or how much of the intrusion was autonomous. Its description is still notable for an industry that increasingly expects agents to operate software and coordinate work.

The NCSC’s assessment through 2027 expects AI to make parts of cyber intrusion more effective and efficient, including vulnerability research, exploitation and social engineering. It also expects the window between a vulnerability being disclosed and exploited to shrink further.

My concern is the effect on systems that already struggle to keep up with basic maintenance. An organisation may have thousands of devices, several suppliers and equipment that stays in service for years. Faster reconnaissance and exploitation put more pressure on the time available to identify and fix weaknesses.

Defenders can use AI too. Chainalysis says it is using language models to support a broader security review. The outcome will depend on whether faster detection and remediation reach the systems that need them.

Smithers spots the overloaded socket at the plant. His warning carries less weight than Burns’s authority to ignore it. I’d ask the same question of a system that lets an AI agent transfer funds or control equipment. If a compromised account issues an instruction beyond the agreed limit, what actually stops the transfer or the machine?

Visual timeline

Major incidents + disclosures

Relevant service outages

These are availability incidents. The provider notices reviewed do not establish a cyberattack; partial product failures should not be described as an internet-wide outage.

The EU estimate incorporates ZachXBT’s 3 November update. The register covers 16 selected incidents or disclosures and seven provider outages. Counts describe different populations and must not be added together. Related airline disclosures are grouped to avoid counting the same supplier incident twice.

The 12 October French refrigeration alert and the 20 October Google Cloud banking disruption predate the reporting window. The February FICOBA case and the December–January La Banque Postale incident also remain outside it. No new French-bank hack is included as confirmed.

The French bank rumour

Springfield puts a name to the blackout long before anyone checks the evidence. In the suspected $133m+ theft, ZachXBT can trace withdrawals while the account holder’s identity remains unknown. He suspects a large financial institution in Western Europe. The claim that it was a French bank remains unverified, but rumours are circulating:

Rumour or not, this isnt the first time it’s happened… there are genuine older cases. France disclosed unauthorised access to its FICOBA bank-account registry in February, affecting information associated with around 1.2 million accounts.

That is distinct from a bank’s payment systems being taken over. La Banque Postale’s major denial-of-service incident ran from December 2025 into January 2026; the bank said its core systems and data remained intact.

What I take from this week

Last week’s household plan still makes sense to me: a modest buffer of essentials, tested backup power for the things that matter, securely stored records and more than one way to access money and information.

For crypto I already own, self custody reduces dependence on an exchange’s withdrawal service, while putting responsibility for keys and recovery on me. It also leaves other dependencies in place, including connectivity, wallet software and, for stablecoins, the issuer.

For businesses building with agents or robotics, I would put the same care into what happens after a compromise.

1. Can access be revoked quickly?

2. Can a machine remain safe without its cloud connection?

3. Can staff verify a critical instruction through an independent channel?

4. Has recovery actually been rehearsed?

The opportunity for the companies and protocols I cover is to make those questions easier to answer with evidence. A useful product should reduce the number of things that can go wrong, limit the damage when they do, or shorten recovery time.

That’s the connection between a crypto exchange and a supermarket freezer. Both depend on software being allowed to take consequential actions. As we give machines more responsibility, the controls around that authority deserve as much attention as the capability itself.

I still want to see what these systems can build, but I also want to know what keeps working when something breaks.

One to watch

If you watch “It’s a Blunderful Life” (season 35, episode 7), keep an eye on Burns’s electric trapdoor. It fails during the blackout, so Smithers has to crank it by hand. By the time he gets it open, the family has stepped aside

That’s the test I’d borrow for a freezer, a wallet or a robot. When the main system fails, can somebody take over in time for it to matter?

Lisa’s family lights candles to remember the blackout. If an outage becomes one of our own family stories, I hope what sticks is the neighbour who knocked on the door when a phone call wouldn’t go through.

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: Umia Finance + Slop Cash

2. UK + US preparation and lessons from Texas

3. How AI changes the economics of cyber warfare

4. Banking outages, financial records + self custody

5. Outage timeline, household checklist + TLDR

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

AI makes code cheaper to produce, increasing the importance of careful review + ongoing maintenance… so this week’s partner is very relevant!

Slop Cash (Umia’s first announced external project from elizaOS creator Shaw Walters and Drew Pierson) is building a funding system around that work

Projects fund monthly pools that reward merged code changes and accepted reviews. Results face a 14-day review period before funders authorise payments from wallets they control:

As AI expands attackers’ capabilities, funding useful software maintenance becomes increasingly relevant. Slop’s impact will depend on the quality of contributions and review.

Its treasury rules hold auction proceeds in a smart contract, give teams a defined monthly allowance and require a decision market for additional spending. Proposed Slop team allocations combine time-based vesting + price milestones, with project assets held under the governed legal entity.

$SLOP governs Slop Cash’s treasury; code acceptance and contributor payments remain separate. I like the focus on verifiable work + controlled spending, although smart-contract, governance and execution risks remain. Sustained funding and useful contributions will be the real test.

This newsletter goes out weekly to 7.4k+ subscribers.

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

Cyber Resilience in the Age of AI

I have spent part of this week looking at backup power, solar panels and a camping stove. A slightly different research rabbit hole from the usual token launches…

The UK’s emergency-preparation messaging prompted me to look properly at how our household would manage if electricity, internet access or card payments stopped working for a few days.

Some of the headlines invite accusations of scaremongering.

