In 2024, cumulative GDPR fines in the EU surpassed €4.5 billion.

That same year, the U.S. Copyright Office began re-examining the fair use boundaries of AI training data. The New York Times sued OpenAI, demanding the destruction of model weights trained on its content. Japan revised its Act on the Protection of Personal Information to bring browsing behavior data under regulatory scope.

If you're a product manager at any internet company, you can feel the shift. Three years ago, saying "we protect user privacy" was a PR statement. Today, saying it means you need to open up your technical architecture and show your work. Regulators, investors, and users are all applying increasingly rigorous standards to determine whether "privacy" actually means anything.

Inside the DataDID team, when we talk about ZK Proofs, we keep coming back to one analogy.

It's not a door lock. It's a load-bearing wall.

A door lock can be picked. It can be bypassed by someone with admin access. It can be accidentally disarmed during a maintenance incident. A load-bearing wall can't. Tear it down and the building collapses. That's not a permissions issue — it's a physical constraint.

This post is about how that wall gets built, and why it may be the only truly reliable line of defense we have in data privacy.

The Three Paths the Industry Has Tried — and Where Each One Breaks

The internet industry currently has roughly three approaches to protecting user data. Each has its own fatal flaw.

Path one: encryption. TLS in transit, AES at rest, clean key rotation practices. The problem is that encryption protects data while it's being transmitted or stored — but the moment the server needs to use the data, for analysis, matching, or recommendations, it has to decrypt first. The instant it decrypts, the data is vulnerable again. Encryption is the lock on the cabinet, but you always have to open the cabinet to get at what's inside.

Path two: de-identification. Strip direct identifiers — name, phone number, national ID — and retain an anonymized user profile. The problem here is subtler but more serious. De-identification is not the same as anonymization. A substantial body of academic research has shown that with enough auxiliary information, so-called anonymous data can be re-identified with considerable precision. In 2006, the "anonymous" search logs AOL released publicly were traced back to specific individuals by New York Times reporters within days. In 2007, researchers at the University of Texas cross-referenced the anonymous rating data from the Netflix Prize dataset with public IMDb ratings and reconstructed user identities. De-identification is a thin veil, not a wall.

Path three: compliance. User agreements, privacy pop-ups, a stack of documentation ready for a GDPR audit. This is the lowest-effort path and, by far, the most common. The problem is simple: compliance answers the question of who's liable when something goes wrong — not whether something can go wrong. It's a legal defense, not a technical one.

Step back from all three, and a shared blind spot emerges. Every one of them tries to protect privacy after the data has already been collected and uploaded to a server. They protect data once it reaches the server. But the moment data leaves a user's local device, its fate is in someone else's hands.

This is where ZK Proofs do something fundamentally different.

The Bar Analogy — Because It's Still the Clearest Explanation

Before getting into the technical specifics, the bar example. It gets used a lot, and for good reason — it's genuinely the most intuitive way to understand what's happening.

You walk into a bar. The bouncer needs to confirm you're 21 or older. The traditional approach: you hand over your ID, which contains your name, date of birth, photo, and home address. To prove one single thing — "I am at least 21" — you've handed over a bundle of information with zero connection to your age. That information is now in the bouncer's hands. You might trust him, but can you trust every app on his phone that might scan it? Can you trust that the bar's database won't be breached three years from now?

The ZK Proof approach flips this entirely. It gives you a mathematical tool that generates a proof: "This person's age is greater than or equal to 21." Nothing else. The bouncer verifies the proof, gets the answer he needed, and learns nothing about your actual age, your name, or your address. You exposed exactly the necessary information — not one word more.

That's the core insight. Traditional privacy protection asks: "How can we safely do things with this pile of data?" ZK Proof asks: "Can we get the job done without ever needing this pile of data in the first place?" The former is damage control after data already exists. The latter eliminates the need for the data to leave your device at all.

When we designed DataDID's Data Mining module, we faced the same structural question. What does the AI training data market actually need? Not "which five tech articles did this user read today" — it needs the signal that "this is a real user with diverse browsing behavior." That signal can be carried by a mathematical proof. The raw data never needs to leave your device.

How This Works in Practice Inside DataDID

When a user enables the Data Mining module, the system completes three steps entirely on the user's local device.

Step one: identify signals from the public behavioral layer of the browser — which categories of sites were visited, how long was spent on each page, which interest domains the content covered. Step two: feed those behavioral signals into a local ZK circuit and generate a mathematical proof. Step three: upload the proof to the chain; the raw behavioral data is automatically discarded locally.

Throughout the entire process, what the server receives is a single cryptographic attestation. It can verify that the attestation genuinely came from a legitimately authorized client, and that the behavioral diversity metrics it describes are statistically plausible — but it cannot reconstruct any specific browsing record from that proof. The proof is zero-knowledge: the verifier learns nothing beyond "this proof is valid."

This distinction is worth stating precisely, because it gets confused often.

Encryption and ZK Proofs both involve cryptography, but they solve opposite problems. Encryption solves "only authorized parties can see this." ZK solves "nobody needs to see this at all." Encryption protects the confidentiality of data. ZK eliminates the need for the data to be seen in the first place.

The difference between de-identification and ZK Proofs is even more fundamental. De-identification processes the original data — but the original data still traveled to the server. The ZK approach means the raw data was never transmitted. This isn't "we processed your data until no one can recognize it." This is "your data never left your device. What was sent is a mathematical summary about your data."

In DataDID's architecture, the server holds no browsing records. Not "we deleted the records" — "the records were never uploaded." Those two statements sound similar. In security engineering, they are separated by the entire history of internet privacy.

Back to the Load-Bearing Wall

Why is ZK Proof a load-bearing wall rather than a door lock?

Because a door lock is a management mechanism. An administrator can unlock it today. A database admin can bypass it. An internal bad actor can circumvent it. A court order can compel it to be opened. Any system that depends on "permissions being correctly configured" and "administrators not making mistakes" is permanently fragile. It doesn't get broken by technology — it gets broken by human nature.

A load-bearing wall is different. It's a structural constraint.

In DataDID's architecture, "raw data stays local" is not a setting that can be toggled off. It's not a policy switch that can be flipped through an admin panel. It's not an exemption available under certain elevated permissions. It is a physical fact embedded in the code execution path: data collection runs locally, the ZK circuit runs locally, proof generation runs locally. There is no code path in the entire data processing pipeline that sends raw data to a server. Even if someone obtained every server credential, every database password, every API key — they still couldn't get the user's browsing records, because those records have never existed on the server.

That is what "last line of defense" means.

Encryption can be decrypted. De-identification can be re-identified. Compliance can assign liability after a breach but cannot prevent the breach itself. Architectural constraints cannot be circumvented. It's the difference between a system that is physically incapable of doing something versus a system that is configured to not do something.

To be candid, this design has a real cost. ZK circuits running locally means the computational overhead lands on the user's device rather than a centralized server cluster. Local ZK proof generation has meaningful hardware requirements, and the engineering optimization work involved is substantially greater than centralized server-side processing would be. Every time the team has debated moving ZK computation to the server to improve user experience, we've stopped for the same reason: the moment data leaves the user's device, it no longer belongs entirely to the user.

We've decided that cost is worth paying.

One More Thing, If You've Made It This Far

The past twenty years of internet technology have, in a real sense, been a story of data centers accumulating power and users gradually surrendering control. From local software to SaaS, from owned servers to cloud computing, each technological migration has said the same thing: hand us your things and we'll manage them for you. This narrative holds up in the dimension of convenience. It largely holds up in the dimension of security — professional data centers genuinely are less likely to lose your data than your personal hard drive.

But in one dimension, it has failed completely. Control.

Your photos in the cloud: the cloud provider can see them. Your documents in an online editor: the platform can scan them. Your browser open: dozens of tracking scripts are recording your every move. These behaviors are all technically described as "providing a service," but they all point to the same structural outcome — you no longer own your data. You're merely permitted to access it.

ZK Proof is a technology with the potential to reverse that trajectory.

Not because it's already perfect. Not because it's solved every problem. Not because it's been fully validated at massive production scale. But because it is the only known cryptographic tool capable of simultaneously satisfying two contradictory requirements: data that is useful and data that never leaves you.

DataDID's Data Mining module is one small step in this direction — a concrete product experiment. The proposition we're testing: can a product that helps users earn returns from their data, and a technical architecture that rules out privacy leakage at the structural level, be delivered as a single unified product? If the answer is yes, what changes isn't just the detail of how many points some users accumulate today. What changes is a deep-seated assumption — that for data to generate value, it must be collected, uploaded, and controlled by whoever owns the data center.

Whether ZK Proofs can hold the line, time will tell.

But we've at least put up the load-bearing wall.

Because some things shouldn't depend on trust.

If someone told you there's a browser extension that earns you passive income just by being installed — no setup, no extra steps — your first reaction probably wouldn't be excitement. It would be suspicion.

That's a reasonable response.

Over the past decade, the "free service + data collection" business model has trained users to be reflexively wary. Every internet product you use is collecting your data in ways you can't see, then monetizing it in places you'll never know about. You're not the user — you're the product. That line has been repeated for years, but it's never been more true than it is today.

So when we announced that DataDID was launching its Data Mining module — a feature that lets users convert their browsing behavior into point-based rewards — the first question we had to answer wasn't "how are points calculated" or "how much can I earn." It was: "Is my data safe?"

This post answers that question from the ground up.

