See you on the other side that prints. 💰
Cape
Joined March 2022
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𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
What if the next big opportunity on $HYPE isn't another trading platform, but everything being built around it?
→ @Enter_Elysium wants to bring more apps, assets, and liquidity to @HyperliquidX.
Its mainnet goes live November 9.
◢ Here’s what you should know; ↓
→ Built by @Kinetiq_xyz using Arbitrum Orbit and @conduitxyz.
→ Elysium is an EVM Layer 2 targeting;
• 100ms block times
• 300 Mgas/s throughput
• $HYPE for gas
It connects Elysium, HyperEVM, and HyperCore to bring applications closer to Hyperliquid’s trading infrastructure.
◢ HyperCore's integration
A planned upgrade could let Elysium apps access HyperCore’s order books, prices, balances, and positions.
This could improve liquidity management and trading efficiency.
◢ How demand comes in
The idea is simple:
Token launches → DEX liquidity → More traders → More trading activity.
The real test will be whether these applications attract sustainable demand and liquidity.
◢ The Revenue model
Proposed sequencer revenue allocation:
→50%: funds $KNTQ purchases and burns
→25%: to builders
→25%: to Kinetiq's treasury
More activity could also create additional fee opportunities for hyperliquid.
◢ Ecosystem Map
→ Launchpads: @AscendLaunch, @SignalFam, @oxleydotfun, @CorePad_hl, @perpmefun, @Neuriio, @ChainzyLabs, @MontraXYZ, @citadeldotxyz, @kilnpad, @Bunnypadfun.
→ NFTs/marketplaces: @kalosfun, @AtlasHL.
→ DEXs and liquidity: @HyperFlow_fun, @NestExchange, @HybraFinance, @dawndottrade.
→ Derivatives and prediction markets: @hyperflip_xyz, @speculatexyz, @LiquidictionHL, @Bound_Exchange, @BranchpointXYZ, @outrivexyz.
→ Trading and DeFi: @Elypact, @Pantheonindex, @Pindar_finance, @truemarketsco.
→ Analytics/infrastructure: @liquidterminal, @ElysiumEco, @Ellytradebot, @arbitrum, @conduitxyz, @Alchemy, @ChainstackHQ, @Quicknode.
→ Gaming/consumer apps: @eloscapeHQ, @playelysa, @clashforelysium, @Hyperion_RPG.
→ Naming infrastructure: @ElysiumNames.
◢ How to bridge to elysium with no fees
>deposit usdc on @HyperliquidX and send usdc on $arb to hyperliquid.
> move your usdc from perps to spot, buy hype in the hype/usdc spot market.
> in the hyperliquid app, transfer your hype from hypercore to hyperevm, make sure you keep a little extra for gas.
>then bridge hyperevm to elysium, use blobmeme(.)com/bridge. make sure to remove the brackets in the site "()"
there are actually other bridges like volta which had a 2% fee, but this is the only one currently with zero fees.
➤goodluck anon, NFA!
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
We all thought @arc had been forgotten, but its TVL has been quietly growing while everyone was looking elsewhere.
I went through DefiLlama's data, and @arc is now sitting at roughly $633M in DeFi TVL.
What's even more surprising is where that money is going:
• @Morpho Blue: $298.5M
• @aave V4: $184.7M
• Extended: $106.3M
That's nearly $483M sitting in Morpho and Aave alone, accounting for about 77% of Arc's TVL.
And lending isn't the only activity picking up.
Arc also has around $1.16B in stablecoin supply, with weekly perpetual trading volume approaching $1.9B.
But I wouldn't rush to call this organic growth just yet.
Depositing capital into lending protocols is one thing. Having people actively borrow against it is another.
I'd be watching:
• how much of that liquidity is actually being borrowed
• whether trading volume holds up
• how much capital stays when incentives slow down
@arc may have gone quiet on the timeline, but the numbers suggest people haven't stopped putting capital to work there.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
Your AI agent might be smarter than you, but its payment system is more expensive than yours.
And that's a problem crypto is quietly trying to solve.
How do these agents actually pay for things?
➤ The hidden cost of agent payments
AI agents often can’t simply use your personal debit card. They rely on virtual cards issued through their developers or agent startups.
These transactions can be classified as commercial card payments, which often carry higher interchange fees.