The actual government advice, updated on 22 October, is fairly modest: prepare for three days of disruption, build supplies gradually + leave emergency services better able to help people who need them most.

The government has published household preparedness advice since 2024. This campaign broadens its reach; it does not establish that an attack is imminent":

Although did lead to a surge in sales driven by panic buying:

There is a parallel development in the US.

Fox reports a 90-day independent-operation target for major US military bases. The Army’s account of Fortress America confirms the ambition for installations to generate their own power, strengthen supply chains and harden against intrusion:

These are military resilience targets that require infrastructure upgrades over time. The announcement does not mean every base is already self-sufficient for three months.

What interests me is the assumption behind both initiatives:

essential services can be interrupted, and having some capacity to operate independently makes that interruption easier to manage

AI changes the economics of cyber warfare

Most of us following AI spend our time thinking about what a small team can now build. The same improvement in capability is available to people trying to break into systems.

The NCSC’s assessment through 2027 expects AI to increase the frequency and intensity of cyber threats, leading to there being a “realistic possibility” of incidents through 2027 (essentially probable, or 50% likelihood):

It highlights faster vulnerability research and exploitation, alongside better reconnaissance and social engineering.

In practical terms:

• attackers can research more targets, produce more convincing messages and work through software weaknesses faster

• Defenders have less time to install fixes before those weaknesses are exploited.

Anthropic’s September 2026 threat report gives a more recent view. Across the notable operations it investigated, it describes AI moving beyond answering questions into executing and coordinating parts of attacks, while people still chose targets and reviewed results.

One case involved a suspected Russian espionage actor using AI-assisted workflows against government, diplomatic and defence targets. Anthropic says the workflow could rebuild and redeploy tools after security software detected them. These are the company’s investigative findings, rather than a measure of all cyber activity.

• For a state-backed group, that can mean more operations for the same resources.

• For criminals, it can bring tasks within reach that previously required a larger specialist team.

Espionage, theft and disruption have different objectives, but they can benefit from the same tools.

A successful attack on a supplier or administrative system can also interrupt services further downstream. Disrupting payments or deliveries can affect a household even when its own devices are secure. Turning that into sustained physical disruption still depends on the target, the access obtained and the defences in place.

AI is also improving defence, so it’s certainly not all doom and gloom!

The NCSC describes Cyber Shield, a planned capability using agentic AI to help defend at machine speed. The outcome will depend partly on how quickly organisations adopt effective protection.

My expectation is that intermittent disruption becomes more likely as these capabilities spread unevenly. That is my judgement about the direction of risk, rather than a quantified forecast of UK blackouts. It is enough to make a modest household backup plan feel worthwhile.

Texas showed how power loss becomes a wider crisis

Winter Storm Uri in February 2021 is a useful case study. Extreme cold, freezing equipment and fuel problems disrupted electricity generation across Texas and neighbouring states. FERC reports that more than 4.5 million people in Texas lost power, some for as long as four days.

The disruption extended well beyond lighting. Texas’s water regulator documented failures in water treatment and pumping, frozen equipment and burst pipes. The state ultimately confirmed 246 storm-related deaths, including deaths associated with cold exposure, carbon monoxide and disrupted medical care.

This was a weather-driven disaster. It demonstrates the consequences of prolonged infrastructure failure, without telling us how likely a similar event is in the UK or establishing a link to AI. For households, keeping warm, accessing safe water and maintaining essential care can become urgent well before the power returns.

A banking outage does not mean the records disappear

There are two separate concerns here:

1. whether I can use my money today, and

2. whether the system can still establish what I own.

If a banking app or card network goes offline, the immediate problem is access. A failed balance screen does not tell us that the underlying account records have been erased.

Banks have disaster-recovery arrangements designed to restore services and records. In its review of recovery practices, the Bank of England describes firms using protected backups, rebuilding systems in clean environments and switching to separate recovery facilities.

Those backups and alternative servers are a substantial reason I regard permanent, system-wide loss of financial records as an unlikely extreme scenario. I do not see evidence that it is the likely outcome of the current threat.

The Bank also warns that attacks can affect multiple sites and backups. Recovery can take time, particularly when firms need to check that restored records are complete and accurate. Having a recovery plan does not guarantee uninterrupted access.

For me, the practical response is to keep recent statements, account details, transaction exports + insurance documents securely available offline, alongside some cash for essentials. Those records would help with reconciliation or a dispute; they are supporting evidence rather than a replacement for the bank’s ledger.

What self custody actually gives you during an outage

Crypto gives us another way to separate control of an asset from access to a particular institution. Bitcoin’s transaction history is maintained across its network, while private keys authorise spending.

A local power cut does not erase that history from the computers still operating elsewhere… hence the power of “distributed ledger technology” (DLT) which is the entire premise of blockchain

A hardware wallet holds the keys. The coins are accounted for on the blockchain. With the correct recovery material, you can regain control through a compatible wallet if the device is lost or damaged.

Actually spending still requires a route to an operating network so the transaction can be broadcast and confirmed. Local internet failure can prevent that, and a wider disruption can affect the network itself. Self custody also cannot make a shop accept crypto or protect the asset’s market price

For crypto I already hold, I want a recovery arrangement I understand and have checked, with durable offline backups protected from theft, fire and water. Recovery phrases should stay out of screenshots, email, cloud notes + AI chats. Any extra passphrase is also essential to recovery

I would keep a separate record of wallet addresses, transaction history and purchase costs. Those records help establish what happened, but an address or a screenshot of a balance cannot replace the keys. A trusted person should also know how to find the recovery instructions if I am unavailable

Stablecoins add another dependency. Holding USDC in your own wallet still leaves you exposed to its issuer (Circle), reserve arrangements + address-blocking powers. Self custody changes who controls the keys; it does not remove every counterparty.