What We Collect — and What We Don't

Bottom line first: the Data Mining module collects signals from the public behavioral layer of your browser. It does not touch account credentials, personal identity information, the content of what you browse, or any private data.

Specifically, the system identifies and records locally: the domain of each website you visit, how long you stay on that page, and which content category that domain belongs to. All of these signals come from a layer of the browser that is publicly observable by any extension running in it — what types of sites you visit, which pages hold your attention longer, how your interests shift across different content categories. The difference between us and other actors is this: others take that data and build advertising profiles. We run the entire processing pipeline locally, and only send out a mathematical proof.

Two details are worth calling out explicitly.

First, we cannot see what you're actually reading. The specific articles you read, the videos you watch — none of that is within the scope of collection by design. We don't need to know what you're looking at. We only need to know what type of site you're visiting and whether your browsing pattern is diverse.

Second, the system has strict anti-gaming mechanisms built in: pages you spend fewer than 5 seconds on don't count as valid visits, and sub-pages under the same second-level domain are consolidated into a single entry. The underlying logic is that high-quality behavioral data comes from genuine, meaningful browsing — not mechanical page-hopping.

What ZK Proofs Do: Why a "Proof" Is Not the Same as "Data"

What genuinely sets Data Mining's privacy architecture apart from conventional data collection is zero-knowledge proofs (ZK Proofs).

The concept might sound abstract, but the principle is straightforward.

The traditional approach: the data collector takes your data, stores it on their servers, then tells buyers "this data is real." In that pipeline, your data has already been copied in full and handed off. What happens to it next — where it's stored, how it's used, when it gets deleted — depends entirely on the collector's integrity. You have no actual control.

Our approach: all raw data processing happens locally on your device. A ZK circuit then generates a mathematical proof. That proof can verify something like: "This user visited 20 distinct domains in the past 24 hours, spanning more than 5 content categories, with all visits representing genuine browsing sessions of at least 5 seconds." But it cannot be reverse-engineered to reveal which specific sites you visited, in what order, or at what time.

One sentence captures the difference: traditional systems export your data. ZK systems export a proof about your data.

A commonly used analogy: you walk into a bar and the bouncer needs to confirm you're 21 or older. The traditional approach — you hand over your ID, which has your birthdate, name, home address, and photo. The ZK approach — you present a mathematical proof that states "this person's age ≥ 21," nothing more. The bouncer gets what he needs. You keep everything you didn't need to share.

When designing Data Mining's architecture, we faced essentially the same trade-off. What does the AI training data market actually need? Not the specific articles you read — it needs the signal that "this is a real user with diverse browsing behavior." The value of that signal comes from its diversity and authenticity, not from its specificity.

So raw data never leaves your device. What goes on-chain is only the verification credential generated by the ZK Proof — a cryptographic "mathematical attestation" that records your behavioral diversity but cannot be used to reconstruct your browsing history. That was the design boundary we drew from the very beginning and never moved.

Why the Toggle Defaults to Off

One decision during Data Mining's product design generated significant internal debate: should the data incentive module default to on or off?

Default-on means zero friction for the user and much better early participation numbers. That's almost conventional wisdom in internet product design — every additional step in a flow causes meaningful drop-off.

We chose default-off anyway.

The reason is simple: data collection is in the middle of a global trust crisis. GDPR has levied over €4.5 billion in cumulative fines. Regulators across jurisdictions are tightening provenance requirements for AI training data. The "collect first, notify later" product logic is being systematically challenged. In that environment, a data-related product where users have to discover for themselves that they even have a choice — that product has already compromised its own credibility before it's shipped.

So the first time you enable the Data Mining module, the plugin surfaces a clear authorization screen that specifies exactly what signals are collected, what they're used for, and that you can turn off the toggle and revoke consent at any time. Turning it off stops collection immediately. Accumulated points are not cleared.

We believe the path forward for the data economy isn't using better technology to collect data more invisibly. It's using stronger mechanisms to genuinely return data control to users. That sounds like a platitude, but at the product level it comes down to a single concrete choice: default-off instead of default-on.

The Last Line of Defense: No Raw Data Stored Server-Side

There's one more detail that's easy to overlook but critical to the privacy architecture: DataDID's servers do not store users' raw browsing behavior data.

This means that even in the most extreme scenario — a successful attack on DataDID's backend — what an attacker could access would be only the on-chain ZK Proof verification credentials. There would be no browsing records to reconstruct, because the raw data never left users' local devices in the first place.

This is the most fundamental distinction between privacy by design and privacy by promise. A promise is a sentence in a whitepaper. A design is a physical constraint baked into the system architecture. Promises can be broken. Architectural constraints cannot be circumvented — even the system administrators themselves have no access to data that was never stored on the servers.

This is why, at the architecture design stage, we chose a fully local ZK Proof approach rather than uploading data to a server for "de-identification processing." The latter would have been more cost-efficient in operational terms — running ZK circuits on centralized server hardware is far more efficient than running them distributed across user devices. But the cost savings would have come at the price of a security gap: the moment data leaves a user's device, it no longer belongs entirely to that user.

A Final Note

As a product, the Data Mining module is a points incentive tool. But from where we started, it's closer to an experimental proof of concept. The proposition we wanted to test: can a product that helps users earn returns from their data, and a technical architecture that fully respects user privacy, coexist?

That sounds like walking a tightrope. But the maturation of ZK Proof technology has made that rope considerably thicker than it was a few years ago.

Users have never been bystanders in the data economy. They've simply never had the tools — a mechanism that lets them participate in data value distribution without surrendering their privacy. Data Mining is our first answer to that problem.

It's not perfect. But the direction is right. The rest is up to time.

DataDID is MEMO's decentralized data identity system. It enables on-chain confirmation and circulation of data assets through the ERC-7829 protocol. The Data Mining module is now live in the DataDID browser extension.

👉 datadidapp.memolabs.net

Ever since Data Mining launched, the most common question we’ve seen is:

How exactly are points calculated? Why do some people earn nearly twice as many points as others, even with similar online time?

It’s a great question because it gets to the heart of one of Data Mining’s most important design decisions.

Most people assume the system is simple:

Keep the plugin running, stay online longer, earn more points.

That’s partially true.

Online points are positively correlated with time spent online. You earn 6 points per hour, and the longer your consecutive online streak, the higher your bonus multiplier becomes.

But that’s only the first layer.

The real difference comes from something many users haven’t noticed yet:

Data Contribution Points.

Unlike online points, these aren’t tied directly to time. They’re tied to the diversity of your browsing behavior.

A Simple Experiment

To make this easier to understand, let’s look at a hypothetical example.

Three users spend the exact same Thursday online.

Each remains active for 8 hours.

The only difference is how they browse.

User A: The Engineer

In the morning, A opens a few technical documentation sites.

In the afternoon, they spend three hours on Stack Overflow debugging code.

Almost all of their browsing falls into two categories:

• Technology
• Developer Tools

Without realizing it, A is repeatedly visiting pages within the same knowledge domain. From the system’s perspective, many of those visits are consolidated into a relatively small number of effective browsing signals.

User B: The Content Creator

B’s day looks very different.

• Morning: Tech news
• Noon: Financial data
• Afternoon: Browsing Pinterest for design inspiration
• Evening: Reading discussions on Zhihu

Their browsing spans:

• 4 content categories
• Approximately 12 unique domains

User C: The Multi-Domain Explorer

C is also an average user, but their work requires frequent context switching.

Throughout the day they move between:

• Technology
• Finance
• Education
• Healthcare

During lunch they watch food videos.

Before finishing work they check a few sports articles.

By the end of the day they’ve accumulated:

• 20 effective domains
• 6 IAB-standard content categories

The Results

At the end of the day:

• User A: 52 points
• User B: 78 points
• User C: 101 points

Same online time.

Nearly double the score.

That difference reflects one of the core ideas behind Data Mining:

The value of AI training data comes from diversity, not volume.

Spending an afternoon naturally moving across six different domains can be significantly more valuable for training general-purpose AI systems than spending six hours deeply focused on a single topic.

Three Rules You May Have Missed

Behind the scoring system are several important rules that many users never notice.

Each one exists for a reason.

Rule #1: Visits Under 5 Seconds Don’t Count

This isn’t designed to limit your earnings.

It’s designed to identify genuine browsing behavior.

If you click a link and close the page before it even finishes loading, that’s not meaningful engagement.

Your attention was never actually invested.

From an AI training perspective, that signal is mostly noise.

The 5-second threshold is intentionally low, but highly effective.

It separates:

• “I actually consumed this content”
• “I merely passed through”

Rule #2: Pages Under the Same Domain Are Consolidated

A common question is:

“If I read 30 articles on the same website, why don’t I get credit for 30 effective visits?”

Because the system isn’t measuring how much content you consume on a single site.

It’s measuring how broadly your interests extend across the web.

Browsing multiple pages within one domain demonstrates depth.

Browsing across many domains demonstrates breadth.

And for AI training, breadth is often far more valuable.

Someone who shows interest in technology, art, and sports all within the same day provides a richer behavioral signal than someone who spends the entire day inside a single website ecosystem.

Rule #3: Effective Domains Are Capped at 20 Per Day

This number wasn’t chosen randomly.

Once you’ve visited 20 meaningful domains in a day, you’ve already demonstrated substantial browsing diversity.

Beyond that point, additional domains contribute diminishing informational value.

For AI training purposes, 20 distinct domains are generally enough to create a robust picture of someone’s interests.