In some cases, merchant costs can reach 2.5–3%.
A $100 purchase could cost a merchant $2.80 instead of $0.27.
Imagine millions of agents making purchases at that cost.
That’s a serious problem for merchants.
➤ Enter crypto-native payments.
What if agents could pay directly using wallets and stablecoins?
No traditional card issuance, fewer intermediaries, and programmable settlement.
This is where x402 comes in.
Built around HTTP 402 Payment Required, x402 enables internet-native payments, allowing agents to pay for API calls, services, and digital resources programmatically.
Instead of subscriptions, imagine paying for exactly what an agent consumes.
➤ The ecosystem is already forming.
I mapped startups building this infrastructure:
Agent cards & tokens: @agentcardhq @prayapayments @getpayos @evervault @basis_theory
Wallets & accounts: @crossmint @openfort_hq @turnkeyhq @1shotapi @HallidayHQ
Banks & networks: @naturalpay @catena_labs @GoKiteAI @PaymanAI + ATXP
Pay-per-call & x402: @daydreamsagents @merit_systems @PayWithLocus + Nevermined, Lava
Trust & identity: @VouchedID @trySkyfire @sapiom @t54ai @Mnemopay
Checkout & merchants: @rye @nekuda_ai @bitrefill @meshpay + Firmly
➤ The bigger opportunity
AI agents will need wallets, stablecoins, payment rails, identity, spending controls, and settlement infrastructure.
That’s an entirely new financial infrastructure market.
My takeaway:
The next wave of crypto adoption might not come from humans buying tokens.
It could come from millions of AI agents using stablecoins to pay for software, APIs, services, and real-world goods.
We’ve built financial infrastructure for humans.
Now we’re building it for machines.
The killer use case for crypto might not be humans using crypto.
It might be machines.
@BlackRock latest paper on the “machine-native economy” lays out why.
➤ The thesis is pretty simple:
AI = machine-native intelligence
Crypto = machine-native money
And as AI agents become capable of actually doing things, this starts getting interesting.
Agents won’t just answer questions. They’ll call APIs, buy data, hire compute, talk to other agents and even execute transactions.
And they’ll need to pay for all of it and that’s where things like x402 come in.
Instead of a human pulling out a card every time an agent needs an API, an agent can potentially pay programmatically; including tiny, high-frequency payments.
That’s a very different payment environment from the one we’re used to.
But the part I found even more interesting:
“compute itself could become a financial asset.”
BlackRock points to $1.1T in projected 2030 revenue across AWS, Microsoft Intelligent Cloud and Google Cloud segments.
As compute becomes scarce and increasingly expensive, you can imagine markets emerging around claims on compute capacity.
An agent could eventually:
➤ discover available compute
➤ compare price/performance
➤ provision it
➤ pay for it
➤ use it
➤ settle everything programmatically
What I’m saying basically is:
AI agents become economic actors.
And this is where the AI + crypto convergence starts making more sense to me.
If machines are going to participate in the economy, they need machine-readable money, assets and settlement.
The interesting stack is already taking shape:
MCP → agents access tools/data
A2A → agents communicate
x402 → agents pay
Stablecoins → machine-native money
Blockchains → settlement
Compute markets → machine-accessible resources
@AnthropicAI , @Google , @coinbase , @stripe , @OpenAI , @Visa and others are already building pieces of this infrastructure.
The ecosystem is obviously still early.
But the direction is fascinating:
humans → agents → agents → machines → money → compute
The next economy may have a lot fewer humans clicking “buy.”
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
➥ 11% apy means nothing until you ask where is it comes from
so here’s where @grvt_io says it comes from.
four yield sources
•real world asset financing
•tokenized fixed income
•crypto credit
•market neutral strategies
this sources are very important because most yield products lean on one engine.
when that engine stalls, the yield stalls with it
four independent sources means one weak spot doesn’t sink the whole thing.
exposure limits are also built in to reduce concentration risk
maintain moderate risk.
Code: 9HW7U3Z
grvt.io/?ref=9HW7U3Z
dyor, not financial advice
Idle stablecoins earn nothing.
@grvt_io just launched High Yield Earn up to 11% target APY
An 11% target APY got my attention.
What matters more to me is where that yield comes from.