My preference is to have several usable options: ordinary banking, a modest cash reserve + properly managed custody of existing crypto. Each deals with different failures, and each needs to be usable when something goes wrong.

How a prolonged power cut could affect daily life

The timeline below explores a severe regional outage that continues for days. The time bands are illustrative assumptions, not official thresholds or a forecast. Weather, the area affected, backup capacity and the response from authorities can change both the sequence and severity.

By survival mode, I mean people redirecting their time and money towards water, warmth, food + protecting family. Uncertain supplies can bring queues and precautionary buying, while prolonged disruption can force households to seek help or move somewhere safer.

The research on crisis behaviour also finds that cooperation and mutual support are common. A useful scenario includes neighbours sharing resources and organised assistance, alongside the potential for stress and disputes. There is no reliable countdown to societal collapse:

My two week plan and the longer term move off grid

I’m aiming for around two weeks of basic household resilience.

That goes beyond the government’s three-day baseline and reflects my own preference, rather than an official prediction about how long an outage will last.

I’ve been researching a roughly £1,000 starting budget, including a Jackery-style battery and solar setup, plus a simple outdoor cooking option. The priority is equipment I can understand and test before I need it

My checklist starts with the government’s essentials, extended gradually to suit our household:

A portable battery also has a finite energy budget. I would size it around the devices that matter most and test their runtime. Keeping the router powered only helps if the broadband network outside the house is still working. Camping stoves and petrol generators belong outdoors, away from doors and windows.

Longer term, I want to move towards off-grid living supported by renewable generation and sustainable resources. That means thinking about year-round energy demand, storage, heating, water and food, alongside maintenance and the people nearby who could help.

There is a technical detail worth checking before buying solar: ordinary grid-connected systems often shut down during a power cut. Energy Saving Trust explains that backup operation needs the right equipment and configuration, even with a battery. UK winter generation is also much lower, so a summer demonstration tells you little about December independence

TLDR

This is a diversion from my usual coverage of crypto, AI + robotics development, but it matters as we move deeper into an AI-centric world

More of our lives depend on connected systems, and the consequences of those systems failing deserve some thought

There is no specific imminent blackout warning in the evidence covered here. AI can strengthen defences as well as attackers, and banks have recovery arrangements to restore services and records

My concern is being able to manage a disruption while those systems recover; I’m not a doomer; I’m being practical about a worst-case scenario.

A personal disaster recovery plan means knowing how your household would access water, food, warmth, information + money, and where you would go if home became unsafe

Use this as a practical guide and a prompt to think ahead (maybe print this off as a hard copy). AI capabilities could make parts of our infrastructure less reliable if defences fail to keep pace. Building a sensible buffer now gives us more options, helps us support others and creates a starting point for longer-term self-sufficiency.

Prepare for the worst, hope for the best…

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

A shorter edition this week to share our latest report at Khala Research, which I alluded in last week’s newsletter

We’ve also given our website a refresh & opened a new Telegram channel for shorter snippets of news, subscribe for free here: Link

This newsletter goes out weekly to 7.5k+ subscribers.

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

Language models had an internet full of text to learn from. Robots need demonstrations of physical tasks across different machines, objects + environments. Collecting enough variety remains one of the biggest barriers to making them useful beyond the lab.

Our report examines how robots learn, the businesses supplying their training data + where crypto incentives fit. It includes a deep dive into Axis Robotics, alongside coverage of BitRobot, Eastworlds + THOR:

Read the full report →

We’ve also launched a free Telegram channel for news, research + updates from Khala.

Subscribe to Khala on Telegram →

We’ll be back with the usual format next week, covering the latest topics across crypto, AI + robotics so stay tuned!

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. The Robinhood Chain sell-off

3. Standard’s liquidity position

4. The launchpad + its 1M+ USDG rewards campaign

5. What the economics mean for STANDARD holders

6. My predictions + how to approach the opportunity

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

Axis Robotics is building infrastructure for collecting the demonstrations robots need to learn physical tasks.

Contributors operate simulated robots through their browsers, producing data that Axis cleans, augments + uses to train models.

Its data pipeline connects those demonstrations to simulation and real-world evaluation, while model failures help identify where further training data is needed.

Axis has already secured numerous top tier partnerships with established companies:

Our upcoming Khala Research report examines how this process works, the published benchmarks + the commercial opportunity for robotics data providers

A full robotics data report featuring Axis is coming later this week. Keep an eye out!

This newsletter goes out weekly to 7.4k+ subscribers.

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

Standard Reserve’s Next Phase

In my September newsletter, I covered Standard’s launch: an onchain currency, NFT banking Charters + Branches that earn token issuance

Its monetary policy responds to capital flows, adjusting issuance while directing fees towards reserves, buybacks + other protocol functions

A month later, trading activity across Robinhood Chain has slowed considerably. Standard is preparing a launchpad that could bring new projects and traders into markets supported by its reserve":

That gives us another way to assess the business: how much income can it earn from people trading other assets?

