The DATA Foundation

AI-native infrastructure for real-world data.

[The DATA Foundation @datafdn]

Today, Story becomes The DATA Foundation.

$IP is now $DATA. The Story Network is now DATA Network, tracking 1B+ data records with a singular mission: to become the trust layer for all AI training data.

Welcome to The DATA Foundation.

[The DATA Foundation @datafdn]

Korea's biggest fintech giant @toss__official is bringing its 30M users into the AI data economy with @psdnai, as Toss's first-ever AI/web3 partnership.

[Poseidon @psdnai]

Big News.

Korea's biggest fintech giant @toss__official is bringing its 30M users into the AI data economy with @psdnai, in its first ever AI/web3 partnership.

We'll begin working to integrate our contributor app Numo as a mini-app inside Toss, so anyone can provide real-world data to train AI and get paid for it ↓

[DATA 中文社区 @datafdn_cn]

每家 AI 实验室都在问同一个问题：这批数据从哪里来？

Trace 来了。

DATA Network 的公开审计层，正式上线。搜索任意数据集、钻取任意记录、查看完整审计链路——让数据来源不再是黑盒。

不是盲目信任，而是有迹可循。

➡️ https://t.co/r5F2Cqg2Nh

[The DATA Foundation @datafdn]

@andyfolio @avipat_ @saintofthehour @birleh truly gorgeous

[The DATA Foundation @datafdn]

Starting today, Kled runs on DATA.

The world's largest opt-in human-data marketplace with over 1B records is now fully auditable.

Kled’s founder, Avi Patel (@avipat_), joins us with a single mission: make all AI training data auditable by default.

[Avi Patel @avipat_]

Kled the leading human data marketplace has officially partnered with The DATA Foundation to onboard over 1.5 billion user records on chain.

We've spent the last 3 months working directly with some of the leading foundation models and labs. The single biggest point of discussion was trust. Labs have 0 room for error and consumer data is the single largest but also most sensitive data type on the planet.

Labs need two things before confidently purchasing a dataset.

1. A clean audit record: end-to-end receipts, consent forms, and proof of payment. They need to fully trust that data uploaded on the marketplace can confidently be licensed without consumers introducing risk into training pipelines.

2. Full confidence that the data on our marketplace is original. Not pirated, not AI generated. They need to fully trust that data uploaded is real human unaltered content that can significantly enhance a training pipeline.

We've been hugely against 99% of all blockchain offerings that have come our way, but things aligned way too well here and we found a unique opportunity to build something great.

Kled will be moving its full audit rails onto DATA Network, backed by a16z crypto, Polychain, and other top VCs.

The instant a user uploads data to our platform we create an anonymized receipt that is automatically sent to the TRACE (demo video, live links, and more in the post below). The content hash ID, signed consent forms, full payment record, and end-to-end timestamps.

Any AI lab can now verify the legitimacy of any dataset in seconds, making us the first data marketplace in history to publicize its data audit records. Consumer identities are fully anonymized and hidden, no users will be exposed in the process.

To add to this Kled will also be supporting USDC payouts on DATA Network. This will be alongside other stablecoin options rolling out with our existing fiat payouts. All of this will be fully auditable on DATA Network as well.

Lastly Kled and The DATA Foundation will be pooling its efforts to create the world’s best fraud detection protocol. We will be allocating the majority of our time/resources towards creating this, labs need to trust our data and the creation of AGI will be a function of this trust.

I'm also joining The DATA Foundation with a Part Time Advisor Role as the Chief Data Officer, where I’ll be advising the foundation team to make sure the Trace audit product reflects what AI labs actually need to license data with confidence.

The DATA Foundation's audit / licensing rails and Kled's data marketplace are complimentary in nature, labs need both. Kled is the largest contributor to that audit layer so we have the most skin in the game to get this product right.

Every effort described here will create a safer future for consumers and labs. We are here to set the gold standard for trust and nothing will stray us away from this goal. Onward.

[Avi Patel @avipat_]

Kled the leading human data marketplace has officially partnered with The DATA Foundation to onboard over 1.5 billion user records on chain.

We've spent the last 3 months working directly with some of the leading foundation models and labs. The single biggest point of discussion was trust. Labs have 0 room for error and consumer data is the single largest but also most sensitive data type on the planet.

Labs need two things before confidently purchasing a dataset.

1. A clean audit record: end-to-end receipts, consent forms, and proof of payment. They need to fully trust that data uploaded on the marketplace can confidently be licensed without consumers introducing risk into training pipelines.

2. Full confidence that the data on our marketplace is original. Not pirated, not AI generated. They need to fully trust that data uploaded is real human unaltered content that can significantly enhance a training pipeline.

We've been hugely against 99% of all blockchain offerings that have come our way, but things aligned way too well here and we found a unique opportunity to build something great.

Kled will be moving its full audit rails onto DATA Network, backed by a16z crypto, Polychain, and other top VCs.

