Analysis, takes, and signals from the ownership coin frontier.
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The Big Picture
5 raises were completed on Backable in October. The Syndicate raised $600K, The Inner Table raised $159K, and MimiKrew, Orendal and DRAWUP each raised $20K. The next curated raise on MetaDAO, over computer, a trader-owned prop firm, was set to open in late October but has been delayed.
Decision markets cooled in October. 14 proposals resolved and 10 passed on $105K of volume, which was down 71% from September. One market, Solomon’s liquidity migration, accounted for about 70% of that volume. MetaDAO’s own proposal to move $1.9M into an ecosystem fund passed on Oct 29.
This issue’s Call argues that AI has made token research cheap, but has not fixed the data underneath it or replaced the judgment needed to make it compelling. Our Signal looks at tokenized equities competing for the same onchain investors as ownership coins, and the Valuation Analysis looks at Laso Finance.
The Call
AI has made token research cheap. The edge now comes from judgment. Asking the right questions, comparing like-for-like assets and verifying what a token holder actually owns.
AI Has Compressed Token Research. But Has Not Fixed the Data or Replaced Judgement
Everyone has an analyst now. An AI agent can read documentation, find operating metrics, compare competitors and produce a token research report in minutes. But what it cannot do is repair the data the report is built on.
Onchain data can be a mess. It is fragmented on explorers, governance forums, legal filings, dashboards, social channels, etc. Wallets can also be mislabelled, documentation and programs/smart contracts change, and mints and burns can easily be missed. Most importantly, the relationship between a token and the business behind it is often unclear.
As token research becomes easier to produce, the work moves elsewhere. Analysts have to connect the entities and wallets, verify the data and establish what a token holder actually owns.
1. Research has become cheap
Only a year ago, a thorough token research report could take weeks. An AI agent now does a decent version of that work in minutes. Much of what the industry called research was this collection and organization of public information. That work is now becoming commoditized. Since everyone has access to the same documents and models there is less of an edge.
Today, the value of research depends on whether the information is complete, current and connected to the asset being valued.
2. The token is not the business
A business can have a strong product, users and growing revenue while its token has no enforceable claim on any of it. This has always been one of the central problems in token analysis. AI makes it easier to repeat the mistake at scale.
We described the problem in Crypto’s Ownership Crisis as the token-equity split. A token may trade publicly and finance a product while a separate operating company owns the IP, employs the team and captures all the commercial or acquisition value.
In that structure, an analyst can be right about the product and still be wrong about the token. The good thing is that the market has started to recognize the difference.
Because the relationship between token and equity is not always clear, research has to trace the relationship between all of the following:
- The token
- The treasury
- Revenue
- IP and brand
- The operating company
- Governance
- Founders and equity investors
An AI model may be able to identify some of these things from the public domain, but it should not assume that value flows between them.
Revenue earned by a company does not reach a token because the company website mentions both. A treasury controlled by a 3/5 multisig is not owned by token holders because the documentation calls it “community owned”.
Analysts or their sidekick need to answer what part of that business the token represents and what evidence supports the relationship.
3. Start with rights
Before analyzing growth, market size or the team, analysts should answer a more basic set of questions:
- What does the token entitle its holder to?
- Who controls the treasury?
- Who owns the IP and brand?
- Where does revenue accrue?
- What happens in an acquisition or liquidation?
- Can holders enforce governance outcomes?
- Which decisions remain with founders, directors or multisig signers?
- Can the token’s rights be changed without holder approval?
If these questions have no clear answers, conventional valuation may be meaningless. A discounted cash flow model or comparable analysis is only relevant if the cash flows belong to the asset being valued.
Some teams state the separation explicitly, others obfuscate it or change the rules at their discretion.
This is where ownership coins change the analysis. When a token is connected through enforceable governance to the treasury, IP and operating entity, there is something concrete to investigate. An analyst can inspect the documents, contracts and execution path rather than relying on an implied relationship.
That does not make every ownership coin valuable, it just makes the ownership claim verifiable. It also determines which assets can be compared at all. Two tokens can trade at the same multiple of revenue while only one has a claim on that revenue. Putting them side by side on price-to-sales compares a claim on a business with exposure to its narrative.
4. Primary data matters more
AI research raises the cost of bad data because an incorrect input can now be turned into a convincing report in minutes.