The cap also acts as a natural anti-abuse mechanism.

You don’t need to spam-click random websites to maximize earnings.

Normal browsing behavior is enough.

The goal is genuine diversity — not artificial activity.

What These Rules Are Really Teaching Us

Viewed from another angle, Data Mining’s anti-abuse system is actually defining what high-quality data contribution looks like.

The 5-second rule tells us:

Meaningful data comes from real attention.

Domain consolidation tells us:

The system values cross-domain interests more than repetitive activity within a single site.

The 20-domain cap tells us:

A person’s interests can be effectively represented without endless data collection.

Your Existing Habits Already Have Value

The good news is that you don’t need to change the way you use the internet.

You don’t need to force new behaviors.

You only need to understand one thing:

Your browsing habits already have value.

And some behaviors happen to be more valuable than others.

The most valuable signals often come from something completely natural:

Moving between different interests, topics, and communities throughout your day.

Your Data Is Finally Working for You

Let’s go back to our original example.

The difference between Users A, B, and C wasn’t about effort.

It wasn’t about spending more time online.

It was about the diversity that already existed in their digital lives.

Data Mining simply turns those naturally occurring signals into measurable rewards.

Install the plugin.

Browse normally.

Let it run.

And for the first time, your data can start working for you.

The Data Mining module is now live in the DataDID Browser Extension.

👉 datadidapp.memolabs.net

Last week, Anthropic published a research report titled Paving the Way for Agents in Biology. The team deployed multiple scientific AI agents — Claude, GPT, Biomni, and others — into virology databases like NCBI Virus to run sequence data retrieval experiments. The results were surprising: without an added deterministic retrieval layer, not a single model could reliably hit the accuracy threshold required to build a dependable dataset.

But the models weren’t the problem.

Anthropic’s analysis identified three systemic weaknesses in current biological database infrastructure: fragmented data, highly customized formats, and inconsistent interfaces. These databases were designed around how human researchers interact with information — not how AI agents programmatically query it. Once the team inserted a deterministic retrieval tool (gget virus) as an intermediary layer between the agents and the databases, accuracy jumped to nearly 100%.

The implications of this research reach far beyond biology. It exposes a structural tension that is accelerating: AI agents are becoming data consumers at unprecedented scale, but the data infrastructure they depend on was built for humans. That gap — wider in some fields, narrower in others — exists across every vertical.

An Infrastructure Gap Nobody’s Talking About

Pull the lens back from biology, and the contours of this problem become clearer.

For the past two decades, the architectural logic of internet data infrastructure has rested on a single core assumption: the primary consumers of data are human beings. Database query interfaces, data format standards, access authorization mechanisms — all of it was built around the benchmark of “how does a person look at this, how does a person operate it?”

When AI agents began arriving as large-scale users, that logic started breaking down.

Agents don’t need graphical interfaces. They don’t need pagination. They don’t need dropdown menus. What they need is structured data that can be retrieved reliably, data identity that can be verified, and interfaces that support high-frequency programmatic calls. When those capabilities are absent, agents do exactly what Anthropic’s experiment showed — they keep hitting walls inside a fragmented data maze.

This isn’t just biology’s problem. Financial databases, healthcare data platforms, government open data repositories — every database designed for human use is facing the same agent incompatibility problem.

MEMO’s Response — From Storage Layer to Agent Infrastructure

This structural tension is precisely what explains the evolution in MEMO’s positioning over the past year.

MEMO entered the market as a decentralized storage project. But as the technology developed, the team arrived at a clearer realization: storage is only one slice of the problem. What actually needs to be rebuilt is the entire technical stack through which AI agents access, verify, and consume data.

MEMO is currently building this agent-native infrastructure around three core capabilities.

Data DID: Giving Data an On-Chain Identity

One critical pain point that Anthropic’s experiment exposed was the agent’s inability to confirm whether retrieved data could be trusted. Who submitted a particular gene sequence? Has it been altered? What’s its version history? That information is scattered across disparate metadata systems, forcing agents to expend significant compute on backward verification.

MEMO’s Data DID protocol assigns every piece of data a unique on-chain identity. From the moment data is created, its origin, timestamp, update history, and reference relationships are recorded immutably on-chain. When an agent retrieves data, it simultaneously receives a complete, verifiable provenance chain — moving trust verification down into the infrastructure layer rather than leaving it as something the model has to repeatedly re-check on its own.

x402 + ERC-8004: A Two-Sided Market Designed for the Agent Economy

Current biological database operations are heavily dependent on government grants and institutional funding. Data is openly available but interfaces are outdated and inefficient. That model isn’t sustainable at agent-scale query volumes — not because costs blow up the budget, but because responsiveness can’t keep pace with call volume.

The x402 protocol provides an atomic, pay-per-use model for data consumption. Every time an agent calls a dataset, a micropayment is automatically processed. Database operators gain a direct economic incentive to maintain data quality and accessibility. The ERC-8004 delegated computation protocol addresses the data transfer efficiency bottleneck: rather than downloading full datasets locally for analysis, agents offload computation to nodes close to where the data is stored and receive only the results.

Together, these form a closed-loop, two-sided market between data providers and agent consumers. This is not just orders of magnitude more efficient than the legacy FTP-plus-static-page paradigm — more importantly, it provides the first viable economic framework for agents to consume data at scale.

Unified Addressing and Decentralized Storage: A Ground-Level Fix for Fragmentation

The data fragmentation problem Anthropic identified has a natural solution in a decentralized storage architecture. All data on the MEMO network is addressed through a unified protocol. Instead of facing hundreds of databases with incompatible formats and inconsistent interfaces, an agent faces a single, unified, programmable data plane.

From Biology to Every Field — A Universal Infrastructure Paradigm

Anthropic’s report is focused on biology, but its core argument applies to a much wider industrial landscape: databases need to be redesigned for agents as large-scale users.

This isn’t incremental improvement. It’s a paradigm shift at the infrastructure level.

Before the agent economy fully arrives, whoever builds AI-native data infrastructure first will control the critical intermediary layer between agents and data. That is exactly where MEMO is positioned: providing AI agents with a data layer that is trustworthy, queryable, and payable on demand — while giving data providers decentralized deployment and a revenue distribution mechanism.

Anthropic found a crack in the biological domain and patched it. MEMO’s goal is to rebuild the foundation for the agent era before that crack becomes a systemic collapse.

When a top AI research institution starts using experimental data to argue that infrastructure needs to be redone, the direction itself is no longer in dispute.

The only questions left are: who builds it, and how fast.

📢 Data Mining is Now Live — Earn Points Just by Leaving Your Browser Open

The DataDID plugin has launched its Data Mining feature. After installing the plugin, grant it permission to collect anonymized browsing data. Raw data is processed entirely locally and never uploaded; only proof of contribution is recorded on the blockchain via ZK Proofs. Users automatically earn points based on their data contribution. In a nutshell: Install the plugin, enable Data Mining, browse the web as usual, and watch your points grow automatically.

We invite you to try it out:
👉 [datadidapp.memolabs.net]

The United Nations University Institute for Water, Environment and Health released a report a few days ago. The numbers are blunt enough to make you stop and sit in silence for a moment: by 2030, global AI data center electricity consumption will double from 448 terawatt-hours to 945 terawatt-hours per year — and water consumption will jump from 4.5 trillion liters to 9.3 trillion liters.

What Does 9.3 Trillion Liters Actually Mean?

Here’s one way to grasp it. Today, more than 600 million people in sub-Saharan Africa lack access to basic water for daily living. By 2030, that number climbs to 1.3 billion. The water that AI data centers will consume in a single year is enough to supply all 1.3 billion of those people for an entire year.

Kaveh Madani, Director of the UNU Institute for Water, Environment and Health and the report’s lead author, put it plainly: “The industry’s relentless race for growth is overriding the most fundamental principles of sustainability.”

That stings. But after reading through the primary data ourselves, it’s hard to argue with him.

Let’s Break Down the Report

In 2025, data centers worldwide consumed 448 terawatt-hours of electricity. AI accounted for roughly one-fifth of that. By 2030, AI’s share is projected to climb to 40%.

Why? Because models keep getting larger, inference sequences keep getting longer, and multimodal capabilities send per-call energy costs through the roof. A single inference request at the GPT-5.5 tier consumes more than ten times the energy of a GPT-4 call from two years ago.

And it’s not just training that burns resources. The agentic era has arrived. A single task now orchestrates dozens of sub-agents and spawns hundreds of tool calls, with token counts exploding at every step. Every additional token means additional energy — and another scoop of cooling water.

TSMC recently noted that AI demand is so intense that capacity can only “support so much.” NVIDIA’s newly released Nemotron 3 Ultra is explicitly designed for “long-running agents” — translation: the old paradigm of run-and-exit is over. Today’s AI is supposed to stay on, keep thinking, and keep calling tools indefinitely. Like an intern who never goes home.

Behind all of this: denser server rooms, taller cooling towers, and river evaporation rates creeping up year after year.

A Question Worth Sitting With

Where does AI end up, ultimately?

Bigger models? Stronger reasoning? More servers, more electricity, more water?

Here’s a counterintuitive fact: the global energy consumption curve for data centers tracks almost perfectly with the AI capability curve. Every ChatGPT conversation, every Cursor autocomplete, every NotebookLM summary translates into real electricity bills and real cooling water charges somewhere in the world.

And that bill is spiraling out of control.

