Altcoin model portfolio: the AI-agent thesis behind 15 picks

I went through ilme aalim's explanation of an altcoin model portfolio, and the main idea is a bet on software that AI agents could use to manage money. The creator presents a 79-page Citrini Research report as the basis for a basket of 15 projects that excludes Bitcoin and Ethereum. I read that as a specific investment thesis about future users, not evidence that Wall Street as a whole has agreed on these tokens.

The bet on AI agents

The report's central argument, as the creator describes it, is that crypto has found a practical user in agentic AI. An AI agent is software that can carry out tasks toward a goal with some degree of independence. In this thesis, those tasks extend to payments, borrowing, exchanging assets and managing funds across crypto networks.

The creator attributes a strong judgment to the report: earlier crypto activity was largely gambling or a bubble, while agents could provide a more useful source of demand. That is the report's interpretation of crypto's development. It is not a neutral description of everything people have previously done with these networks.

What interests me is the distinction between demand from people trading tokens and demand from software using financial services. A person might use an exchange occasionally. Software designed to manage a treasury, meaning a pool of funds, could need access to several services as part of its ordinary operation. The basket tries to identify the protocols that might supply those services.

That argument assumes agents will need blockchain-based finance and will choose the named platforms. Neither follows automatically from growth in AI. An agent could use financial infrastructure outside crypto, or a competing crypto service could win its business. The investment case therefore depends on both adoption of the broader idea and the choices users make within it.

The creator also links institutional research to investment decisions by large pools of capital. I would keep the scope narrow: a research basket tells you how its authors organize an opportunity. It does not establish that institutions have purchased the listed tokens, committed to the target weights or adopted a common strategy.

The altcoin model portfolio weights

The creator explains the allocations through a $100 example. In that framing, a target weight expresses how much of the hypothetical basket goes to a project. It is a model for distributing exposure, not a statement about a token's price or an expected return.

The first group contains the largest stated allocations, using the target weights displayed in the source table.

The remaining stated allocations cover exchanges, connections between networks, credit and tokenized assets.

The Ondo allocation comes from the spoken explanation; the other weights above appear in the on-screen basket. Pendle is also named as a selection. The sources do not establish Pendle's weight or resolve the displayed Backpack token label, so check the original basket's allocation and token identifiers before relying on it.

I would treat these percentages as a map of the thesis's priorities. The basket gives substantial attention to trading and financial services, even though its organizing story is AI. That matters because an AI narrative can sound like exposure to a new technology while the underlying businesses remain closely tied to financial activity.

A target weight also says little about suitability. It does not reveal how much loss an investor could tolerate, how the basket would behave during a market shock or whether positions would be easy to exit. Those are separate questions from whether the authors have identified a plausible use case.

Trading venues carry much of the thesis

Derive, formerly called Lyra Finance in the creator's explanation, supplies the options-trading part of the basket. Options generally give their holder a right to buy or sell an asset under specified terms. They can serve different purposes, including taking a view on price or limiting a particular financial exposure.

The creator describes Derive as a leading decentralized options protocol and contrasts it with Deribit, a centralized platform. I would treat the leadership claim as the creator's assessment. The practical distinction is the type of infrastructure the basket wants to own exposure to: financial activity carried out through decentralized protocols.

Lighter and Hyperliquid cover perpetual trading. Perpetual contracts generally let traders take long or short positions without a fixed expiry date. A long position benefits from a price rise, while a short position benefits from a fall, subject to the contract's costs and terms.

The creator notes that other perpetual exchanges exist and specifically mentions dYdX as an older alternative that the basket does not select. That is a useful counterpoint. Recognizing demand for a service does not settle which provider will capture it, and picking a category winner requires more than observing that the category is active.

For me, the derivatives selections introduce a tension in the broader story. The thesis describes agents as practical users of crypto, but some of the chosen services support activities already central to speculative trading. Agents could use derivatives for risk management, yet their involvement would not make those products inherently safe or productive.

In general, leveraged derivatives can magnify losses and trigger liquidation, meaning a platform closes a position when its collateral no longer meets requirements. A protocol token creates a different exposure from an individual trading position, but its investment case may still depend on trading activity, competition and the protocol's economic design. More contracts traded does not automatically mean more value reaches token holders.