1. The Robinhood Chain Sell-Off

Average daily DEX volume on Robinhood Chain fell from $2.29B during 1–15 September to $897M during 6–12 October, a decline of approximately 61%:

There is still substantial activity on the chain, although launchpads and trading venues are competing for less volume

STANDARD has been moving against some of the larger ecosystem tokens:

_{*DEXScreener snapshots checked on 13 October 2026. Values are rounded and move quickly.}

The launchpad announcement could be attracting buyers, although one day of outperformance gives us limited information about how STANDARD will behave through a longer downturn

The underlying liquidity + proposed fee income deserve a closer look:

2. Standard’s Liquidity Position

STANDARD’s main trading pool currently shows approximately $15.8M of combined pool inventory:

Comprising around 2,600 ETH + 28.8M STANDARD

That is a substantial pool alongside a token market cap of roughly $26M.

• The whitepaper describes the genesis liquidity as protocol-owned and permanently locked in the canonical Uniswap v4 pool.

• In its 29 September update, the team reported owning 99% of its liquidity; that percentage is a dated team-reported figure.

Owning liquidity reduces dependence on external liquidity providers choosing to stay. It also gives the protocol an established market around which to build further products.

There are some practical limits to what the headline number tells us.

The $15.4M includes STANDARD itself. Its dollar value changes with the token price, and locked pool assets have restrictions on how they can be used. Capital available for stock-token markets needs to be accounted for separately.

For traders, the useful comparison is the cost of executing an order.

I would check buy + sell quotes at $1k, $10k and $100k, including pool fees, hook charges and price impact. Concentrated liquidity can produce very different execution depending on where positions sit relative to the current price.

Standard’s potential moat comes from keeping useful liquidity available and attracting recurring trading through it.

3. The Launchpad + $1M USD Campaign

I touched on Standard’s launchpad in last week’s v4 hooks newsletter:

The proposed model connects new token markets to reserve-owned stock liquidity.

A new token can trade against a stock token, while an underlying market allows traders to enter or exit that stock exposure. Deeper liquidity in the underlying market could improve that part of the trading route and make it easier for projects to launch around it.

Each new token still needs sufficient liquidity in its own pair. The stock pairing determines what it trades against; any rights over the underlying company would require separate arrangements

This point has had reasonable coverage when AMC CEO had a public bout on the timeline with Vlad as one of the arguments against tokenized stocks not having the same rights to holders (for now).

On 12 October, Standard announced King of the Markets (going live today), described as its launchpad and initial stock-token liquidity pilot:

Traders + token deployers will compete for a growing rewards pool starting at 1M+ USDG.

That should give people a reason to try the markets. It will also make early activity difficult to interpret, because some trading will be motivated by the rewards.

There are two ways I could see this developing:

1. Launchpad activity: projects launch tokens, bring their communities + generate trading fees

2. Wider stock trading: competitive stock pools attract orders routed through other wallets and aggregators

The second route would give Standard customers who arrive through other apps. For that to happen, its pools need to offer competitive prices, sufficient depth + reliable execution

4. What the Fees Need to Cover

The reward campaign gives us a useful scale for modelling the rollout.

To generate $1M of retained fees, the required trading volume would be:

_{Illustrative scenarios. These are assumed retained fee rates, rather than announced launchpad charges. The calculation makes no assumption about who funds the rewards.}

At a retained rate of 0.10%, the markets need $1B of trading volume to generate $1M of fees. At 0.05%, that requirement rises to $2B.

The amount retained after fee-sharing has a large effect on the business.

I would track:

• Fees retained by the protocol

• Rewards paid from the protocol’s own assets

• Operating costs

• Changes in the value of its liquidity positions

Market making can leave a pool holding more of an asset as its price falls. That inventory exposure needs to be assessed alongside fee income, including when the underlying stock market is closed.

After the campaign, recurring volume will tell us how much demand the rewards helped establish. Continued subsidies would increase the cost of maintaining those markets.

5. How This Connects to STANDARD

STANDARD’s investment case depends on where the launchpad’s earnings go

Fees could support reserve growth, token purchases or distributions, depending on the final rules. Those allocations need to be explicit before investors can sensibly include launchpad income in their valuations

There has also been an update to the existing fee model since my September article.

The current whitepaper describes the split as:

The 15% allocation was previously associated with adding liquidity. The purchased tokens now remain available for future distribution, so supply forecasts need to account for them returning to circulation.

Charter economics also continue to change.

A 14 October protocol snapshot showed 970 active Charters, 3,468 open Branches + a 0.35× issuance multiplier:

Using the 700,000-token base rate, that equates to 245,000 STANDARD per day, or approximately 71 STANDARD per Branch, before recycled rewards and any subsequent changes

The team’s 11 October update anticipated a further reduction towards 0.20× and announced support using accumulated tokens. The snapshot above still showed 0.35×

• For Charter owners, future issuance, competing Branches + withdrawal costs all affect the return

• For liquid-token holders, I would focus on how external earnings change supply and the reserve’s productive assets

6. My Predictions

a) Activity will rise during the rewards campaign

First month after launch | Medium confidence

I expect rewards to attract an initial burst of trading, followed by a decline when the campaign ends

Four weeks afterwards, I would want average daily volume to remain at least half of the final campaign week’s level, alongside returning fee-paying wallets. That is the threshold I’ll use to assess retention, after checking for self-trading + reward recycling.

b) Sustained volume will concentrate in a few stock markets

First three months | Medium confidence

I expect a small number of useful stock markets to attract most recurring activity.

Projects will have a reason to return if their users receive competitive execution and can enter or exit positions reliably. Further launches could fragment liquidity if they create isolated markets with little ongoing demand.