Cloudflare Radar recently published a striking data point: over the past week, 57.5% of global HTML web requests came from bots. Only 42.5% came from humans. The internet is no longer primarily a place where people browse — it’s a place where machines talk to machines, scrape data from each other, and train each other.

Every one of those machine-to-machine communications burns energy.

The internet is transforming from something built for humans into something built for machines. And machines have a much bigger appetite.

So What Do We Do?

Efficiency improvements are real and ongoing — better chips, more optimized inference frameworks, more aggressive quantization. These help. NVIDIA’s Nemotron 3 Ultra alone delivers meaningful cuts to inference costs.

But efficiency gains are symptom relief. The underlying condition is the architecture itself.

Look at the structural logic of today’s AI data infrastructure. It’s built to funnel the world’s compute and storage into a handful of hyperscalers. AWS, Google Cloud, and Microsoft Azure collectively control over 60% of global cloud compute. Data centers grow larger. Cooling towers grow taller. Power demands grow faster.

That centralized model has a structural flaw: the larger the scale, the lower the marginal efficiency.

Pack ten thousand servers into one campus, and heat dissipation becomes a physical nightmare. Liquid cooling, immersion cooling — whatever you try, the conversion efficiency from electricity to useful compute always hits the same ceiling imposed by centralized physics.

And there’s another layer most people overlook: most of the data stored in these facilities is duplicated.

The same model weights stored across a hundred nodes. The same dataset downloaded separately by dozens of teams. The same video shuttled back and forth across CDN nodes on three continents. The energy wasted on storage redundancy is larger than most people imagine.

This Is Where MEMO’s Thinking Comes In

MEMO’s core premise is simple: don’t put all the world’s eggs in one basket — and don’t pour all the world’s cooling water into one pool.

MEMO uses a decentralized storage network (MEFS) to distribute storage tasks across idle nodes scattered around the globe. You don’t need to build a million-square-foot hyperscale data center. You just activate storage resources that already exist, already distributed, already sitting mostly unused.

The benefits go beyond data sovereignty and privacy.

Take cooling. Centralized data centers dedicate specialized cooling infrastructure that accounts for 30–40% of total power consumption. Decentralized nodes operate in ambient environments — they don’t require centralized cooling, and that entire chunk of energy overhead simply disappears.

Academic research backs this up. A 2025 study published in Energy and Buildings compared centralized and distributed cloud architectures directly. The conclusion was unambiguous: distributed architecture delivers 19–28% energy savings.

That’s not a projection or a thought experiment. It was measured.

There’s another hidden cost in centralized storage worth naming: data transit. A model inference call from Beijing might route through a data center in Virginia — crossing Pacific undersea cables, bouncing through more than a dozen routing nodes, burning transmission energy at every hop.

MEMO’s decentralized network stores data close to where it’s needed and retrieves it locally. Routing hops drop by more than half. In the agentic AI era — where agents read and write data continuously at high frequency — the transaction cost isn’t just gas fees. It’s real, physical electricity.

A Candid Note

Decentralized storage isn’t a cure-all. It doesn’t solve every AI energy problem. It doesn’t replace solar or wind. Its value is in offering a different possibility: AI infrastructure doesn’t have to follow the centralized playbook.

You don’t have to concentrate the world’s compute in three companies’ server rooms. You don’t have to let a single data center drain a city’s water allocation. You don’t have to keep fighting the laws of physics with “bigger, denser, hotter.”

There’s another way to look at it.

Activate idle hard drive space. Connect distributed nodes into a coherent network. Let data live where it belongs, instead of routing everything into a single massive warehouse.

Simple in concept, hard in execution — it requires the protocol layer, the incentive layer, and the consensus layer to work together. MEMO has spent over three years building this infrastructure: the x402 protocol connecting AI with on-chain payments, ERC-8004 defining decentralized data interaction standards, and the DataDID plugin giving users full control over their own data.

These look like Web3 vocabulary. At ground level, they address one problem: infrastructure efficiency isn’t a one-way street of hardware optimization. Architectural rethinking is a far larger lever.

Back to That UN Report

9.3 trillion liters of water. 945 terawatt-hours of electricity. 399 million tons of carbon emissions.

These numbers describe an industry sprinting toward something unsustainable.

Kaveh Madani’s statement had a second half: “With nations and corporations rushing to build new compute infrastructure, overall water and energy demand will in all likelihood continue to rise.”

In other words: efficiency alone isn’t enough. Switching to more power-efficient chips alone isn’t enough. The industry needs a genuinely different architectural choice.

The MEMO team holds one belief that’s been constant throughout this work.

The future of AI shouldn’t be built inside a handful of giant data centers. It should grow across the hard drives of countless individuals and nodes distributed around the world.

Not centralized — distributed.

Not monopolized — collectively built.

Not “bigger, denser, hotter” — but more dispersed, more efficient, more sustainable.

The road ahead is long. MEMO has been on it for three years.

2030 isn’t far away.

9.3 trillion liters isn’t science fiction. It’s a number the United Nations ran through models and calculated seriously.

When we look back at today from that vantage point, someone will ask: at that inflection point, some people chose to build more servers. Others chose a different path. Which one were you?

This has been in the works for a long time — both the idea and the execution.

Today, we’re officially announcing the most important feature update we’ve shipped for the DataDID plugin: the Data Mining module is now live.

Something You Probably Haven’t Thought About

Every time you open a browser, you’re generating data — which categories of sites you visit, which pages hold your attention longer, how your interests drift across different topics throughout the day.

AI companies are paying real money for exactly this kind of data. Not because they want to know who you are — but because authentic sequences of real user behavior are the scarcest raw material for training general-purpose AI agents. It can’t be synthesized. It can’t be scraped.

That money has never made it back to the users generating it.

DataDID is here to change that.

How the Data Mining Module Works

As you browse normally, your browser naturally produces observable, public behavioral signals: which categories of sites you visit, how long you spend on each page, what topics the content falls under. None of this involves passwords, identity information, or anything private — these are objective signals that exist at the public behavioral layer of the browser.

What we do is structure this public behavioral data, run it through ZK Proofs (Zero-Knowledge Proofs) for local anonymization, and package it into behavior datasets formatted for AI training.

One important thing to be clear about: your raw data never gets uploaded. What goes on-chain is a verifiable “mathematical proof” — a proof that you have diverse browsing behavior, not the browsing history itself. Your privacy boundary is set at the design level.

How Points Are Calculated: Two Systems

Online Points — Simply having the plugin active signals that your node is available. Points are issued hourly. The base rate is 6 points per hour, with a streak multiplier that increases the longer you stay consistently online, up to a maximum of ×1.5. Daily cap: 108 points. Simple rule: the more consistently you participate over time, the more you earn.

Data Contribution Points — This is where we spent the most time on design. We deliberately chose not to measure by traffic bytes — byte counts are a black box, users can’t feel what they’ve contributed, and AI training data derives its value from behavioral diversity, not volume. So we measure by the number of unique domains effectively visited.

Visit 20 distinct domains in a day, spanning categories like tech, finance, and education, and you’re eligible for the highest diversity and quality multipliers. The system also has anti-gaming measures built in: pages you spend fewer than 5 seconds on don’t count, and sub-pages under the same second-level domain are consolidated.

A real example: A user who has been online for 7 consecutive days and visited 20 quality domains the day prior can earn up to 101 points — without doing anything at all.

A Few Details Worth Knowing Upfront

The Data Mining module is off by default. When you enable it for the first time, a clear authorization dialog explains exactly what data is collected, what it’s used for, and that you can revoke consent at any time. The toggle lives in the plugin — control stays with you. Turning it off stops collection immediately, but your accumulated points remain intact.

The plugin also launches a companion dashboard so you can see your stats in real time: today’s online hours, yesterday’s point breakdown, your 7-day point trend, and your current data quality tier. The web app offers a 14-day daily detail view, with a color-coded stacked bar chart that shows the ratio of the two point types at a glance.

What DataDID Is Actually Trying to Do

Some projects turn idle bandwidth into income. Others monetize residential IPs. The DataDID Data Mining module has a sharper focus: return ownership of personal browsing behavior data to users, while turning ZK-anonymized behavior datasets into a visible, ongoing income stream in the AI era.

Users have never been bystanders in the data economy. They just never received their share of the returns.

That changes now.

Open your DataDID plugin, find the Data Mining module, flip the switch, and let it run.

👉 datadidapp.memolabs.net

Imagine an ordinary morning in 2030.

You pick up your phone, scroll through social media for twenty minutes, post a tweet about breakfast, chat with a friend about a movie you recently watched, and casually answer a question from an AI assistant.

In the top-right corner of your screen, a tiny number keeps ticking upward.

It’s your daily data earnings notification — an AI company’s training system has accessed your content three times today, and the licensing fees have already been automatically settled into your account. Not a huge amount, but real.

You put your phone down and continue eating breakfast, barely thinking about it. Just like how today you use Alipay to pay or WeChat to message people without finding it remarkable.

Right now, this scenario sounds like science fiction.

But what if I told you that every piece of technology required to make it happen already exists today?

1. An Overlooked Reality

Before talking about “the future,” let’s first talk about something happening right now.

Over the past few years, the speed of AI development has shocked everyone. From ChatGPT to large language models, from text generation to image and video creation, the boundaries of AI capability are expanding at a visible pace.

But very few people seriously ask one question:

What exactly are these AI systems trained on?

The answer is simple:

Human data.