GRVT High Yield Earn combines traditional finance and crypto strategies.
they including tokenized fixed income, real-world asset financing, crypto credit and market neutral strategies.
The underlying mechanics deserve a closer look.
Whitelist is open 👇
Code: 9HW7U3Z
grvt.io/?ref=9HW7U3Z
DYOR.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
How do you know an AI agent actually did the work it claims to have done?
This is one of the bigger problems in the agent economy.
You can't simply take an agent's word for it.
You need a way to verify its work without necessarily exposing the data behind it.
That's where the two @termix_ai technologies become interesting: TEE and zkVM.
1. TEE (Trusted Execution Environment)
Think of it as a secure room inside a computer chip.
Code runs in an isolated environment.
Sensitive inputs stay protected from the rest of the system.
Useful when agents handle private information
2. zkVM (Zero-Knowledge Virtual Machine)
Think of this as a machine that can produce evidence that a computation was executed correctly.
• Executes a program.
• Generates a cryptographic proof of its execution.
• Allows another party to verify the result without repeating the entire computation.
3. Why does this matter for AI agents?
Agents are moving beyond answering questions.
They're beginning to handle tasks, process data, interact with applications and execute workflows.
But if an agent is going to handle valuable work, users need more than a completed task notification.
They need evidence.
TEE helps protect the execution environment. zkVMs help establish computational correctness.
They approach different problems, but together they point toward something important which is private execution with verifiable results.
The next phase of AI agents won't just be about what they can do.
It will be about whether anyone can independently verify what they did.
That's a much bigger conversation than simply making agents smarter.
The agent economy becomes interesting when agents can do more than complete tasks.
when they can find work, follow instructions, and get paid.
I think that's the next challenge for autonomous agents.
Giving an agent more skills is useful, but skills alone don't create an economy.
There also needs to be a way to connect those skills to real demand and handle payment.
That's why I'm looking at what @termix_ai is building with Agent Skills.
They announced integrations include Grok, Muse, and Claude, although the exact capabilities may differ across agents.
I'm interested in seeing whether this can move beyond a good concept and become a practical way to get useful work done.
Want to explore it yourself?
Check out the TermiX Agent Skills and follow the installation guide.
Personally, i think the agent economy won't be built on intelligence alone, It needs a way for agents to turn skills into income.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
So we're getting Primus sooner than later expected..
@primus_labs just dropped a teaser about this NOV likely to be their TGE
I joining primus_labs on @KaitoStudio_
cause the allocation was worth looking at.
0.3% of $PRIM’s total supply is allocated to the campaign, with 0.24% for creators and 0.06% for Kaito stakers.
That’s a larger creator allocation than Axis’s 0.25% campaign pool.
Second, there’s still time to get involved.
Epoch 2 runs until November 21, followed by Epoch 3 from November 22 to December 21.
Points carry across epochs, and only your four best posts count per epoch.
Don’t rush to flood the timeline with low-effort content.
Use the time to understand what Primus is building and publish a few posts that actually explain the tech.
Its zkTLS technology verifies web data without exposing the underlying information, while zkFHE enables computation on encrypted data.
Privacy is becoming a bigger conversation in crypto.
The opportunity here is to understand the technology, contribute something useful, and let the quality of your work speak for itself.
Still early enough to make your entry count.
Make sure you start earning your XP here: s.kaito.ai/zJlvAyo
Most privacy solutions only address part of the problem.
They either hide everything or reveal everything.
Neither is enough when real capital and real decisions are involved.
What's missing is the ability to prove facts without exposing the underlying data, and to compute on sensitive information without decrypting it.
That's the gap Primus is building to address.
Not more secrecy.
Not more transparency.
But a way to keep sensitive data private while making it usable and verifiable.
Primus is building that cure.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
Found a crazy opportunity for creators who are actively on here.
@FungoLabs just launched its $FUNGO campaign and they're putting 300M $FUNGO into its content campaign.
but the scoring rule is what I noticed.
Only your 5 best posts count toward the campaign.
That puts more weight on the quality of your content than on how often you post.
few details you might want to note:
→ Eligible posts must have been published from November 1, 14:00 UTC.
→ The reward pool is 300M $FUNGO.
→ Rewards are scheduled for distribution at TGE.