Repeat issuers + orders arriving through outside aggregators would strengthen the case

c) Valuation will become more sensitive to external earnings (the equities + crypto markets converge)

First six months | Medium confidence

I expect the initial launchpad premium to fade unless Standard establishes repeat fee income from the new markets.

Two consecutive 30-day periods of positive net contribution, with incentives deducted and inventory changes disclosed, would give me more confidence in assigning value to that business

Persistent subsidies or worsening execution would reduce my expectations

7. How I’d Approach the Opportunity

1. For builders: assess which stock markets are supported, what launch liquidity is available + what users will pay to trade. Distribution through the launchpad becomes valuable when those users stay active

2. For traders: compare actual quotes across venues. Reward eligibility, fees and the cost of exiting a position all belong in the calculation

3. For STANDARD holders: follow reserve deployment and the destination of external fees. Growth in launchpad activity needs a clear route into the token’s economics

4. For Charter owners: model returns using several issuance rates and Branch counts, including the cost of withdrawing

5. BONUS: Snitches get Stitches! turns out you can grass up your fellow inactive bankers for a bounty:

Caveat:

The risks remain substantial; smart-contract failure, changes to permitted parameters, inventory losses, token issuance + weaker demand across Robinhood Chain.

Standard holders also have no fixed-value redemption claim over the reserves.

Standard has an established liquidity base + a concrete plan to expand the markets it serves.

The launchpad could give that capital more ways to earn fees, while introducing new projects and traders to the protocol. Its progress should become measurable through returning users, execution costs + the income left after incentives.

I’ll be following those numbers as the rollout develops.

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: Umia’s $6.1M raise + the investors behind it

2. Uniswap explained + what v4 hooks actually change

3. v4 projects to watch, grouped by hook mechanism

4. Where I think the value goes next

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

Umia has announced the investors behind its $6.1M public token auction, including Galaxy Ventures, DCG, Draper Associates, RenGen, Alpha EV, Maven 11 + Eon Capital. Ten funds participated alongside nearly 700 individual bidders (same auction terms):

Umia gives projects the tools to launch a token, raise capital + govern the business around it. The operating team, intellectual property and treasury sit within a shared legal structure, while decision markets let participants put capital behind the outcomes they expect to create value

That tackles a familiar problem: a successful business doesn’t automatically make its token valuable if the assets and economics sit elsewhere

As I wrote in my quote-post, the interesting part is giving tokenholders a say in how the money gets used:

External projects launching through Umia are the next milestone I’m watching

This newsletter goes out weekly to 7.4k+ subscribers.

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

The Market Around the Token Is Becoming Programmable

We’ve spent the past few weeks following memes that accumulate stocks, fund treasuries & distribute assets to holders (aka “reflections”)

Uniswap v4 hooks help explain why the experiments keep getting more specific, and why this will lay the foundations for the next leg up in innovative onchain mechanisms with tokens

My thesis is that the strongest hook projects will make their markets useful enough for other people to build around them

A mechanism that improves execution, funds development or gives users a reason to return has a better chance of surviving after the launch excitement fades

Some of these experiments will be short-lived… but the ability to change what happens when someone trades opens up a much bigger design space than another token with a different fee split

MCG Live has covered a useful cross-section of this market. Seven of the ten projects below have appeared on the show, with the interviews linked alongside their current product material… so if you haven’t already I’d recommend tuning into their show!

The v4 Hook Origin: Uniswap (UNI)

Uniswap is a decentralised exchange protocol (“DEX”): people swap tokens through smart contracts, using liquidity supplied by other users. Those liquidity providers, or “LPs” deposit assets into pools and can earn fees when traders use them

The pool’s pricing rules do the work of an automated market maker (aka “AMM”). There doesn’t need to be another person submitting the opposite order at the exact moment you trade

Uniswap v4 makes those markets much more programmable; Its hooks let builders add custom behaviour around a pool’s normal actions, without building an entirely separate exchange for each idea

Think of Uniswap as the trading infra; a hook adds rules for what happens when that infrastructure gets used

So what is a v4 hook?

It’s a smart contract attached to a Uniswap v4 liquidity pool, with functions that can run at defined points before or after actions such as a swap or a liquidity change

That lets a pool adjust fees, distribute rewards or coordinate with another protocol as part of trading. The hook belongs to the pool, so the same token can trade in different pools with different rules

Each pool specifies one hook contract when it is created. That contract can coordinate several functions, but v4 does not make every hook upgradeable. Whether its behaviour can change depends on how that particular system was built

This matters when a team adds hooks to an existing token: it may be opening a new market for that asset. The old pool doesn’t automatically acquire the new rules

A recent announcement that Hayden (Uniswap founder) highlighted was with TSV:

Projects to Watch

I’ve grouped these by what their hooks enable. Some span several categories, and the launch platforms support multiple kinds of hook:

A) Launch Platforms + Modular Hooks

Tools for choosing, deploying and distributing custom market rules.

1. Programmable (V4) | ~$5.54M market cap

Custom token mechanics become easier to launch.

Valuation shown is for V4 on Robinhood Chain, not an older Ethereum token with the same name.

Programmable is building the tools for teams to choose how their token’s market behaves

Its current documentation describes two routes: Module Mode, where creators select compatible building blocks without coding, and Custom Launch, where developers bring their own token, hook or supporting contracts. Public launch records identify the contracts so other applications can discover and display the projects.

The important detail is that existing module-based coins keep the versions selected at launch. Adding a new module to the platform doesn’t silently change every existing market.