Why the basket chooses Solana

Solana is the only layer-1 network in the basket, according to the creator. A layer-1 is a base blockchain on which transactions and applications operate. The creator describes Solana as an application network where developers can launch smart contracts, programs that carry out rules on the blockchain.

The stated reason for leaving out Bitcoin is that the report views it as digital gold rather than the software infrastructure it wants for agents. The creator says Ethereum is excluded because the report considers it slow. Those are selection arguments attributed to this thesis, not universal judgments about either network's usefulness.

The creator expects Solana to have an important role as the agent market develops and describes it as faster than Ethereum. They also acknowledge that Solana's current attention is heavily connected to memecoins. That acknowledgment separates the hoped-for future use from the activity the creator says is prominent now.

I think this is where readers need to distinguish a network selection from a complete view of crypto. A basket built around applications can exclude an asset with a different purpose without proving that asset has a worse investment outlook. Likewise, leaving out Ethereum's token does not mean the basket avoids Ethereum-related services: ether.fi is included through its restaking and financial-services role.

The speed argument assumes performance will strongly influence agents' choice of network. In general, software users also depend on reliable execution, available services and clear transaction rules. A network can suit a proposed workload without becoming the sole destination for it.

The creator briefly points to another network as a possible participant in the agent market. The broader lesson is that Solana's selection reflects a choice among competing possibilities. I would judge the thesis by whether useful agent activity develops on the selected network, rather than treating the AI label itself as proof of future demand.

Treasury services, payments and stablecoins

The creator connects ether.fi to a possible need for agent payments and movement between traditional money and crypto. Those entry and exit routes are often called on-ramps and off-ramps. They matter because funds held on a blockchain may eventually need to enter or leave that system.

In the explanation, ether.fi is a liquid restaking platform where users stake Ethereum and receive a token representing that position. The creator also mentions crypto cards and describes the service as an all-in-one crypto app. Their argument is that agents could need financial services that do not rely on the same human presence and documentation processes as conventional banking apps.

I would separate convenience from eligibility. Automated software can interact with a financial interface, but that does not make it exempt from the service's access rules or legal requirements. In general, a product's technical availability and a user's permission to use it are different issues.

Liquid restaking also needs a plain-language distinction. Staking commits assets to a blockchain-related security process; a liquid token can make a claim on that position transferable. Restaking generally adds another layer of use and risk. The representative token does not remove the underlying exposure, and its market value or redemption conditions can matter when someone needs access to funds.

Aave supplies the lending and borrowing part of the treasury argument. The creator describes it as the crypto market's largest borrowing and lending platform and suggests agents could use it to lend funds or obtain liquidity. That size description remains the creator's claim; the investment thesis is that programmable access to credit could be useful to software managing money.

Ethena fills the stablecoin-related role. Stablecoins generally aim to track a reference value, which can make them useful for payments and accounting, but that aim is not a guarantee. The creator frames Ethena as a decentralized alternative to centralized stablecoin infrastructure and argues that centralized issuers can block assets.

I would not read that framing as proof that any stablecoin is free of control or failure risks. In general, stablecoin users need to understand how value is maintained, what collateral or counterparties are involved and how redemption works. The creator also mentions DAI and other decentralized alternatives, so the selection represents a preference within the category rather than the only available route.

Swaps, connections and outside data

Aerodrome and Uniswap cover token exchange. The creator describes both as decentralized exchanges, or DEXs, where users can swap one token for another. For an agent treasury, that could be a practical function: the asset it holds may differ from the asset another service accepts.

The creator describes Uniswap as multichain and suggests Aerodrome's selection may relate to Base and Coinbase. They express uncertainty about that explanation. I would preserve it as their proposed rationale, rather than claim that the report confirms why it gives Aerodrome the larger allocation.

The existence of an exchange does not guarantee that a swap will happen at an attractive price. In general, liquidity, the availability of assets for trading, affects execution. Slippage is the difference between an expected price and the price received. Automated systems need constraints around execution because a technically successful transaction can still be economically poor.