This problem has actually been measured. A study called Aggregate Confusion in Crypto Market Data published in August 2026 found widespread disagreement among major crypto data providers on prices, market caps and volumes. The report findings show that data inconsistencies can materially distort research and investment analysis.
This reinforces the importance of data provenance and primary data because an AI agent reading incorrect datasets inherits their errors. So, for a token with an onchain treasury and governance system, a fundamental model should track the following as close to the source as possible:
- Treasury balances and movements
- Runway at the current burn rate
- Token issuance, burns and supply changes
- Unlocks and performance-based vesting
- Team and investor wallet activity
- Protocol fees and their destination
- Governance participation
- Proposal outcomes
- Custody and control type
Each metric also needs a definition to avoid misinterpretation. It's also important to have a model that changes when the asset changes as these metrics usually don’t remain static.
5. Governance is fundamental data
In conventional equity research, governance is often treated as a secondary consideration. For onchain assets with binding governance (non-advisory) it can directly alter the balance sheet, supply and rights attached to the asset.
A spending allowance change, token issuance, incentive campaign, acquisition or liquidation proposal can change the investment case overnight. Each passed proposal is a capital allocation decision with an onchain record.
Decision markets, which are native to the MetaDAO ecosystem, add another source of information. When a proposal has conditional pass and fail markets, the difference between them shows how traders expect the decision to affect the token’s value.
That signal still needs context because a market with three traders and a few hundred dollars of volume should not be treated as a reliable forecast simply because it produced a price.
Governance data should sit in the model alongside revenue, expenses and treasury assets as a first class data point.
6. AI introduces new research risks
AI produced research may look authoritative but it can be built on weak foundations. Here are some of the risks for researchers using AI.
This can happen even when the underlying provider is established. In October 2026, an analyst published a fundamental scorecard comparing privacy tokens using Blockworks data.
Umbra’s founder responded that much of the information about Umbra was incorrect. The analysis had missed months of revenue, migration of liquidity and burned tokens.
The scorecard had not invented its conclusions from nothing. It had inherited gaps in the data and turned them into an output.
That is what makes it a problem. The better the presentation, the less likely a reader is to question the inputs; unless someone steps in to correct it. It's worth noting that the analyst corrected the inaccuracies in a follow-on post.
Every material claim about ownership, control or value flow should lead back to a contract, legal document or filing, governance execution or onchain record. When that evidence is unavailable, the answer should be “unknown”, “not confirmed by team”, etc.
7. Where the research edge moves
Since AI driven reporting is cheap, the edge moves to work that cannot be completed by reading public documentation.
- Asking the right questions - Models answer what they are asked. Most prompts ask whether the project is good, not what the token represents.
- Working from primary sources - Contracts, legal documents, governance executions and onchain records should take priority over articles describing them.
- Reconciling the data - Conflicting supply figures, wallet labels and treasury balances need to be resolved before they enter a valuation.
- Tracing rights and value flows - Analysts need to establish where revenue lands, who controls it and how a token holder can benefit.
- Understanding incentives - Every governance decision has beneficiaries, counterparties and potential conflicts.
- The known, unknowns - Side letters, private agreements and offchain intentions may never appear in public data.
- Updating the model - A live model that incorporates new proposals, spending and issuance is more useful than a better written report that is already stale.
AI can assist with all of this. It can inspect documents, compare records and monitor changes. But it needs a research process that forces it to distinguish between what is verified, what is reported, what is inferred and what remains unknown.
Final thoughts
AI will produce more token research than the industry has ever seen. Most of it will summarize the same public information.
Fundamental research begins where summarization ends. It traces who owns the assets, who controls the capital, where value flows and what a token holder can enforce. It checks that the data behind each answer is complete and current before attempting a valuation.
This is also where 01Resolved fits.
AI makes analysis easy, but it still needs reliable data underneath it. Mapped accounts, categorized treasury movements, rights records and decision market history are what turn a confident report into a verifiable one.
The models will continue to improve. The quality of the underlying data, and the process used to verify it, is where the research advantage remains.
Ecosystem Ledger
Decision Markets
14 proposals were resolved, 10 passed and 4 failed, on $105K of volume. Activity was concentrated. Solomon’s liquidity migration made up 70% of the month’s volume from just 3 wallets, and 9 of the 14 markets traded under $2K. All 4 failures came from Areal Finance.
