Recently, while browsing my hard drive, I discovered that the 3 pieces of lung CT data I marked last year are still “making money” — an AI company used them to fine-tune their model, resulting in 2 new versions. I receive a share of the profits every month, and I have already made $6000. In the past, I wouldn't even have dared to imagine this: traditional platforms either buy outright (3 pieces sold for $300) or the data is used for secondary development, and I wouldn't see a dime.
It wasn't until later that I understood how ruthless OpenLedger's 'ancestor profit sharing' is: whether it's a piece of data you uploaded, a set of model weights you adjusted, or a testing script you wrote, as long as it is subsequently called or derived, you can earn money based on 'lineage'. Today, in my dual role as a technician and DeFi architect, I will unpack how this works and why it allows 'small contributors' to enjoy long-tail benefits. @openledger#OpenLedger #$OPEN #AI long tail profit-sharing
I. Technician perspective: 3 layers of technology implement 'ancestor profit-sharing'; every profit-sharing can trace lineage and be clearly calculated.
Don't think 'profit-sharing' is just a fantasy; OpenLedger has made 'who contributes, who benefits' a traceable project on the BSC chain. I delved into its 2.0 technical white paper (available for download at the official 'developer center') and March mainnet data, extracting 3 key implementation designs.
1. Asset lineage layer: Issue 'genealogy NFTs' for data/models, leaving traces for every generation of derivatives.
1 asset 1 'genealogy ID', traceable from root to leaf: Whether data or models, each upload generates an NFT with 'lineage fields'—for example, my 3 CT data includes 'source data ID: CT-2025-0312, annotator: me, first user: company A'; later, when company A uses it to fine-tune a model, the NFT of the new model automatically adds 'parent ID: CT-2025-0312', effectively giving the asset a 'genealogy.'
On-chain receipts record all call records without any tricks: Every time an asset is called or modified, a 'call receipt' is generated on the BSC chain, detailing 'which ancestor assets were used, what new assets were derived, and how much revenue was generated.' I entered my data ID on the OpenLedger blockchain explorer (accessible in the official website's 'tools' section) and could see that company A called my data 37 times while fine-tuning the model, and the derived model was subsequently used by 2 companies, with each record carrying a timestamp and hash.
Pitfalls of traditional platforms: I once uploaded 100 pieces of data to a certain platform, and later found out it was turned into a 'medical dataset' and sold. When I asked the platform for an explanation, they said, 'No records,' leaving me at a loss. Now with 'genealogy NFT,' everything is recorded on the chain—who used it and how it was used—so there's no way to shirk responsibility.
2. Profit-sharing calculation layer: Dynamic weights avoid 'egalitarianism'; those who contribute more earn more.
Weights are calculated across three dimensions; the better the data, the more points: Profit-sharing is not a fixed proportion, but is dynamically adjusted based on 'asset performance'—for example, my CT data has 'annotation precision of 98% (higher than platform average of 92%), called 37 times (top 20% on the platform), and derivative model accuracy improved by 12%,' so the profit-sharing weight increased from the initial 1.0 to 1.8. In contrast, the 1 piece of data my colleague uploaded had an annotation precision of 85%, was called only 2 times, and the weight dropped to 0.3.
Settlement cycles are transparent, with data precise to two decimal places: On the 1st of each month, the previous month's profit-sharing is automatically settled using a smart contract; the formula is clearly stated on page 23 of the white paper: single asset profit-sharing = (asset weight / total weight of all ancestor assets) × total revenue of derivative assets × 80% (20% goes to the ecosystem pool). My profit-sharing for March was calculated as follows: total revenue of derivative models was 15,000U, my data weight was 1.8, total weight was 13.5, so (1.8/13.5) × 15,000 × 0.8 = 1600U; plus the base calling fee of 400U, the total was 2000U, matching exactly with the wallet receipt.
March mainnet data verification: The platform had 1200 assets participating in profit-sharing in March, with the top 10% high-weight assets (like precision medical data and efficient fine-tuning weights) averaging 820U/month in profit-sharing, which is 9.6 times that of low-weight assets (averaging 85U/month), truly rewarding hard work.