I prefer campaigns where the rules are clear and good research has room to stand out.
you should check it out : fungolabs.org/launchpad/c/cf…
1/6
10 billion $FUNGO: Who gets how much?
Amounts, tokenomics and claim rules for Genesis NFT, Zama OG and the X Campaign.
Details 👇
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
An AI agent wants to buy $0.10 worth of market data.
The data costs 10 cents, but processing the payment through a conventional card could cost another 30 cents.
Imagine that same agent making thousands of small purchases every day.
Suddenly, the cost of moving money becomes a bigger problem than the service itself.
This is one reason I've been paying closer attention to the 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀.
And the numbers are getting difficult to ignore.
According to @chainalysis, x402 recorded approximately 138.5 million cumulative transactions on Base through Q3 2026.
Over 87% of the payments analyzed were worth $0.10 or less.
That's a fascinating payment pattern because AI agents don't necessarily spend like humans.
An autonomous research agent might need to purchase market data, pay for inference, access an API, and run security checks, all within a single workflow.
Charging a conventional checkout fee for every tiny purchase becomes economically inefficient.
𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝘅𝟰𝟬𝟮 𝗲𝗻𝘁𝗲𝗿𝘀 𝘁𝗵𝗲 𝗽𝗶𝗰𝘁𝘂𝗿𝗲.
Developed by @CoinbaseDev, x402 allows software to:
→ request a resource
→ receive payment instructions through HTTP 402
→ authorize a payment
→ access the service after verification
Coinbase's facilitator offers 1,000 free monthly settlements, followed by $0.001 per settlement under its published pricing.
But payment settlement is only one part of this emerging market.
I've been following several companies building different pieces of the infrastructure:
• @CoinbaseDev — x402 payment infrastructure for machine-to-machine transactions.
• @crossmint — Agent wallets, stablecoins, existing card integration, and merchant checkout.
• @turnkeyhq — Wallet infrastructure that developers can integrate into agent applications.
• @evervault and @basistheory — Secure payment credentials and sensitive-data infrastructure.
• @nevermined_ai — Usage-based payments, metering, and x402 settlement for AI services.
• @Stripe — Shared Payment Tokens that allow agents to initiate authorized purchases without exposing underlying card credentials.
• @Visa — Intelligent Commerce and Trusted Agent Protocol for recognizing and authorizing agent-driven transactions.
• @Mastercard — Agent Pay for Machines, combining agent credentials, spending permissions, and multiple settlement rails.
Notice something here?
𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝘀𝗶𝗺𝗽𝗹𝘆 𝗰𝗿𝘆𝗽𝘁𝗼 𝘃𝗲𝗿𝘀𝘂𝘀 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗮𝘆𝗺𝗲𝗻𝘁𝘀.
It’s about which infrastructure can give machines purchasing power without sacrificing cost efficiency, security, or accountability.
And there’s still an uncomfortable question:
If an AI agent makes an unauthorized purchase, exceeds its spending limit, or pays for a service that never arrives, who takes responsibility?
Cheap settlement doesn’t automatically solve those problems.
There’s also a reason to be cautious about the adoption numbers. Chainalysis identified speculative activity contributing to earlier x402 growth, so transaction counts alone don’t prove sustainable commercial demand.
My expectation is that agent payments will develop across multiple rails:
▸ Stablecoins and x402 have compelling economics for micropayments.
▸ Card networks retain established merchant acceptance and dispute mechanisms.
The infrastructure that connects both while enforcing verifiable spending permissions, could become an important part of autonomous commerce.
AI agents are learning to do work independently. The next challenge is allowing them to pay for that work without losing human control over the money.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
Crypto tokens are getting easier to value when protocols return revenue to holders.
Buybacks and burns are becoming a bigger part of token economics.
Zeneca’s research puts annual spending at $1.85B for the year to October 2026, up from $190M in 2021.
But a protocol buying its own token doesn’t automatically make that token undervalued.
Here are six models I’m tracking:
→ $HYPE: Trading fees fund HYPE purchases through Hyperliquid’s Assistance Fund. The tokens are burned.
→ $PENDLE: 80% of V2 yield and swap fees fund buybacks distributed to eligible active sPENDLE holders.
→ $PUMP: Its current program commits 50% of revenue to buybacks and burns for one year from April 2026.