Every post you make on social media, every keyword you type into a search engine, every digital trace you leave across platforms — all of it forms the foundation of today’s most powerful AI models.

Without this data, modern AI would not exist.

And where does that data come from?

From you.
From billions of ordinary internet users just like you.

So here’s the real question:

When AI companies use this data to build models worth hundreds of billions of dollars — and then use those models to build commercial empires — what do ordinary users get in return?

The answer is:

Almost nothing.

You provided the raw materials for free. Someone else used those materials to build the factory. The factory generated enormous wealth, but that wealth had nothing to do with you.

This isn’t a conspiracy theory.

It’s simply how the current digital economy works.

2. Why Has This Happened?

To understand why ordinary users have been excluded from AI’s value distribution system, we first need to understand a deeper issue:

Data ownership has never been taken seriously.

The content you post on social platforms may legally belong to you, but the platform typically has extremely broad rights to use it.

The same applies to tweets on Twitter/X.

As for behavioral data generated inside apps, platforms often claim ownership so completely that users cannot even access the data themselves.

This system was created during the early internet era, when the value of data was not fully understood.

Platforms offered free services. Users contributed data. An unspoken transaction took place between both sides.

The problem is that over time, this trade has become increasingly unfavorable for users.

AI has made the issue impossible to ignore.

When data becomes the core production resource of AI training — and the AI industry grows into a trillion-dollar market — the question of “Who owns the data?” stops being merely a legal or technical issue.

It becomes a fundamental question about wealth distribution.

3. The Technology Is Already Ready

The good news is that the technology needed to solve this problem already exists today.

Layer One: Data Ownership Verification

Blockchain technology can create immutable on-chain ownership records for data.

Who created the data, when it was created, and how it has circulated can all be permanently anchored on-chain and transparently verified.

This solves the foundational problem:

Who actually owns the data?

Layer Two: Self-Sovereign Identity

Decentralized Identity (DID) systems allow users to own digital identities that do not depend on any platform.

This identity is generated and controlled by you.

No platform can unilaterally take it away.

Your data assets, authorization records, and behavioral history are tied to your identity — not to a company’s servers.

Layer Three: Automated Licensing and Settlement

Smart contracts can automate the entire authorization process through code.

If an AI company wants to use your data, it triggers a licensing contract, pays the required fee, and the earnings are instantly settled into your account.

No lawyers.
No intermediaries.
No manual operations.

Everything happens automatically.

Together, these three layers form a complete infrastructure for a new data economy:

• Data ownership verification
• Self-sovereign identity
• Automated revenue distribution

This is not just a concept.

It’s not a vision trapped inside a whitepaper.

It is a technological path that can already be built and used today.

4. Ordinary People Can Finally Participate in AI Development

Once this infrastructure becomes reality, one thing changes fundamentally:

For the first time, ordinary people become suppliers to AI — not just raw materials.

Today, you are an AI user.

You use AI tools to improve productivity and save time.

At the same time, you are also unknowingly an AI trainer.

Your data is already being used, even though you are not informed and receive no compensation.

But in the future, you can become an active data supplier to AI systems.

You can choose to verify ownership of your data, turn it into an asset, license it to AI companies, and receive real economic rewards in return.

This is not a small difference.

It is a fundamental shift in identity.

From passive raw material
to active participant.

From the edge of the value chain
to one of its core nodes.

And importantly, this is also beneficial for the AI ecosystem itself.

One of the biggest challenges facing AI companies today is the shortage of high-quality training data.

Freely scraped internet data may be massive in quantity, but its quality is inconsistent, and copyright risks are becoming increasingly severe. Lawsuits from organizations like The New York Times have already demonstrated how serious this issue has become.

When data can be verified, legally licensed, and market-priced, AI companies gain access to higher-quality and more trustworthy training data.

Meanwhile:

• Data suppliers (ordinary users) receive fair compensation
• AI companies gain compliant data sources
• The entire ecosystem operates under transparent and equitable rules

This is what the AI economy should look like.

5. What’s Missing Is Not Technology — It’s Awareness

Now let’s return to that ordinary morning in 2030.

For that future to become reality, we need more than mature technology.

We need a shift in awareness.

Enough people need to recognize that the data they generate every day is a valuable asset — not free raw material for platforms to harvest indefinitely.

And that shift in awareness is already happening.

AI’s rapid rise is accelerating it.

Once the enormous value of the AI industry became visible to everyone, the question of “Who owns the data?” could no longer be avoided.

Every person who begins thinking about data sovereignty today is helping push this transformation forward.

Every person who chooses to take control of their digital identity today is casting a vote for a fairer AI economy.

Data sovereignty is not some distant Web3 ideal.

It is a real transformation already underway.

And you can choose to become part of it starting today.

MEMO is a decentralized AI data infrastructure network developed by the Memolabs team. Its core mission is to provide decentralized storage, data ownership verification, identity authentication, data assetization, and trading services for the AI economy.

In simple terms, MEMO is building two things:

• A system where users truly own their data
• A secure and trustworthy memory layer for AI Agents

The MEMO project was launched in 2017 and now operates more than 50,000 storage nodes across Southeast Asia, the Americas, and Africa, with millions of registered wallet addresses.

Its investors include HashKey, DHVC, and SNZ, while ecosystem partners include Metis, Harmony, Alibaba Cloud, and AWS.

What Problem Is MEMO Solving?

The core contradiction of traditional cloud storage services — such as AWS or Google Cloud — is simple:

Your data lives on platform-controlled servers, which means users never truly have ownership or control.

At any moment, data can be:

• Deleted by the platform
• Sold to third parties
• Used to train AI models

Meanwhile, the original creators of that data receive nothing in return.

The AI era makes this contradiction even more obvious.

AI Agents require persistent memory. But if that memory is stored on centralized servers, everything can disappear the moment a session ends or an account gets suspended.

MEMO’s solution is to move data storage away from centralized servers and into decentralized networks, while using blockchain technology to establish verifiable ownership from the moment data is created.

In other words:

Every piece of data truly belongs to the user from day one.

MEMO’s Core Technical Architecture

MEMO uses a modular layered architecture, with different layers responsible for different functions throughout the system.

MEFS: The Core Decentralized Storage Protocol

At the foundation of MEMO is MEFS, the project’s self-developed decentralized storage protocol.

MEFS uses:

• Redundant storage
• Data sharding
• Erasure coding
• Multi-replica mechanisms

to ensure data security and resilience.

Even if part of the network goes offline, the data remains available and recoverable.

Storage task matching, fee settlement, and node verification are all managed automatically through smart contracts, without manual intervention.

Meeda: Data Availability for Ethereum Rollups

Meeda is MEMO’s data availability solution designed specifically for Ethereum Rollup scenarios.

It stores actual data off-chain inside the MEMO network, while only keeping indexes and proofs on-chain.

This significantly reduces Rollup transaction costs while still ensuring that data remains verifiable and retrievable at any time.

MemoLayer: Layer 2 Scaling Infrastructure

MemoLayer improves network throughput through an architecture that combines off-chain execution with on-chain final settlement.

It is specifically designed to support high-frequency micropayment scenarios, which are critical for autonomous transactions between AI Agents.

Zero-Knowledge Proofs (ZK)

MEMO also introduces Zero-Knowledge Proof technology at the privacy layer.

This allows users to complete data verification or transactions without exposing the original data itself.

Potential use cases include:

• DID verification
• AI training data authorization
• Privacy-preserving transactions

The MEMO Ecosystem Products

The MEMO ecosystem revolves around one central concept:

Data sovereignty.

Its products are designed for three major groups:

• Everyday users
• AI developers
• Enterprises

DataDID: The Identity Gateway of the MEMO Ecosystem

DataDID is MEMO’s decentralized data identity system and serves as the main entry point into the ecosystem.

After registration, users receive a dedicated on-chain DID (Decentralized Identifier). All points, data assets, and participation records are tied to this identity.

DataDID includes several built-in modules:

• AliveCheck (digital life protection)
• AppsList (application marketplace)
• SkillsList (AI Skill plugin marketplace)

Access:
👉 datadid.memolabs.net

MEFS MCP Server: Persistent Memory for AI Agents

The MEFS MCP Server is MEMO’s decentralized storage access service built specifically for AI Agents.

Through the MCP protocol, AI Agent clients such as OpenClaw can directly connect to the MEMO network to store:

• Conversation history
• Task outputs
• Knowledge bases

This enables true persistent memory across sessions.

Open-source repository:
👉 github.com/memoio/mefs-mcp-server

ERC-7829: The Data Asset NFT Standard

ERC-7829 is MEMO’s proposed NFT standard for data assets.

It allows content such as:

• Tweets
• Documents
• AI interaction records

to be minted into on-chain data assets.

The standard includes built-in support for:

• Data provenance tracking
• Access control
• Automated revenue distribution

ERC-7829 serves as the technical foundation of MEMO’s data assetization framework.

The Upcoming Data Marketplace

The upcoming data marketplace completes the ecosystem loop.

Once users convert their data into assets, those assets can be traded in the marketplace.

AI companies and other data buyers can purchase data through smart contracts, with revenue automatically settled directly to the original creators.

This creates a fully decentralized data economy where value flows back to those who generated the data in the first place.

How Is MEMO Different from Filecoin and Arweave?

All three projects belong to the decentralized storage sector, but their positioning is fundamentally different.

Filecoin

Filecoin focuses primarily on the general-purpose cold storage market, with miner incentives at the center of its design.