3. Risk control security layer: Openness does not mean 'running bare'; anonymization + strict permission control.
Sensitive assets are forced into 'sandbox calls'; data cannot be taken away: When I uploaded CT data, the platform automatically triggered 'medical data mode,' and buyers could only use it in OpenLedger's anonymized sandbox (cannot download the original data). Even secondary development must occur in the sandbox to ensure data does not leak; in March, the platform's medical data anonymization success rate was 100%, with no leaks occurring.
Profit-sharing wallet directly linked, no need to wait for platform transfer: Profit-sharing goes directly to my bound BSC wallet, without going through the platform. March profit-sharing was 2000U, arriving at 00:05 on April 1—at least 7 days faster than traditional platforms' 'application-review-payment' process.
Cost savings of 60%, even small contributors can participate: Using IPFS for storage, 100GB costs 5U per month, more than half the price of Alibaba Cloud (12.5U); I stored 200GB of data and only spent 10U monthly, so even if profit-sharing is low, I won't lose out on storage.
II. Perspective of DeFi architects: 3 types of people can enjoy 'long tail earnings'; I've done the math, and ordinary people can replicate it.
Don't think 'ancestor profit-sharing' is just for big players; whether you're uploading data, adjusting models, or writing scripts, you can earn long tail money from it—here are 3 real cases, with costs and earnings clearly laid out.
1. Data annotators: 3 pieces of old CT data, earning 2000U monthly (my case).
Asset preparation: In March last year, I spent 2 hours annotating 3 pieces of lung CT data (annotation cost 60U) and selected 'allow derivative profit-sharing' upon uploading.
Subsequent earnings: In March, called upon 37 times by company A, receiving a base calling fee of 400U; derived 2 models, earning 1600U in profit-sharing, totaling 2000U; up to now, there have been continuous profit-sharing for 6 months, accumulating 11,000U, having already earned back the cost.
Tip: Spend an extra 5 minutes calibrating precision when annotating data (e.g., by marking the lesion location more precisely); this can significantly increase weight and profit-sharing; prioritize fields with high demand like 'AI diagnosis and autonomous driving', which have 3 times the calling probability compared to ordinary data.
2. Small model developers: A set of fine-tuning weights, earning 12,000U in profit-sharing over 3 months (friend's case).
My friend is an AI engineer; last November, he spent 3 days tuning a set of weights for a 'lung disease diagnosis model' and uploaded it to OpenLedger, selecting 'allow derivative profit-sharing.'
Performance data: Using this set of weights for fine-tuning, the model inference speed increased by 40% (from 2 seconds/image to 1.2 seconds/image), and accuracy reached 93% (8% higher than the original). In March, it was called upon by 23 companies, raising the weight to 2.5.
Profit-sharing earnings: 3200U in January, 3800U in February, 5000U in March, totaling 12,000U over three months—more than what he earned from outsourcing coding.
In comparison to traditional models: In the past, he adjusted the weights for a company and received 8000U at once; later, the company made hundreds of thousands using those weights, and he didn't get a cent more; now, with profit-sharing, as long as the weights are useful, he can keep earning.
3. Script developers: A set of evaluation scripts, earning 300U monthly (junior case).
My junior is a computer science student; last December, he wrote a 'model robustness evaluation script' (which can quickly test the model's resilience) and selected 'allow derivative profit-sharing' upon uploading.
Usage data: In March, 150 models used his script for evaluation, with 80 models later commercialized, and his script weight was 0.8.
Profit-sharing earnings: March profit-sharing was 300U, equivalent to his previous 10 days of annotation income. The key is that once the script is uploaded, he doesn't need to manage it; profit is distributed automatically every month.
My junior said: 'I used to think writing small scripts was meaningless, but now I realize that as long as it helps others save time, I can keep earning from it, which is much easier than doing part-time jobs.'
III. Ordinary people can get started in 2 steps: Now upload assets, and you can participate in profit-sharing next month, with an avoidance guide attached.
Don't think it's complicated; I taught my cousin (a nurse, not tech-savvy) how to upload her nursing tutorial data in just 15 minutes. Following these 2 steps, she can upload assets now and participate in profit-sharing in May.
1. Upload assets: Choose the right type, fill in the correct information, and profit-sharing will follow.
Step one: Select 'profit-sharing asset type': On the official website's 'asset upload' page, select 'data/model weights/tool scripts,' and do not choose 'one-time sale' (otherwise there will be no profit-sharing); when uploading medical data, remember to check 'anonymized sandbox call' for safety and compliance.