→ $SKY: Protocol surplus funds SKY purchases for burns or staking rewards.
→ $RAY: 12% of pool trading fees fund RAY buybacks.
→ $STONK: The project has spent money buying STONK, but weaker recent revenue makes it harder to assume that pace will continue.
→ $PONS: Its published V1 model directs 80% of protocol fees to PONS buybacks and burns.
The differences matter.
Some tokens are burned, while others are bought and distributed to holders.
Emissions and unlocks can also offset buybacks.
When comparing these tokens, I look at three things:
• Revenue: Is it holding up over 30–90 days?
• Supply: Are buybacks large enough to matter alongside emissions and unlocks?
• Valuation: How much revenue growth is already priced in?
A quick starting point is market cap divided by annual buyback spending.
A $1B token with $50M in annual buybacks trades at 20x that spending.
That isn't an earnings multiple, and it only helps if the buybacks can continue.
• I use DefiLlama to compare revenue
• CoinGecko for market cap and FDV
• Tokenomist for unlocks
• Block explorers to check buyback and burn transactions.
if want to hold any of these tokens, you’ll want to focus more on some of these protocols
but focus more on the ones that keep earning while returning enough value to token holders.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
Idle stablecoins earn nothing.
@grvt_io just launched High Yield Earn up to 11% target APY
An 11% target APY got my attention.
What matters more to me is where that yield comes from.
GRVT High Yield Earn combines traditional finance and crypto strategies.
they including tokenized fixed income, real-world asset financing, crypto credit and market neutral strategies.
The underlying mechanics deserve a closer look.
Whitelist is open 👇
Code: 9HW7U3Z
grvt.io/?ref=9HW7U3Z
DYOR.
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One balance. Two jobs.
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𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
Creator Season 1 is live.
Create content about Sight and robinhood:0x238e40b75ae78a1388a14e517d855893e92e58db, earn points and compete for USDC and other rewards.
Your content. Your points. Your rewards.
Start here: creator.sighthood.com
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
AI agents are creating a new payments market, but the rails they use will determine whether autonomous commerce is economical at scale.
Commercial cards can carry interchange fees of 2.5–3%. Meanwhile, 138.5M reported x402 transactions on Base by Q3 2026 point to growing use of stablecoins for machine-to-machine payments.
Here’s how the agent payment stack is evolving.
—
● AI Agents Need a New Payment Stack
Unlike humans, autonomous agents continuously purchase API calls, datasets, compute, and inference without manual checkout.
That activity creates four infrastructure requirements:
• Programmable spending through budgets and transaction limits.
• Machine-native payments without human intervention.
• Low-cost settlement for micropayments.
• Verifiable authorization to establish who controls an agent’s spending.
Wallets, stablecoins, payment APIs, and identity protocols are converging around these needs. But the payment rails underneath them carry very different costs and incentives.
—
● The Hidden Cost of AI Agent Cards
AI agents using commercial virtual cards can trigger interchange fees of 2.5–3%, creating a recurring cost for merchants.
On a $100 purchase, the cited estimate puts fees at $2.80 for commercial agent cards versus $0.27 for regulated debit.
Agent startups and card issuers may share this revenue, giving them an incentive to preserve expensive card rails even as autonomous payments scale.
This cost structure underpins the crypto thesis: stablecoins could provide a more economical payment layer for agentic commerce, particularly for small, frequent purchases.
—
● x402 Brings Payments Into HTTP Requests
One approach combines wallets, stablecoins, and x402, allowing agents to pay for resources directly through HTTP requests.
Agent requests an API, dataset, or compute resource → Server returns HTTP 402 with payment terms → Agent authorizes a $USDC payment → Facilitator verifies settlement → Resource is delivered.
By embedding payment into this interaction, x402 removes conventional checkout friction and enables programmable payments for individual API calls.
That includes micropayments worth just a few cents, where conventional payment fees can exceed the purchase itself.
—
● Micropayments Change the Economics
The source compares traditional card pricing of 2.9% + $0.30 with modeled stablecoin processing at 1.5% and x402 settlement at approximately $0.001.
• On a $0.10 payment, modeled card fees reach $0.3029 versus $0.001 for x402.
• The source places the card-to-stablecoin cost crossover near $21.43 under its assumptions.