I like the distribution opportunity here. If more builders start experimenting with hooks, they need somewhere to assemble their ideas, launch them and make the results understandable to traders.

The test for Programmable is whether independent teams keep choosing it and whether their projects sustain activity. A growing list of modules only becomes valuable when people use them

2. Hookr (HOOKR) | ~$15.12M market cap

A market for reusable hooks.

Hookr gives builders a way to compose and reuse market rules:

Its current offering includes early-buy restrictions, fees that scale with trade size, burns, LP rewards and a pot paid to every Nth qualifying buy

That last mechanism is a public counter; traders can inspect the rules before participating

Hookr also offers pools for existing assets and an integration with What The Hook for arbitrage recapture. Its own leverage hooks remain labelled in audit, which is worth separating from the features already offered in the app

For me, the larger opportunity is developers distributing their work across multiple projects and earning royalties when it gets used. A useful hook could reach traders through launchpads and applications they already use

I’ll be watching how much activity comes from outside teams adopting the infrastructure. That is a better measure of demand than trading in HOOKR alone.

Dynamic Fees, Arbitrage + Liquidity Support

Hooks that change the economics of a trade and where its value goes

3. What The Hook (WTH) | ~$5.79M market cap

Return arbitrage value to the market.

When a swap creates a price gap between venues, an arbitrageur can trade the difference; it’s essentially where WTH fits in with the protocol token now trading just shy of $6m:

“What The Hook” brings that process inside the transaction and distributes captured profit according to the integration’s rules:

This is one of the clearest economic use cases in the group: change who receives value from a trading opportunity that already exists

Its current published split allocates 40% to the pool’s designated creator, 20% to the authenticated trader, 20% to active LPs, 10% to WTH + 10% to Hookr. Through other routes, the trader allocation goes to LPs instead. Metrics are currentyl as follows:

Those percentages apply to captured profit, not the value of the swap. An eligible second venue and a profitable gap are needed; there is no automatic payout on every trade

I’d track net execution for traders and total returns for LPs. A cashback headline is only useful if the complete trade is competitive.

4. Fables (PROLOGUE) | ~$5.61M market cap

Fees that respond to the asset being traded.

Valuation shown is for PROLOGUE, the pre-launch claim token. It is not the total valuation of the forthcoming FABLES token.

Fables applies hooks to an issue familiar from our Robinhood Chain coverage: tokenized equities trade around an underlying market with opening hours, closing hours and scheduled events. Currently just below $6m:

With TVL up to $44m:

On MCG, the team described separate fee designs for different assets: market-hours-based fees for equities and volatility-based fees for its ETH/USDG market.

That is a practical reason to customise a pool. A liquidity provider faces different conditions during the US trading session and over a weekend, when the underlying stock market is closed.

The thesis is that fees which respond to those conditions can make providing liquidity more attractive. Whether that works depends on the balance between compensation for LPs and execution costs for traders.

This is a useful counterweight to the more speculative experiments. Better market design can be valuable even when the end user never needs to know what a hook is.

They dropped their roadmap, with the FABLE token transition taking place on October 20th:

B) Treasury Liquidity + Market Governance

5. Umia Finance (UMIA) I $20M

Connect a token’s market to the business around it.

Umia’s auctions seed treasury-owned Uniswap v4 pools. Its deployed UmiaHook restricts liquidity additions and removals to each pool’s registered operator, requires full-range positions and records prices for a time-weighted oracle

That gives the protocol control over its canonical liquidity and a history of market prices. Separate decision-market contracts let traders back competing proposals, with the winning outcome directing actions by the venture’s treasury

Why it makes the list: the hook supports a broader system for raising capital, maintaining liquidity and governing a business. Its function is concrete, even if it is less eye-catching than a weather-driven burn

What could drive momentum: outside ventures launching through Umia and repeatedly using its decision markets. The fundraising announcement has brought attention; the next test is whether teams use the infrastructure after their auction closes

The v4 hook handles pool rules and price observations. Governance runs in separate contracts, and Umia’s auction eligibility hook is a different component again

C) Monetary Policy + Reserve Liquidity

6. The Standard Reserve (STANDARD) | ~$18M market cap

Use market flows to guide monetary policy.

Standard’s ETH/STANDARD pool uses a Uniswap v4 hook to record ETH entering through buys and leaving through sells. The liquid token is trading at $18m

Its central bank contracts use that signal to adjust issuance, with sustained inflows supporting expansion and outflows prompting contraction. Charter NFTs and their branches determine participants’ shares of issuance:

Why it makes the list: the hook does more than redistribute a trading fee. It supplies an input to the protocol’s monetary policy, connecting the market to how the wider system behaves

The new Liquid Launchpad announcement extends that idea. Standard describes the launchpad as the first use of its Reserve, directing liquidity and trading into Reserve-owned pools:

What could drive momentum: outside projects using that liquidity would give Standard a source of activity beyond speculation in STANDARD itself. A working rollout, repeat launches and fees earned on deployed reserve capital are the milestones I’d watch.

Reserve deployment also exposes the protocol to the assets traders sell into its pools. Fee income needs to compensate for that exposure. And non-upgradeable core contracts don’t mean every parameter is fixed: the whitepaper permits owner adjustments within stated bounds, without an enforced notice delay.

The launchpad is an announced development; the pool’s use of a v4 hook is already documented… but the “liquidity engine for tokenized stocks” is a strategially smart angle:

D) Game Theory + Evolving Token Rules

Hooks that connect a changing product mechanic

7. Claus (SAME, formerly CLAUS) | ~$5.38M indexed market cap

The same coin can keep evolving.