Step two: Fill in the 'key information to enhance weights': Annotation data must clearly state 'precision (e.g., 98%), applicable scenarios (e.g., AI training for lung diseases)'; model tuning must state 'how much the inference speed improves, what the accuracy is'; this information affects the initial weight, so don't be lazy.
Avoid pitfalls: Don't upload low-quality assets (like poorly annotated data or scripts that don't work), as this will not only lower weights but may also lead to platform delisting, wasting your efforts.
2. Check profit-sharing: Two places to look, clear earnings.
Real-time call monitoring: On the 'my assets' page, you can see who has called the asset, the number of calls, and the derivation status. I check it every day to know my data is 'working', which puts my mind at ease.
Monthly settlement check: On the 1st of each month, go to the 'profit-sharing record' page to see the detailed calculation process (weights, total earnings, profit-sharing amount), and you can also check the smart contract transfer records on the chain, so there's no worry about miscalculations.
IV. Interactive chat: Do you have 'old assets that can generate money'?
I used to think 'old data and small scripts are useless,' but now I earn 2000U monthly passively with just 3 CT data; what about you?
Friends involved in data annotation/collection: Do you have any old data annotated on your phone (like medical images or e-commerce review annotations)? Please comment with 'data type + annotation precision + upload time' (e.g., 'CT data + 95% + 2025.1'), and I will help estimate how much you can earn monthly.
Small model/script developers: Have you ever adjusted weights or written tool scripts? For example, 'model weights that can increase speed by 30%' or 'automatic annotation scripts'; please share their effects in the comments, and we can discuss how to make them earn long tail money.
Friends who have fallen into pitfalls before: Have you ever uploaded assets to other platforms, only to see them 'bought outright' without earning a share while watching others profit from your assets? Share your story in the comments to help others avoid pitfalls.
Friends who leave comments in the section, I will randomly select 5 to send (OpenLedger Ancestor Profit-Sharing Practical Handbook) — it contains 3 key items: ① Techniques for building high-weight assets (e.g., how to annotate data to improve precision) ② Profit-sharing calculation self-check table (just fill in the numbers to estimate potential earnings) ③ Asset upload pitfall guide to help you avoid detours!
In the past, contributing to AI always felt like 'dressing someone else's wedding'; data was sold once and gone, and model tuning ended with no returns. But OpenLedger's 'ancestor profit-sharing' changed this—if the assets you contribute are useful, you can keep earning from them, even if it's just 3 pieces of old data or a small set of weights; they can become 'assets that generate money every month.'
Now the platform has just upgraded its profit-sharing system in March, offering 'weight bonuses' for new assets (first 3 months weight × 1.2); take advantage of this opportunity to upload, and you can earn an extra 20% next month. Don't let your old assets sit idle on your hard drive; it would be better to take them and earn long tail money.
Useful tools/document retrieval guidance.
1. OpenLedger official website: Find via official channels (recognize BSC certification mark), the homepage 'asset upload' section allows uploading of data/models/scripts.
2. Technical white paper: Download from the official website's 'developer center' → 'technical documents,' focusing on P18-P25 (asset lineage and profit-sharing calculation).
3. Blockchain explorer: Go to 'tools' on the official website → 'blockchain explorer,' input the asset ID to check call and profit-sharing records.
4. High-demand asset list: Check the official website's 'task hall' → 'high-demand assets' for real-time updates on which asset types (like precision medical data and efficient fine-tuning weights) are called more and have higher profit-sharing; uploading according to the list is guaranteed to be correct.
5. Customer support: Use the 'online customer service' in the lower right corner of the official website or add the official Discord (link available on the official website) to resolve inquiries regarding profit-sharing or asset uploads; response times are very fast (the last time I asked about profit-sharing calculations, I got a reply in 5 minutes).
Data description
Data in this article sourced from: OpenLedger March 2026 official mainnet operation report (available in the 'announcements' section of the official website), V2.0 technical white paper (P18-P25), personal and friends' experimental records (can provide on-chain transaction hashes for verification); $OPEN price calculated based on the average price of 2.1U on March 31, 2026; medical data processing complies with (Data Security Law) (Personal Information Protection Law); this article does not constitute investment advice, and $OPEN investment carries risks; participation should consider individual risk tolerance.