Arweave

Arweave focuses on permanent storage, making it suitable for archival use cases.

MEMO

MEMO differentiates itself in two key ways:

1. AI Agent-Oriented Real-Time Storage and Memory Management

MEMO directly supports MCP protocol integration, enabling AI Agents to use decentralized persistent memory in real time.

2. A Full Data Economy Stack

Through DataDID and ERC-7829, MEMO builds a complete loop from:

• Storage
• To data assetization
• To data trading

MEMO is not just storing data.

It is turning data into economic value.

FAQ

Q: What is the relationship between MEMO and Memolabs?

A: Memolabs is the development team and incubation lab, while MEMO is its core product and network.

Q: How can ordinary users participate in the MEMO ecosystem?

A: The easiest entry point is DataDID:

👉 datadid.memolabs.net

Users can begin by completing daily check-ins to earn points, then install the browser extension to mint tweets into data assets or access applications through AppsList.

Q: Can data stored on MEMO be lost?

A: No.

The MEFS protocol uses redundant storage and data sharding technology, ensuring that data remains available even if some nodes go offline. The system also includes self-repair mechanisms.

Q: How can AI Agents connect to MEMO’s storage capabilities?

A: By deploying the MEFS MCP Server.

Any AI Agent client that supports the MCP protocol can read and write data through the MEMO network, enabling persistent cross-session memory.

Q: What is MEMO’s standard for data assetization?

A: MEMO introduced the ERC-7829 Data Asset NFT Standard, specifically designed for data-based content rather than image NFTs.

It includes built-in access control and automated revenue distribution and is already being used through the DataDID browser extension.

Q: How large is MEMO today?

A: MEMO currently operates more than 50,000 storage nodes across Southeast Asia, the Americas, and Africa, with millions of registered wallet addresses.

It is one of the largest decentralized storage networks in terms of both node scale and user coverage.

Official Channels

• Official Website: memolabs.org
• Twitter: @MemoLabsOrg
• Telegram: t.me/memolabsio
• DataDID Portal: datadid.memolabs.net
• MEFS MCP Server: github.com/memoio/mefs-mcp-server

In 1870, John D. Rockefeller founded Standard Oil in Ohio. Within a decade, he controlled more than 90% of America’s oil refining capacity.

He didn’t win by inventing the oil engine, nor by drilling the most wells. His strategy was simpler — and more ruthless. He controlled the refineries, the pipelines, and the transportation system. In other words, he controlled the most critical thing of that era: the pricing power of fuel.

Whoever controls fuel controls the lifeblood of industrial civilization.

150 years later, no one monopolizes oil anymore. But a new battle over pricing power is quietly emerging. This time, the fuel is no longer oil, but a unit of measurement most people have never heard of:

Tokens.

I. Four Revolutions, Four Types of Fuel

Every major technological revolution has had its own core source of energy. Whoever controls the production and pricing of that energy ultimately holds the real power of the era.

The core fuel of the First Industrial Revolution was coal. Every roar of the steam engine was powered by shovels of coal burning underneath. Whoever controlled the mines controlled the fate of factories. Britain became the “workshop of the world” in the 19th century largely because it possessed Europe’s richest coal reserves and the most efficient mining system.

The core fuel of the Second Industrial Revolution was electricity. The famous “War of Currents” between Edison and Westinghouse was, at its core, a fight over the pricing power of electricity. Whichever transmission standard became dominant could effectively charge an “entry fee to modernity” for entire cities.

The core fuel of the Third Technological Revolution — the Information Revolution — was bandwidth. In the early internet era, telecom operators were the unquestioned toll booths of the digital world. Without bandwidth, even the best content could not travel. Without bandwidth, e-commerce, social media, and search engines could never have existed. The term “traffic anxiety” reflects how deeply ordinary people felt dependent on this fuel.

Now, the Fourth Technological Revolution has arrived.

Its name is artificial intelligence.

And its core fuel is called the Token.

II. What Exactly Is a Token?

For many people, the first time they heard the phrase “token economics” was probably through the blockchain industry. But as AI continues to evolve, the word “Token” no longer belongs exclusively to crypto.

In AI, it refers to something extremely simple:

The unit used to measure how AI processes text.

When you input text into ChatGPT or any large language model, the model does not read word by word the way humans do. Instead, it breaks text into “tokens” — roughly equivalent to three-quarters of an English word, or about half a Chinese character’s worth of information.

The number of tokens consumed while processing your input and generating a response becomes the billing basis for that interaction.

A more intuitive analogy:

Tokens are like AI’s electricity meter — or a taxi meter.

Every sentence you type, and every word the AI generates, quietly makes the meter tick upward.

To make this more concrete:

Suppose you ask AI to draft a 500-word business email. From your prompt to the final output, the process may consume around 1,000 tokens. At current mainstream model pricing, the cost is roughly $0.002.

Or imagine asking AI to analyze a 10-page PDF report and generate a summary. That might consume around 8,000 tokens, costing about $0.016.

Sounds cheap? It is — for individuals.

But now change the perspective.

Imagine a mid-sized company with 100 employees using AI tools daily. If each employee consumes 50,000 tokens per day, that’s 5 million tokens every day. Based on enterprise API pricing, the daily bill could reach around $10. That becomes $300 per month, or $3,600 per year.

Now scale it further.

If the company’s core business itself is AI-driven — such as an AI customer service platform or a content generation platform — it could easily process billions of tokens per day.

At that point, token costs are no longer negligible.

They become a defining variable that determines whether the business model itself works.

Tokens may be microscopic units, but token economics is macroeconomic in scale.

It determines who can afford AI — and who occupies which position in the hierarchy of this new revolution.

III. The Pricing Power of Fuel Is Once Again Concentrated

This brings us back to a recurring historical pattern:

At the beginning of every major revolution, the pricing power of the core fuel becomes highly concentrated.

At its peak, Rockefeller’s Standard Oil not only controlled refining capacity, but also manipulated railroad freight rates through secret agreements, systematically driving competitors out of business.

Early electric utilities operated as regional monopolies. Consumers had no bargaining power.

Telecom operators during the broadband era similarly controlled information flow through expensive, slow, and opaque pricing structures.

Today, AI token pricing is following a remarkably similar trajectory.

Only a handful of companies in the world are capable of independently training and deploying top-tier large language models. They possess massive parameter scales, global data center infrastructure, and enormous training datasets — all of which create towering barriers to entry.

In practice, token pricing power is concentrated in the hands of these few companies.

There is also a subtle paradox worth examining.

Over the past few years, the price per token for mainstream large models has dropped dramatically. When GPT-4 first launched, one million tokens could cost as much as $60. Three years later, models with comparable performance cost less than $1 per million tokens.

At first glance, this seems like a victory for market competition and consumers.

But there is another side to the story.

As prices fall, model capabilities grow exponentially — and more advanced models consume significantly more tokens.

Tasks once handled by GPT-4 may now require GPT-5 for acceptable results. But GPT-5 may consume multiple times more tokens than GPT-4.

“Smarter” and “more expensive” are becoming quietly intertwined.

More importantly, token pricing itself lacks transparency.

Different companies define “a token” slightly differently. Input tokens and output tokens are often billed separately. Even the model’s internal “chain of thought” reasoning may generate additional token consumption.

Ordinary users have little ability to calculate true costs or make meaningful comparisons across providers.

And opacity is one of the classic characteristics of fuel monopolies.

This is not an accusation against any specific company. Historically, the concentration of fuel pricing power has never been purely a moral issue. More often, it has been an inevitable phase of technological development.

The real question is this:

Once concentration emerges, history never stops there.

IV. How History Breaks Fuel Monopolies

Fortunately, history also shows another pattern:

Fuel monopolies never last forever.

In 1911, Standard Oil was forcibly broken up by the U.S. Supreme Court into 34 independent companies. This outcome was driven by the Sherman Antitrust Act of 1890, along with two decades of public pressure and political struggle.

What Rockefeller lost was not his oil.

What he lost was the exclusive right to set prices.

Electricity followed a different path. In most countries, power grids eventually became public infrastructure subject to government regulation. Electricity transformed from a commercial product into a basic utility everyone had the right to access.

Only when electricity became cheap enough to be almost invisible did factories achieve true 24-hour production — and modern industrial civilization fully mature.

The decentralization of internet bandwidth came largely through technological progress itself. Falling fiber-optic costs, the spread of wireless networks, and the rise of WiFi gradually transformed bandwidth from a telecom-controlled commodity into a widely accessible public resource.

Looking across these histories, one clear pattern emerges:

Every “democratization of fuel” requires two conditions.

The first is decentralized supply.

The fuel can no longer be produced by only a handful of players. Ordinary people must also be able to participate in production.

The second is a redistribution mechanism.

Producers need incentives and fair compensation, while consumers need affordable access.

In previous revolutions, these conditions were achieved through technological innovation, antitrust legislation, and government regulation.

So what about the Fourth Revolution?

What will break the monopoly over AI tokens?

V. The Next “Refinery” May Be Every Connected Device

Let’s imagine something bold.

When electricity costs approached zero, factories achieved nonstop production for the first time.

When bandwidth costs approached zero, video streaming transformed from a luxury into an everyday utility.

Whenever revolutionary energy sources become universally accessible, society experiences a massive leap in productivity.

Will Tokens follow the same path?

Technologically, the answer is probably yes.

Model inference efficiency improves by orders of magnitude every few years. Specialized AI chips are rapidly becoming cheaper. Open-source models are quickly narrowing the gap with proprietary systems.