The month's largest market at $73.5K across 465 trades, but from only 3 wallets, one of which placed 21+ trades. 67% of volume was Pass-aligned and the proposal cleared the -3% threshold by ~9.6%.
Solomon withdraws 20% of the DAO's SOLO/USDC FutarchyAMM position, converts the USDC to USDv and redeploys it as SOLO/USDv liquidity on Raydium, so the dollar side of its own market earns. The remaining 80% stays in FutarchyAMM. Any further migration needs a new proposal that reviews how the two venues behaved during decision markets.
Volume was $9.4K across 72 trades from 8 wallets. 67% of volume and 75% of traders were Pass-aligned, and the proposal cleared the -3% threshold by ~14.4%.
$250K USDC moves from the treasury to a team-controlled wallet to buy more Hot Wheels inventory. Rip Cars says users now hold 907 of its ~2,000 items and its lower-tier gacha sold out on day one, so inventory, not sourcing, is the constraint on growth.
Only $342 traded, across 4 trades from 3 wallets, all Pass-aligned. The Pass and Fail TWAPs finished 0.16% apart, so the proposal passed on the -3% team-sponsored threshold by ~3.2%.
Umbra's monthly spending limit rises from $100K to $150K to run V2 on Solana, expand to Robinhood Chain and operate Nimbus, its private execution infrastructure. The team says it does not expect to spend the full amount. A 50% increase in the spend limit passed with almost no market scrutiny.
Volume was $1.5K across 5 trades from 3 wallets, almost all from a single $1.48K Fail-market sell. All volume was Pass-aligned. The TWAPs finished 0.2% apart and the proposal cleared the -3% threshold by ~3.2%.
Up to 2% of liquid supply (546,890 UMBRA, ~$232K) is set aside for Optimist: 0.5% earned over 12 months and up to 1.5% for milestones on Nimbus integrations, research and user onboarding. Tokens sit in a multisig and are distributed only after 12 months and once UMBRA's 30-day TWAP reaches $0.60. Anything unearned returns to the treasury.
Volume was $5.6K across 97 trades from 7 wallets, split almost evenly with 47% Pass-aligned. The Pass TWAP finished ~6.8% below Fail against a +3% threshold, missing by ~9.8%.
The market rejected minting 74.2M ARL, nearly 3x the existing supply, and sending 20M to the team without vesting. It was the first of four Areal proposals to fail in October, ending with a vote to liquidate the company and return its IP to the operators, which also failed on $150 of volume. With ~$3K in treasury, Areal is a live test of what happens when holders will neither fund the team nor wind it down.
Also resolved in October
Umbra passed a Liquidity Consolidation Proposal that returned its Meteora LP to the treasury. PREDICT and MimiKrew passed proposals handing their Meteora LP to Spark, LFOWN allocated $500 for marketing, and Futardio Cult renewed its Blockformer growth partnership for Q4. MetaDAO passed its Ecosystem Fund proposal on Oct 29, with Pass ~4.7% above Fail on $8.9K of volume from 2 traders, moving $1.91M of escrowed capital into a MetaDAO LLC fund for early-stage raises.
Valuation Analysis
In this section we highlight one organization in the MetaDAO ecosystem and share a valuation analysis. This is not financial advice or investment recommendation.
Laso Finance
What backs each token - As of Oct 31, LASO traded at $0.105. The DAO treasury holds $830K, and NAV (AS) is $0.082. The other $0.022 is what the market is paying for the business itself, which puts LASO about 27% above its treasury backing.
What the business earns - Laso sells prepaid cards and gift cards that users pay for in crypto, with no KYC required. All revenue generated from this activity goes to the DAO treasury - so far on a daily basis. Since its MetaDAO raise in July, Laso has sent $110K to the treasury: $27.6K in July, $31.6K in August, $28.0K in September and $22.5K in October.
In September, holders raised the monthly spend limit from $50K to $68K so Laso could hire a senior engineer to build out its agent product. At that level, revenue needs to grow about 2.35x from its Q3 average (3x from October) to break even. The team says around 1,200 new paying users would get it there, and it aims to deliver the agent roadmap by the end of Q1 2027. Until then, the treasury is funding growth, at about $46K a month after October’s revenue.