For agents buying inference, API access, and data in small increments, settlement costs directly affect which transactions are commercially viable.
This makes micropayments a natural entry point for stablecoin-based agent payments.
—
● x402 Adoption Is Concentrated in Micropayments
@chainalysis data cited in the research reports 138.5M cumulative x402 transactions on Base by Q3 2026, with over 87% below $0.10.
• The reported time from funding to first payment fell from 73 days to around 18 minutes.
• @CoinbaseDev offers 1,000 monthly settlements free, then charges $0.001 per settlement.
These figures point to growing transaction activity and faster payment activation.
However, transaction growth alone does not establish sustainable demand or revenue. The wider opportunity depends on the infrastructure connecting payments to usable agent services.
—
● The Agent Payment Stack Spans Six Layers
The original ecosystem map identifies 30 startups building across different parts of agentic payments.
• Cards and tokens: @agentcardhq, @pravapayments, @getpayos, @evervault, @basistheory
• Wallets and accounts: @crossmint, @openfort_hq, @turnkeyhq, @1shotapi, @HallidayHQ
• Banks and networks: @naturalpay, @catena_labs, @GoKiteAI, @PaymanAI, ATXP
• Pay-per-call and x402: @daydreamsagents, @merit_systems, @PayWithLocus, Nevermined, Lava
• Trust and identity: @VouchedID, @trySkyfire, @sapiom, @t54ai, @Mnemopay
• Checkout and merchants: @rye, @nekuda_ai, @bitrefill, @meshpay, Firmly
Together, these projects cover payment credentials, wallets, settlement, identity, and merchant checkout.
The market is developing across multiple layers because executing a payment is only one part of enabling an agent to transact reliably.
—
● Crossmint Offers an Alternative to Agent-Issued Cards
The commercial-card cost structure is not inevitable. @crossmint enables users to bring their own cards instead of relying on provider-issued virtual cards.
• Provider-issued cards : May trigger higher commercial interchange fees.
• User-provided cards : May retain consumer-card economics, subject to issuer and network rules.
• Stablecoin wallets : Enable programmable, onchain payments.
This gives agent platforms multiple payment routes. Letting users connect existing cards or wallets can help avoid the higher commercial-card fees associated with some provider-issued products.
The broader competition therefore includes both new settlement rails and better ways to connect agents to existing payment methods.
—
● Traditional Payment Giants Are Building for Agents
Crypto-native startups are part of a wider race. Stripe, Visa, Mastercard, and Coinbase are building infrastructure to capture agent-driven payments.
• @Stripe: Link for agents, MPP, ACP, Treasury for Agents, and Shared Payment Tokens.
• @Mastercard: Agent Pay and AP4M, including signed vouchers integrated with x402.
• @Visa: Intelligent Commerce and Trusted Agent Protocol.
• @Coinbase: x402 and Coinbase Developer Platform for onchain settlement.
• @AWS, @Cloudflare, and @tryramp are also developing agent payment infrastructure and spending controls.
These providers approach the market with different strengths. Crypto offers cheaper micropayments, while established networks bring merchant acceptance, consumer checkout, and dispute handling.
Connecting those strengths will require more than low-cost settlement.
—
● Trust Remains a Critical Layer
Cheap payments have limited value if merchants cannot verify an agent’s identity, spending limits cannot be enforced, or disputed transactions remain unresolved.
Three capabilities matter:
• Know Your Agent (KYA): Verify the agent, its controller, and its spending authorization.
• Spending controls: Enforce budgets, transaction limits, and merchant restrictions.
• Liability and reconciliation: Handle unauthorized payments, failed purchases, and transaction records.
Projects like @VouchedID, @sapiom, @t54ai, and @Mnemopay are building around this trust layer.
Stablecoins reduce payment friction, but they do not inherently provide card-like chargebacks or consumer protections. Those capabilities need to be addressed across the broader payment stack.
—
The long-term opportunity lies in making autonomous payments economical while maintaining authorization, security, and reliable settlement.
x402 and stablecoins have an advantage in micropayments. Established networks retain distribution, merchant acceptance, and consumer protect
ions.
The market will be shaped by transaction economics, the merchants agents can reach, and how effectively these systems connect.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
AI agents can write code, analyze data, and complete tasks.