Claus is the more unusual end of the experiment: a token on Ethereum (launched via Programmable, mentioned previously) whose hook can be rewritten after launch, allowing new functions around the same asset:

Its published mechanics include buybacks, burns, automatic liquidity additions, NFT rewards + a game where trades create waves. A 2% project fee helps fund the system… there’s been a reference to being “GOAT + hooks” essentially the AI Agent catalyst of the December 2024 AI Agetnt boom/cycle:

The “weather switch” function is particularly bizarre:

Signed reports using London Heathrow observations alter the split between burns and liquidity. Rain favours burns, dry weather favours liquidity, and stale reports fall back to an equal split

Apparently the British weather now needs a tokenomics section.

Claus also announced on 6 October that its ticker changed from CLAUS to SAME, with the contract address and balances unchanged. The character remains Claus.

The idea worth following is whether one token can retain its community while the product develops around it. Whoever controls changes to the hook also becomes central to the risk: holders are exposed to future rules as well as today’s

There’s also an NFT collection that accumulates value by distributing the 2% trading fee to holders + also can be burned for 50k of the fungible token:

Where I Think the Value Goes

Three opportunities stand out to me:

1. tools that help developers launch useful hooks,

2. mechanisms that improve the economics of trading, and

3. applications that give people a reason to return

Applying the above mentioned protocols to these opportunities:

• Programmable + Hookr address the first

• WTH + Fables address the second

• Claus, Umia + STANDARD shows how widely the third could vary

The potential connection to AI agents is interesting too. An agent could compare markets by execution cost, rewards and permitted actions, then route activity through the pool that best fits its task

But that only works if the rules are readable, the integrations are reliable and the agent can enforce its spending limits

For now, my focus is simpler:

1. Repeated usage: are people still using the mechanism once the initial attention fades?

2. Value distribution: how much reaches users, LPs, developers, treasuries and tokenholders?

3. Outside adoption: are other projects choosing to build with it?

4. Control: who can change the rules, and which steps depend on a server, keeper or administrator?

Hooks make it easier to design an economy around a token. They don’t create demand by themselves, and trading fees paid by speculators can disappear quickly when those traders move on

I’m looking for the projects whose mechanism earns continued usage. That could be a better trade, a useful financing tool or a game people want to play. If the activity lasts, there is something meaningful to value

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. Robinhood Chain In Numbers

3. The Next Catalyst Is STONK Components

4. The Ecosystem

5. Robinhood Doesn’t Need to Build Every App

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

Axis’s latest research update reports improving real-robot task success from 22% to 52% by feeding successful real-world attempts back into training alongside its simulation data. The deployed robot helps produce the data used to train its successor

In separate specialist-task experiments, Axis reports near-100% success within its tested settings for $5–$10 in compute per task, then chaining those specialists into longer workflows. That is compute cost, not the full cost of developing or deploying a robot

The interesting part is the move towards reusable capabilities. A reliable grasping skill should help with more than one task, and experience from deployment should make the next version better

These remain team-reported experiments, but they give us something more useful to assess than another impressive-looking demo; particularly as their recent evaluation benchmark was announced with Openroboto

The recent discussion with Brian Armstrong (Coinbase CEO) provides some useful insights into the future of robotics - Link

Axis recently bolstered its capital through a community sale, raising $1m via Echo with an oversubscription of 2.4x - Link

This newsletter goes out weekly to 7.4k+ subscribers.

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

Robinhood (RH) Chain in Numbers

The initial frenzy has cooled, but the onchain numbers going into HOOD Summit are still substantial.

DeFiLlama shows $9 billion in weekly DEX volume, down 13%, whilst weekly perps volume is up 25% to roughly $5 billion:

All the while Stock specific cumulative trading volume surges past $10B in 80 days since the chain went live in July

Robinscan also recorded 126 million transactions between September 15–28, averaging 9 million a day:

You will likely see a drop off in RH Chain fees as transactions over $0.50 are gasless/ sponsored, acting as a catalyst to improve user expderience (UX) and boost activity:

The more interesting comparison is DeFiLlama’s $108k in daily chain fees against $4.9 million in application fees; approximately $46 collected by apps for every $1 collected by the chain. These are gross fees, not profits or revenue accruing entirely to Robinhood

This is why I keep coming back to fat apps, thin chain.

As predicted, the “STONKS” trade is spreading beyond RH too:

• Ondo has stock tokens on BSC and Solana (among others like xStocks or PreStocks), and its September 22 integration brought an initial 20 assets to near.com and NEAR Intents

• On Base, STONKEX lets creators launch stock-paired tokens and distribute fees back to holders in stocks

• Binance announced it would enable distribution of reflection tokens for Marscoin (SPCX stock reflections) and Niu Lai (Invesco); with leading STONK pairs coming out of FLAP

Access to tokenized equities is becoming less exclusive, and the onchain mechanisms that enable distributions of cashflows from these assets is becoming more accepted by infra providers

What keeps pulling me back to Robinhood is the prospect of these experiments reaching people who already use the platform to invest. With HOOD Summit taking place on September 29–30, that is the connection I’m watching

The Next Catalyst Is STONK Components

When I say “Stonk components” I mean the things you can do around just buying and holding tokenized stocks in your portfolio; dividend reinvestment plans, Index funds, leverage and credit so you don’t need to sell the underlying and incur tax penalties!!