The instant a user uploads data to our platform we create an anonymized receipt that is automatically sent to the TRACE (demo video, live links, and more in the post below). The content hash ID, signed consent forms, full payment record, and end-to-end timestamps.

Any AI lab can now verify the legitimacy of any dataset in seconds, making us the first data marketplace in history to publicize its data audit records. Consumer identities are fully anonymized and hidden, no users will be exposed in the process.

To add to this Kled will also be supporting USDC payouts on DATA Network. This will be alongside other stablecoin options rolling out with our existing fiat payouts. All of this will be fully auditable on DATA Network as well.

Lastly Kled and The DATA Foundation will be pooling its efforts to create the world’s best fraud detection protocol. We will be allocating the majority of our time/resources towards creating this, labs need to trust our data and the creation of AGI will be a function of this trust.

I'm also joining The DATA Foundation with a Part Time Advisor Role as the Chief Data Officer, where I’ll be advising the foundation team to make sure the Trace audit product reflects what AI labs actually need to license data with confidence.

The DATA Foundation's audit / licensing rails and Kled's data marketplace are complimentary in nature, labs need both. Kled is the largest contributor to that audit layer so we have the most skin in the game to get this product right.

Every effort described here will create a safer future for consumers and labs. We are here to set the gold standard for trust and nothing will stray us away from this goal. Onward.

[The DATA Foundation @datafdn]

1 million live records and counting ↓

https://t.co/VAe4vKwVxO

[The DATA Foundation @datafdn]

Introducing Trace.

Trace is our flagship, public audit and search platform where every asset permanently registered on DATA Network can be accessed.

AI Labs can search every human contributed photo or voice sample and drill into any individual record to see the full audit trail.

Trace is where labs and regulators can filter by dataset, app, data type, modality, and time to verify data provenance so AI models can be trained with confidence.

[The DATA Foundation @datafdn]

Read more from our CEO, Andrea Muttoni @devrelius

https://t.co/Q2O8skeXNN

[Andrea | Devrelius @devrelius]

https://t.co/DlfQELgfDx

[The DATA Foundation @datafdn]

Today, Story becomes The DATA Foundation.

$IP is now $DATA. The Story Network is now DATA Network, tracking 1B+ data records with a singular mission: to become the trust layer for all AI training data.

Welcome to The DATA Foundation.

[The DATA Foundation @datafdn]

@devrelius We did it.

[The DATA Foundation @datafdn]

https://t.co/vpLatW7sNB

[Poseidon @psdnai]

https://t.co/CsxT48ULLA

[The DATA Foundation @datafdn]

Voice data is the hardest to make verifiable.

It holds identity, and traceability has to persist through downstream training.

@otodotearth + @psdnai address the missing layer: verifiable consent, with licensing and receipts onchain.

Data stays private, proof goes public.

[Poseidon @psdnai]

The bottleneck in AI moved from architecture to data.

Voice is the clearest proof. The new speech-to-speech models can finally hold a real conversation but they need a kind of data that barely exists in the wild.

Today we're partnering with @otodotearth and @StoryProtocol to fix that. ↓

https://t.co/Ci0GIaUTnn

[The DATA Foundation @datafdn]

Scraped internet → today's AI.
Rights-cleared data → the next frontier.

[The DATA Foundation @datafdn]

Indonesian spans thousands of islands, cultures, and ways of speaking.

Capturing that diversity is exactly what makes speech data valuable. Nuance is the next unlock.

[Poseidon @psdnai]

Reminder: Numo tasks are live in Indonesian.

Tap in below.

Semoga beruntung (good luck)!

↓

[The DATA Foundation @datafdn]

An early look at the new system:

•- Ever felt like your contributions deserved recognition, but not quite a role ascension? This is built around that gap.

•- Instead of the three-tiered OG roles, it moves toward a single, continuously growing status with no ceiling on how high it can go.

•- New contributors may receive early recognition for their high quality efforts, and some power users may end up receiving repeated recognition through their consistent efforts.

Let's hear your thoughts and suggestions!

[The DATA Foundation @datafdn]

The way we recognize contributors is evolving.

Today, recognition is mostly tied to OG promotions, but we're building a system that recognizes contributions more consistently.

Here's an early look. Let us know what you think ↴

[The DATA Foundation @datafdn]

Camera footage
Radar signals
Depth sensor readings
Environmental audio
Perception data

All real-world data.

[The DATA Foundation @datafdn]

How do you benchmark AI systems when the benchmark itself is uncertain?

@psdnai tested three frontier models on Bengali transcript review and compared them with native linguists.

Spoiler alert: low-resource languages are still a blind spot for flagship AI models ↓

[Poseidon @psdnai]

Bengali is spoken by ~280M people.

Yet when we tested frontier models on Bengali transcript review, they rarely agreed on what was incorrect.

The results point to a broader challenge for AI in low-resource languages ↓

[The DATA Foundation @datafdn]

AI runs on people.

That makes human data a trillion-dollar annual market.

And the appetite for IP doesn’t cap out.