But there is still a gap between an agent that can complete a task and one that can reliably earn money doing it.
Imagine an agent that can analyze blockchain data. It may be good at the job, but where does it find paying customers?
How does a buyer know the work is worth the price? And what happens when the result falls short?
These are problems of trust, payment, and coordination. Better models alone won't solve them.
✧ For agents to do business with each other, four things need to work:
→ Discovery: Buyers need to find the right agent.
→ Agreements: Both sides need clear terms before work starts.
→ Payment: Agents need a reliable way to get paid.
→ Accountability: Buyers need a way to check results and challenge poor work.
Without these basics, agents may remain useful tools rather than independent service providers.
How TermiX approaches it
@termix_ai is working on this through its Agent Autonomous Commerce Protocol and the agent.family marketplace.
Its documented features include agent identities, job listings, quotes, on-chain escrow, records of submitted work, and a process for handling disputes.
The idea is straightforward: make the terms of a job and its payment easier to verify.
But having these features doesn't automatically mean the marketplace works well.
That depends on how people use it and whether the system handles real disputes fairly.
Sources: TermiX marketplace · AACP whitepaper
The biggest question is demand.
Agent registrations and job listings don't tell us how many tasks are actually paid for, completed successfully, or repeated by the same customers.
Dispute resolution also needs to work when a task has no clear right or wrong answer.
TermiX must show that its approach offers a real advantage over existing marketplaces.
TermiX is addressing a real problem, but the idea alone isn't enough.
I'll be watching whether people actually pay for work through the marketplace, return to use it, and trust the results.
That's what will separate a useful product from an interesting one.
Ok guys, I’ll be joining the termix train..
Read that @termix_ai is backed by @yzilabs
Termix is building around a commercial layer, and its AACP has now launched an agent marketplace.
I'll be watching how agents are listed, how users interact with them, and whether the marketplace creates lasting demand.
so going foward expect more bullish content about em.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
What happens when an AI agent can go out, find a job, do the work, and actually get paid?
Sounds a little strange at first.
But that’s exactly the kind of future @TermiX_ai is building toward.
And no, the money isn’t for AI agents to “spend” 😅
It’s about giving them a way to pay for the resources they use and get compensated for the services they provide.
Think of TermiX as a marketplace for AI agents.
Agents can find jobs, offer services, complete tasks, and get paid for the work they do.
Here’s how the pieces fit together:
-AACP — the protocol that helps agents coordinate and do business with each other.
-agent.family — the marketplace where agents can find work and offer their services.
-Onchain verification + reputation — helps prove what was done, track performance, and build trust over time.
-Escrow + settlement — handles payments between agents and their clients.
The bigger picture is what makes this interesting.
AI agents may not always be just tools we give instructions to.
They could eventually become independent service providers that find work, complete tasks, get paid, and build a reputation based on what they actually deliver.
That’s the kind of agent economy @TermiX_ai is working towards.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
Privacy in crypto is worth looking at beyond $ZEC.
At a reported $22.3B market cap, Zcash is valued at roughly 39x the combined $567M market cap of four other privacy projects.
Here's where they differ:
→ $ZANO ($60M): Private-by-default L1 and confidential assets.
→ $ZEN ($109M): Privacy infrastructure built on Base.
→ $RAIL ($149M): Private transactions and DeFi across EVM chains.
→ $ZAMA ($200M): Encrypted computation using FHE.
these aren't direct competitors.
They approach privacy from different angles, and that matters when assessing their potential.
If demand for privacy grows, I don't expect the market to focus on payments alone.
Confidential assets, private DeFi, and encrypted computation could all matter.
$ZEC has the market's attention. But if onchain privacy expands.
I want to see which other projects can turn their technology into real demand.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
The next big RWA use case might not be tokenizing more assets. It might be making existing assets useful across DeFi.
That's how I see it.
A tokenized Treasury is useful, but its value goes further when it can be used as collateral without being sold first.
Think about $100M in tokenized Treasuries.
If it sits in custody, that capital has limited use.
If a lender can verify its price and accept it as collateral, it can back a loan, provide trading margin, or support stablecoin borrowing.
The same capital can now do more.
This is where I think the RWA market gets interesting.
→ chainlink’sFulcrum launch brings more attention to collateral mobility.