From this perspective, the long-term direction of token costs seems obvious:

Downward.

But technology only solves the cost problem.

It does not solve the pricing power problem.

Lower costs do not automatically decentralize pricing power. Higher efficiency does not guarantee ordinary people can participate in the economic benefits of the revolution.

That is why a new economic model is emerging:

Decentralized compute networks.

Imagine a world where billions of personal devices — home servers, idle GPUs, edge devices with spare compute power — are connected through network protocols and collectively perform AI inference tasks.

Every device contributes computing power, much like individual solar panels feeding electricity into a grid, producing fuel that others can consume.

In this system, the producers of computational power are no longer just giant technology companies.

They become ordinary participants distributed around the world.

They contribute compute power and receive economic rewards in return — settled through blockchain tokens, which can then be directly used to purchase AI services, creating a self-sustaining economic loop.

This would create something unprecedented:

Ordinary people would become not only consumers of Tokens, but also producers of Tokens.

Of course, this vision remains early-stage. Many technical and economic challenges still need to be solved.

But its direction strongly mirrors every previous chapter of fuel democratization in history:

Distributed supply.
Incentivized production.
Broader participation in value creation.

Conclusion: No Fuel Can Be Monopolized Forever

In 1911, when Standard Oil was broken apart, many people believed it marked the end of the oil era.

The opposite happened.

After the breakup, the oil industry experienced the fastest expansion in its history. Distributed pricing power created competition, efficiency, and broader participation.

The power Rockefeller lost ultimately became productivity gains for society as a whole.

History never stops simply because a small group controls the fuel supply.

At the intersection of technology and systems, new paths always emerge.

Tokens will be no exception.

The Fourth Revolution has only just begun. Its core fuel — the AI capability to process information, measured in Tokens — remains highly concentrated in the hands of a few companies.

This is not a moral judgment.

It is simply a historical observation describing a process still unfolding.

But the direction is becoming increasingly clear:

When every device can participate in production and benefit from the system, the pricing power of Tokens will gradually flow from the hands of a few into the hands of everyone.

If there’s one type of content that has dominated the internet over the past two years, MBTI is definitely near the top of the list.

From “Why INTJs always prefer being alone” to “ENFPs are born social butterflies,” MBTI has practically become a new social language for younger generations.

Then came SBTI, which quickly took off in professional circles. Compared to MBTI’s focus on psychological tendencies, SBTI puts more emphasis on behavioral patterns and work styles in real-world environments.

The reason personality tests always go viral is actually pretty simple:

People are endlessly curious about one question:

“What kind of person am I, really?”

But here’s the problem.

These tests all describe your personality in the real world.

So what about the Web3 world?

What kind of person are you there?

Are you an “Alpha Hunter” constantly chasing the next opportunity at full speed?

Or a committed long-term believer with true diamond hands?

Are you an anonymous lurker?

Or an ecosystem socialite who loves interacting with communities?

In the past, these labels mostly existed as memes and inside jokes within crypto communities.

But now, RMTS (Web3.0 Personality Test), newly launched on AppsList, is trying to turn these “on-chain personalities” into something more systematic.

RMTS: A Personality Test Built for Web3 Users

RMTS is a fun personality-testing platform that supports both Web2 and Web3 login methods.

It doesn’t try to measure whether you’re “extroverted” or “sensitive.”

Instead, it analyzes your behavioral tendencies in the on-chain world based on how Web3 users actually think and act.

The entire test contains only 8 questions, but it evaluates you across four core dimensions:

• Risk
• Motivation
• Time
• Social

In simple terms, RMTS isn’t asking:

“What kind of person are you?”

It’s asking:

“What kind of Web3 player are you?”

What Kind of Personality Might You Get?

Based on your answers, RMTS generates a personalized Web3 personality profile.

For example:

Alpha Hunter

Always on the front lines. Extremely sensitive to information and naturally drawn to emerging projects and trends.

Diamond Hand Pro

A true long-term thinker who doesn’t FOMO easily and won’t let short-term volatility shake their convictions.

Beyond that, the system also visualizes your personality distribution across multiple on-chain behavioral dimensions:

• Degen / Safe (aggressive vs. conservative)
• Utility / Lore (practical vs. narrative-driven)
• Sniper / HODLer (short-term trader vs. long-term holder)
• Anon / Public (anonymous vs. highly social)

At their core, these are all real behavioral differences that exist among Web3 users.

And once you see the results, you realize something interesting:

Everyone participates in Web3 in completely different ways.

It Supports Both Web2 and Web3 Login

RMTS offers two login options:

• Email login
• Wallet login

That means even if you’re completely new to Web3, you can jump in immediately.

And for crypto-native users, wallet login makes the experience feel seamless and natural.

Why This Kind of Test Fits Web3 So Well

Because Web3 itself is an intensely personality-driven world.

Some people are obsessed with risk.

Some are deeply rational.

Some love narratives.

Some only trust data.

Some spend all day networking and engaging with communities.

Others stay permanently anonymous.

In the traditional internet era, these behaviors were simply user profiles.

But in Web3, they’re gradually becoming a new form of digital identity.

What makes RMTS interesting is this:

It’s one of the first attempts to visualize this kind of “on-chain personality” in a fun and approachable way.

Maybe You Should Take the Test

Maybe you think you’re a Diamond Hand.

Then the system tells you:

You’re actually a full-blown Alpha Hunter.

Or maybe you’ve always thought of yourself as a hardcore Degen —

only to discover that you’re really just a cautious long-term believer.

A lot of the time, people’s self-perception doesn’t fully match their actual behavioral patterns.

And that’s exactly what RMTS is trying to surface:

Your real Web3 behavioral personality.

So here’s the question:

What’s your true identity in the Web3 world?

Maybe it’s time to find out:

👉 https://datadid.memolabs.net/

If you’ve just heard about DataDID — or already created an account but still aren’t sure what you can actually do with it — this guide is for you.

DataDID is a decentralized data identity system. But rather than starting with definitions, here’s the simplest way to understand it:

Every action you take inside the ecosystem becomes part of your accumulation.

Points, data assets, ecosystem rewards — the deeper you participate, the more you build.

From registration to daily check-ins, minting, AppsList, SkillsList, event tasks, and the developer platform, the DataDID ecosystem is far more expansive than most people realize.

This guide will walk you through the entire ecosystem step by step.

Step 1: Register for DataDID and Create Your On-Chain Identity

Everything starts here.

Visit the DataDID platform and register using either your email address or a MetaMask wallet. The system will generate a unique decentralized identity (DID) for you.

This DID becomes your passport across the entire MEMO ecosystem. All of your future points, data assets, and participation records will be tied to this identity.

Registration takes about 2–3 minutes.

If someone invited you, entering an invitation code will instantly reward you with 500 starting points.

👉 Register now:

• Web: https://datadidapp.memolabs.net/
• H5: https://datadid.memolabs.net/
• Telegram: https://t.me/data_did_bot
• Pi Browser: https://datadidpi.memolabs.net/

Step 2: Daily Check-Ins — Keep Accumulating Points

After registering, the simplest — and most important — action begins:

Daily check-ins.

Click the check-in button on the DataDID homepage to complete your daily security report and receive points.

It sounds simple, but the long-term value is much bigger than it appears.

Points are the ecosystem’s record of participation and rights. They can be used for premium feature subscriptions and will also serve as an important basis for future reward distribution.

Points also benefit from a compounding time effect:

The earlier you start, the greater your long-term advantage becomes.

Step 3: Install the Browser Extension for Easier Check-Ins and More Powerful Features

If manually opening the website every day feels inconvenient, the DataDID browser extension is exactly what you need.

Once installed, you can complete daily check-ins directly inside your browser without switching pages.

But convenience is only the beginning.

The extension unlocks two much more important features.

Mint Your Tweets Into On-Chain Data Assets

After installing the extension and using it on Twitter/X, a Mint button will appear beneath your own tweets.

Clicking the button converts that tweet into an on-chain data asset powered by MEMO’s ERC-7829 Data Asset NFT standard.

Once minted, your tweet is no longer just text stored on Twitter’s servers. It becomes a digital asset with verifiable on-chain ownership:

• Owned by you
• Displayable
• Holdable
• Tradable in the upcoming data marketplace

Every tweet you mint today is positioning you for the future data economy.

AI Button: Earn Points Just by Posting

The extension also adds an AI button beneath tweet and reply boxes.

Click it, and the system automatically generates MEMO ecosystem-related tweets or quick replies. Once posted, you receive point rewards.

In other words:

You can casually use Twitter while accumulating ecosystem points at the same time.

👉 Install the DataDID browser extension:
https://datadidapp.memolabs.net/

Step 4: AppsList — Making Check-Ins More Interesting

DataDID is not just a check-in platform.

It’s a complete application ecosystem.

AppsList is DataDID’s built-in application marketplace, featuring a growing collection of functional Web3 applications.

You can log into all of them directly with your DataDID identity — no additional registration required. All activity remains tied to your DID.

Several standout applications are already live inside AppsList.

AliveCheck — A Core Safety Feature

AliveCheck is one of DataDID’s core protection modules.

Every day, users can check in to confirm they are safe. If no check-in occurs for two consecutive days, the system automatically alerts pre-designated emergency contacts.

Users can also configure:

• Location authorization
• Message capsules

A message capsule functions like a digital will. If you disappear or lose contact, the system automatically sends your preset message to selected contacts.