How the price compares - Using Adjusted Supply (10.06M LASO), the market value of Laso Finance is $1.05M (10.06M Laso * $0.105) . Take away the $830K treasury, and the market is paying about $222K for the operating business. That is 0.64x Laso’s annualized Q3 revenue of $349K, or 0.82x October’s run rate of $269K.
ether.fi, a tokenized card business with public revenue, trades at about 14x its past year’s revenue ($627M market cap / $43.9M of revenue), but most of that revenue still comes from staking. Listed peers in crypto spending trade at a median 1.2x enterprise value to revenue. In other words, investors pay about $1.20 for every $1 of the past year’s revenue:
● Fold: 0.48x
● Exodus: 1.24x
● Paysafe: 1.48x
At 2.0x Q3 revenue, LASO would be worth about $0.15, or about $0.14 on October’s run rate. An experienced founder and revenue that can be checked onchain every day could justify a higher multiple.
Another way to look at the Laso valuation is through a discounted cash flow analysis. To illustrate, assume revenue grows 80% in the first year, then steps down to 70%, 60%, 40%, 30%, 20% and 10%, and 3% a year after that, with costs fixed at the $68.3K monthly limit. Using a 45% discount rate, LASO could be worth about $0.22 today. That counts the 3.42M team tokens that unlock above $0.20, which cannot happen before June 2028.
LASO holders also have a floor most early-stage investments don’t have. Holders can propose to wind Laso down and return the treasury, which today is worth about $0.08 per LASO.
Figures: 01Resolved, prices as of Oct 31. Revenue observed onchain; operating figures from Laso’s September spend limit proposal. Listed companies: stockanalysis.com, Oct 30, 2026 close. This is not financial advice or an investment recommendation.
Spotlight: Over Computer
What it is - A prop trading firm gives traders its money to trade with. Traders keep most of any profit, and if they lose, they lose only what they paid to take part. Over is a prop firm for stocks and crypto. Traders, or their AI agents, can trade stocks such as NVDA and AAPL and keep 80% of any profit, paid in USDC. Over opened a private beta on Oct 6 with trading accounts ranging from $2,000 to $50,000.
What's different - Most prop firms make traders pay for a test first. Few pass, so test fees from traders who fail are the firms' biggest source of revenue. Over has no paid test, traders only pay a fee while they trade, and that fee is the most they can lose. Every trade is also recorded onchain at public market prices, so traders can check they were treated fairly.
The closest comparables are FTMO, the biggest traditional prop firm with $204M of revenue in 2023, and Hypernova, Propr and Iceberg, which run onchain prop firms on Hyperliquid. All 4 still test traders first.
How it makes money - Over doesn't place trades in a real market. Like a bookmaker, it keeps the fees from everyone and pays the winners itself. That makes its cash reserve matter - which is why 30% of the raise goes into it. Later, Over plans to let partners run their own trading competitions on its platform, with Over taking 20% of the fees.
Why it's worth watching - Retail prop trading is an estimated $850M a year industry (Track360). Over is testing whether a prop firm with no test, trade records anyone can check and a reserve token holders can see can win traders over.
Signal vs. Noise
For global investors who face expensive or restrictive brokerages, or have no access at all, owning stock exposure onchain is a step forward. In our view, many crypto assets are fundamentally uninvestible so tokenized equities give onchain investors something with a real claim behind it, and that raises the bar for every crypto native asset.
Stocks come with decades of legal history behind their rights plus legally required investor reporting which tokenized versions should inherit, depending on the wrapper (or lack thereof). Ownership coins are a much newer asset class and don’t match that yet. But they offer many things tokenized equities do not.
Firstly, holders have a claim on a treasury they can see in real time, rather than offchain records reported quarterly. Secondly, they have oversight through decision markets. Shareholders elect a board and approve major corporate actions at set meetings while ownership coin holders price each proposal as it comes up, and the outcome executes onchain.
We believe as more investors hold stocks onchain, ownership coins will increasingly be compared to equities rather than to other tokens.
No notable Noise in October.
Around the Space
↗ 01Resolved Platform Upgrade
A look at 01Resolved's recent platform updates and how to put them to use when analyzing ownership coins.
↗ Galaxy Research: Conditional Markets Are Beginning to Blossom
A deep-dive on the landscape for conditional markets.
↗ Umbra V2: Nimbus, The Raptor 3 of Privacy
A comprehensive breakdown of Umbra V2 and Nimbus.