→ @Ondo 's SPYon and QQQon are being used as collateral through @Morpho.
→ @Chainlink price feeds also support Ondo assets used for borrowing on protocols like @eulerfinance.
The challenge is making these assets usable across chains, lenders, and trading venues without adding more settlement risk.
For me, RWA adoption isn't just about how much gets tokenized. It's about how much of that value can actually be put to work.
That's something I'll be keeping my eyes on...
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
> $GRVT is processing billions in perp volume, yet its market cap sits around $25M.
That gap is worth looking into.
According to DefiLlama:
→ $430M open interest
→ $14.2B in 30D Prep volume
→ $33M Money raised
→ $34M TVL
→ $23.4M Market cap
→ $442B cumulative perp volume
But high volumes doesn't automatically mean a token is undervalued.
What matters is how much trading activity translates into revenue, how that value reaches $GRVT holders, and how the token's large locked supply affects future valuation.
With GRVT 2.0 ahead, I'll be watching revenue, user growth, and user retention than another volume milestone.
There's more happening on @grvt_io with assets moving between chains been one of them.
Firstly, GRVT added support for $USDG deposits from Robinhood Chain and $USDC deposits from Arc, giving users more ways to fund their accounts.
But the attention goes beyond new deposit routes.
GRVT has also shared updates on its business fundamentals, buybacks, and long-term plans.
Now the numbers to watch will be user growth and whether this momentum will hold.
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
$MET is generating more revenue than $JUP and $RAY, but the market is valuing it much lower.
That’s the part of Solana DEX tokens I’ve been looking at.
Over the past 30 days, based on current figures:
→ $MET: $4.75M revenue on a $170M market cap
→ $JUP: $4.35M revenue on a $965M market cap
→ $RAY: $3.7M revenue on a $481M market cap
Annualizing those figures puts $MET at roughly 3.0 revenue, compared with 18x for $JUP and 11x for $RAY.
But there’s a reason I’m not calling this an easy trade.
Raydium generated $2.1M in holder revenue, while Meteora generated just $263K.
More protocol revenue doesn’t automatically mean more value for the token.
Meteora’s DLMM, DAMM and bonding-curve products give it exposure to Solana trading and token launches.
If activity grows, revenue could rise. If trading cools, those numbers can fall just as quickly.
Token unlocks and the gap between circulating market cap and FDV also matter..
We'll see whether $MET’s lower valuation is a mispricing or reflects weaker returns to holders.
I’d lean toward $MET over the next 6 months let's see..
𝐆𝐞𝐫𝐚𝐥𝐝𝐂𝖗𝐲𝐩𝖙 人 retweeted
Robinhood’s real DeFi advantage may be distribution, not technology.
Robinhood Chain reportedly accounted for 53% of Uniswap’s September protocol revenue.
That’s a big number, but it doesn’t tell us which activity drove it.|
The model I’m watching is simple:
Broker users → tokenized stocks → DEX trading → fees.
@RobinhoodCrypto Wallet gives eligible users access to Stock Tokens, with Uniswap among the execution venues.
That creates a route from familiar financial assets into onchain markets.
If that flow brings repeat traders, LPs have more reason to provide liquidity.
Better liquidity can improve execution and attract more trading.
But the loop only works if activity lasts beyond the initial launch.
A few projects I’m tracking across this ecosystem:
→ $UNI / @Uniswap: Does the revenue last, and does any of it translate into value for UNI holders?
→ @fablesfi: Uses Uniswap v4 pools with fees that adjust to stock-market hours and crypto volatility.
→ @TradePools & @ponsdotfamily: Both target token-launch activity, with PONS using Uniswap v3 and TradePools using v4.
This adds another source of trading, but speculative launches may produce very different liquidity patterns from tokenized stocks.
→ @Lighter_xyz / @Morpho: Lighter brings perps into the Robinhood Wallet ecosystem, while Morpho powers Robinhood Earn.
The key metrics are trading activity, lending demand and fees.
The 53% figure is a starting point, not proof that Robinhood users are driving Uniswap’s growth.
Robinhood can bring users onchain, but that alone doesn't guarantee lasting liquidity or value for every token in the ecosystem.
I’d be watching whether liquidity holds up, and if any of that growth creates real value for the tokens involved.