LayerZero and Chainlink cover connections and information. The creator describes LayerZero primarily as cross-chain communication infrastructure, with bridging as a use. Cross-chain activity means sending messages or moving assets between separate blockchain networks, which otherwise maintain their own records and rules.

Chainlink's role also includes oracles, systems that supply outside information to blockchain applications. A financial application may need a price or another external fact before it can execute its rules. The creator describes Chainlink as a leading provider of that infrastructure, but I would not extend the claim to every piece of real-world data used in crypto.

These selections make the agent thesis more concrete to me. Software managing funds might need to exchange an asset, communicate with another network and read a reliable price. Those are distinct jobs, and a portfolio can allocate to services that support them without owning every base blockchain involved.

The corresponding general risks also differ. Cross-chain systems add dependencies between networks and applications. Oracle-dependent services can suffer when data is wrong, delayed or manipulated. Automation may act quickly on an input, which makes validation and transaction limits important even when the underlying software behaves as designed.

Credit, yield and tokenized assets

Maple supplies another form of lending exposure. The creator describes it as a credit marketplace through which agents could lend treasury funds to institutions, and identifies SYRUP as the rebranded token. That use case is about putting funds to work through credit relationships, rather than merely holding them for future payments.

Credit introduces borrower risk. In general, a lender depends on repayment and on whatever protections the arrangement provides if repayment fails. A blockchain record can make a claim easier to track, but it does not make a borrower able or willing to pay.

Pendle supplies the yield-trading component. Yield means income generated by an asset or financial position. The creator argues that agents holding yield-bearing assets could want to trade that income and calls Pendle the best protocol for the task. That ranking is the creator's opinion, not a conclusion I can establish from the basket alone.

What stands out to me is the different job each service would perform. Lending creates exposure to repayment; trading yield creates exposure to how income is valued over time. Treating both as ways to earn returns can hide the difference in what must go right for each position.

Backpack and Ondo Finance address tokenized assets from different angles in the creator's explanation. Tokenization generally means representing an asset or a claim through a blockchain token. The creator describes Backpack as a wallet with tokenization features aimed at retail and institutional users, while Ondo focuses on offering tokenized US Treasuries and bonds to institutions.

A tokenized claim still depends on its terms. In general, readers should distinguish the token record from the legal right it represents, including who can redeem it and under what conditions. Representing a conventional asset on a blockchain does not erase the institutions or agreements behind that asset.

I would also distinguish access to a service from ownership of its associated token. A platform can help users hold assets, obtain credit or trade income without every benefit flowing to token holders. That distinction is central to judging a protocol investment, even when the product itself answers a practical need.

Diversification still has limits

The basket spans several financial functions, but a long list of project names does not establish independent risks. Exchanges, lending services and yield markets can all depend on healthy crypto activity. Stress in one part of that system can affect the demand for others.

I think the largest shared assumption is that agent adoption will translate into economically meaningful use of these selected protocols. A second assumption concerns token economics: the benefits of that use must reach the token in a way that supports the investment case. A useful application and a rewarding token investment are related possibilities, not interchangeable outcomes.

General protocol risks include software bugs and failures in the systems a service relies on. Market risks include sharp token-price changes and difficulty exiting a position. Regulatory or access restrictions can also affect financial services, particularly where tokenized claims connect to conventional assets.

The creator closes by describing the crypto market as falling and suggests that readers interested in an institutional perspective could explore this angle. I would treat that market description as their observation at publication. Falling prices do not validate a research thesis, and a detailed report does not establish where prices will stop falling.

An institutional-style presentation can make a basket look settled. Percentages, named categories and a long report create structure, which is useful, but they cannot eliminate uncertainty. The proper benefit is a clearer account of what the authors expect to happen and which businesses they think would benefit.

Judge the next AI adoption claim

When you encounter a claim that an agent is using crypto finance, identify the actual action: whether it makes a payment, borrows funds, exchanges assets or manages an income stream. Then distinguish a demonstration from sustained use. That gives you a concrete way to test the story without relying on the appeal of the AI label.

I would keep a record of the behavior the thesis predicts and compare later evidence against it. Look for activity that performs a useful task under clear permissions and operating limits. The strongest evidence would explain what the software accomplished and why that financial service was needed; a token being mentioned alongside AI is a much weaker signal.