My “Hood Summit” base-case is progress on shareholder rights and easier access to financial products around Stock Tokens

But here are some specific topics I think could be mentioned:

• Fully autonomous portfolio managers are further along the risk curve… but I’m banking on this becoming a topic given the broader AI trade + the leading meme pairs tied with NVDA or AI-centric stocks

• MemeFi barbell is an obvious one with explicit acknowledgement from the chain’s inception:

We’ve seen larger public spats between Vlad and the AMC CEO directly as the spike in tokenized purchases of AMC stock drove the price of AMC up artificially, capturing the attention of AMC itself:

• Dividends are a good example. Robinhood already accounts for them through an onchain multiplier that adjusts the share exposure represented by each token. The next useful development would be giving investors more control over that return… there’s been some hints with “DRIP” being their “Dividend reinvestment plan”:

• I’d like to see cash distribution, automatic reinvestment into another stock, or routing into a portfolio strategy. A community could eventually use those proceeds to fund tools or repay borrowing, provided the distribution infrastructure supports it

• Voting is closer to a concrete announcement; Vlad and Johann have already discussed one-for-one share redemption and voting for eligible holders, with Johann pointing to Robinhood’s Say platform as existing shareholder-engagement infrastructure:

That matters for the stock-accumulation communities we’ve been following. Owning a meme paired against NVDA does not give someone a vote at NVIDIA. An enforceable route from token ownership to shareholder participation would make coordinated accumulation more consequential.

• Index funds are the other obvious extension. Tokenized ETFs already exist; the opportunity here is constructing new portfolios from the underlying Stock Tokens

The LONG 500 is its attempt to build a broad tokenized-stock index, with more than 70 stocks supported and new stock-paired launches contributing stock-token fees to the $AI community vault

That broadens the thesis around $AI beyond one NVDA pool. More stock communities could mean a more diversified stream of assets accumulating in the vault

The Ecosystem Is Already Building Around Stocks and Commodities

My prediction is broader markets and a more connected portfolio experience

The useful product is being able to manage stock exposure and an appropriate hedge without navigating several seperate interfaces; that third party ecosystem is already expanding on RH chain:

1. Leverage: Lighter is already available inside Robinhood Wallet for eligible users, and its Robinhood Chain instance has separate liquidity and execution from Lighter Core… hedging strategies are a big benefit here

2. Credit and Lending: Using stock holdings as collateral would be another step, but it needs explicit collateral support and reliable liquidations

3. AI Agents: could make these products easier to use. Robinhood has already launched agentic equities and options trading and announced its expansion into crypto. The next question is how that experience connects to onchain portfolios

Orbio is tackling the operating budget behind an agent. Its launchpad currently routes 50% of creator fees into ORBIO staking, 45% into an AI balance and 5% to the treasury. That budget can fund models and tools… trading fees funds AI inference for agents:

4. Liquidity Engines: Standard Reserve is the capital experiment I’m watching alongside this. Its monetary policy responds to net ETH flows through its own market, with expansion building reserves and contraction directing resources towards buybacks and burns

My interest is whether this can develop into a useful liquidity reservoir for stock markets. As more portfolios, lending markets and trading strategies appear, attracting capital is only half the job. Deploying it where other people genuinely need to trade is the harder half

I would judge that thesis on stock-market liquidity and fees from outside users, rather than activity circulating primarily around $STANDARD itself

Robinhood Doesn’t Need to Build Every App

Robinhood reported 28.6 million funded customers, $384 billion in platform assets and $335 billion in equity trading volume during August:

But there’s a working example of how the distribution could develop…Robinhood Earn already brings Morpho-powered lending into the main Robinhood app. An outside protocol provides the lending infrastructure, while Robinhood makes the product accessible through a familiar interface

The potential moat combines funded customer relationships, stock issuance and corporate-action infrastructure, institutional liquidity (through Bitstamp), and distribution through Robinhood’s products. Bitstamp already gives trading firms and market makers access to Stock Token inventory and liquidity

• Another chain can list NVDA, but recreating that customer relationship is harder

• For the investor, the attraction could be a stock portfolio that is easier to rebalance, borrow against or hedge

• For the developer, it is access to customers who already have capital and investment intent

This becomes more defensible if useful apps attract more assets, those assets support deeper markets, and better execution gives customers another reason to stay. It depends on eligibility, integration and product quality, not simply putting a Robinhood logo beside a permissionless app:

Going into the HOOD Summit, LONG/AI, Pons, Orbio and Standard Reserve remain the experiments I’m most interested in. Each gives me a specific thesis to test against usage, fee income and assets accumulated

I don’t expect every prediction to ship this week. I’m looking for announcements that shorten the distance between the apps being built and the investors who could use them

Robinhood already has an audience that wants to invest. Its moat will depend on making onchain products useful enough that those customers choose to use them, without needing to become crypto experts first…

So if you’re exposed to the RH eco then it’s probably worth paying attention to what’s taking place at the HOOD summit, starting in 4 hours 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

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: 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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Its network of more than 200,000 contributors have added > 5 million trajectories now; completing robotics tasks through browser-based simulations, generating the demonstrations and corrections required to improve real-world robot models:

You don’t need a robot, GPU or technical background. The simulations run locally in your browser, while completed trajectories are signed and verified onchain

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.

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

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

This newsletter goes out weekly to 7.2k+ subscribers.

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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.

This newsletter goes out weekly to 7.2k+ subscribers.

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

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.

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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.

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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.