It’s one of the clearest examples of combining data sovereignty with real human-centered utility.

Personality Analysis

This application analyzes your on-chain behavior and data activity to generate a Web3 personality profile.

It’s both entertaining and insightful — a unique way to understand your digital identity.

Using AppsList applications also rewards you with points.

Every interaction contributes to your ecosystem accumulation.

Developers can also submit their own applications through the AppsList developer platform. Once approved, the app becomes available to all DataDID users and qualifies for ecosystem incentives.

👉 Enter AppsList:https://datadid.memolabs.net/

👉 Publish your app:https://datadid-developer.memolabs.net/

Step 5: SkillsList — Unlock More Possibilities for AI Agents

If you’re already using OpenClaw, SkillsList will take your experience to the next level.

SkillsList is a decentralized Skill plugin marketplace built specifically for OpenClaw on top of the DataDID architecture.

Here, users can browse and install Skill plugins that directly expand OpenClaw’s capabilities.

MEFS MCP Service

This is MEMO’s official decentralized storage Skill.

After installation, OpenClaw can permanently store:

• Conversation history
• Task outputs
• Knowledge base data

inside the MEMO decentralized network.

The data remains accessible anytime and doesn’t disappear when a session ends.

This gives AI Agents something critically important:

A true memory layer.

OpenClaw Skills

A growing collection of functional Skills already exists across areas such as:

• Data processing
• Content generation
• Automation tasks

allowing OpenClaw to continuously expand its abilities.

One particularly useful example is the datadid-checkin Skill.

After installation, you simply give OpenClaw a command, and both DataDID and AliveCheck daily check-ins are automatically completed for you.

Points are credited automatically — fully hands-free.

👉 Install here:https://skillhub.memolabs.net/q-hp-true/datadid-checkin

Developers can also upload their own Skills through the SkillsList developer platform. Revenue distribution is protected by smart contracts, preventing unilateral platform interference.

👉 Enter SkillsList:https://datadid.memolabs.net/

👉 Publish a Skill:https://datadid-developer.memolabs.net/

Step 6: Events and Tasks — Earn Both Points and Cash Rewards

Beyond daily check-ins and app usage, DataDID also offers advanced tasks and recurring ecosystem events.

These are among the fastest ways to accumulate points.

Completing tasks inside the platform can reward large amounts of points, while special campaigns often include direct USDT rewards.

Current Event: DataDID Mystery Box Carnival

📅 Event Period: April 24, 2026 — May 23, 2026

🎁 Prize Pool: $2,000 USDT
🏆 100% Win Rate

Rewards

• 🥇 First Prize: 300 USDT*1 (still unclaimed)
• 🥈 Second Prize: 200 USDT*2 (still unclaimed)

How to Participate

• Install the DataDID browser extension
  (First 10,000 users instantly receive 200 points)
• Register a DataDID account
  (Receive one exclusive mystery box draw)
• Invite friends
  (Each successful referral earns one additional draw opportunity — unlimited)

The top prizes are still waiting to be claimed.

👉 Join the event now:https://datadidapp.memolabs.net/

Conclusion: Every Step You Take Becomes Part of Your Accumulation

Let’s recap what you can do inside DataDID:

• Check in daily and accumulate points
• Install the extension and turn tweets into on-chain assets
• Use AppsList applications and continue earning
• Expand OpenClaw with Skills and unlock more AI capabilities
• Complete tasks and events for both points and cash rewards
• Publish apps or Skills as a developer and contribute directly to the ecosystem

DataDID is an ecosystem where every step forward becomes part of your accumulation.

Points are the most visible reward — but more importantly:

Every interaction helps build a truly personal on-chain data identity.

And over time, that identity becomes increasingly complete — and increasingly valuable.

Now is the best time to start.

👉 Register for DataDID:https://datadid.memolabs.net/

In today’s internet environment, we generate content every day—tweets, opinions, discussions, and ideas. Yet most of this content remains confined within platforms, essentially becoming part of the platforms’ data assets.

DataDID offers a new possibility: turning your content into something that truly belongs to you.

This article will walk you through the entire process of minting a tweet—from installing the plugin to generating an on-chain asset—and explain why this is something worth engaging with over the long term.

I. What is DataDID?

DataDID is a decentralized data identity system available across web, mobile, and browser plugin versions. The plugin version allows users to mint their content on X (Twitter) into on-chain data assets.

In other words, it changes one fundamental thing:

your content is no longer just “published”—it can be “owned.”

II. Tweet Minting Process

Install the Plugin

First, open the Chrome Web Store and search for DataDID,

or go to the official DataDID website 👉 datadidapp.memolabs.net to install it.Once you find the plugin, click “Add to Chrome” to complete the installation, and follow the prompts to grant permissions.

After installation, open the plugin to begin the initialization process.

search for【DataDID】

【Add to Chrome】

【Add extension】

2.  Login and Account Binding

Before getting started, you’ll need to complete the basic account setup:

● Log in using an Ethereum wallet

● Enter an invitation code

● Link your X (Twitter) account

The purpose of this step is to connect your on-chain identity with your social account, preparing for the minting process that follows.

【Bind Invite code】

【link X】

3.  Select a Tweet and Execute Mint

Go to your X homepage and find the tweet you want to mint.

At this point, the DataDID plugin will provide a Mint entry within the reply box.Click the Mint button and confirm the action in the pop-up interface to complete the process.

The entire process is very lightweight and usually takes only a few seconds.

4.  Mint Success and Asset Creation

Once the process is complete, you’ll see a confirmation indicating that the mint was successful.

This means the tweet has been transformed from “platform content” into an “on-chain asset,” and is now linked to your wallet address.

What Happens After Minting?

When you click Mint, three things essentially happen:

The tweet is transformed into an on-chain data asset

The data is permanently recorded and anchored on the blockchain

Ownership of the asset is assigned to you

This process is based on the ERC-7829 data asset protocol proposed by the MEMO network.

Unlike traditional NFT standards, ERC-7829 is specifically designed for “data,” enabling various types of digital content—including tweets, documents, AI interaction records, and knowledge bases—to be packaged as assets with verifiable ownership and transferability.Simply put, it makes data ownership possible.

Why Is This Worth Doing?

In the traditional internet:

You publish content, but don’t truly own it

The value of your content is determined by platforms

Your data is difficult to accumulate into long-term assets

Through minting:

Your content becomes your own asset

Ownership can be verified on-chain

Content can circulate on data marketplaces and generate value

All minted data is stored on the MEMO network and can later be displayed, managed, and even traded on data asset platforms.

This means every time you express something, it’s no longer just “posted”—it becomes something you can accumulate.

Final Thoughts

Minting a tweet may seem like a simple action, but it represents a broader shift:

from being a content publisher to becoming a content owner.As the trend of data assetization continues to evolve, tools like this are reshaping our relationship with the internet. In the future, participating in content creation may not just be about sharing ideas—it may be about building your own data assets.

Visit the DataDID official website to download the browser plugin. After installation, log in to your DataDID account to access all features:👉datadidapp.memolabs.net

The story of DataDID isn’t finished yet.

Following the official launch of the AppsList marketplace, the next key piece of the DataDID ecosystem has arrived.

SkillsList — a decentralized Skill plugin platform built specifically for OpenClaw — is now live.

If AppsList opened up the world of applications, then SkillsList opens up the marketplace of capabilities.

What Is SkillsList?

Simply put, SkillsList is a platform for discovering, installing, and publishing Skill plugins, designed for both OpenClaw users and developers.

For users, you can freely browse a wide range of Skill plugins, find the capability modules that fit your needs, and install them with one click — instantly enhancing your OpenClaw. What your Agent can do is now entirely up to you.

👉 Get started: https://datadid.memolabs.net/

For developers, you can upload the Skills you’ve built to the developer platform. Once approved, they will be featured on SkillsList, giving you access to real OpenClaw users and allowing your work to generate real value.

👉 Publish your Skill: https://datadid-developer.memolabs.net/

But what truly sets SkillsList apart is this:

It’s built on DataDID — and is decentralized by design.

What Makes SkillsList Different?

1. Decentralized — Your Data Truly Belongs to You

On traditional Skill platforms, plugin data and user behavior data are stored on centralized servers. Platforms can remove plugins or ban accounts at any time. A developer’s work can disappear overnight, and user activity records can be deleted or sold without notice.

SkillsList is built on the MEMO decentralized network, with plugin data stored on-chain. No single party can interfere.

Your Skill is always there — permanently.

2. Unified DID Identity — Seamless Access

Your DataDID identity is your passport across the MEMO ecosystem.

There’s no need for additional registration when entering SkillsList. The Skills you install and the capabilities you use are all tied to your DID, enabling cross-application interoperability and unified management of identity and data.

3. Developer Ownership — Revenue Guaranteed

On SkillsList, the Skills you upload belong to you.

Revenue distribution is enforced by smart contracts. The platform cannot arbitrarily change revenue splits or remove your work.

Your creations are protected at the protocol level — not by platform policies.

This is the core promise SkillsList makes to every developer.

4. Deep Integration with the Points System

SkillsList is fully integrated with the DataDID points system.

Users earn points by installing and using Skills, while developers receive ecosystem incentives as their Skills gain adoption.

Every interaction generates real value, creating a positive feedback loop between users, developers, and the ecosystem.

5. Native Fit for AI Agents

SkillsList is designed around expanding OpenClaw’s AI Agent capabilities.

Each Skill plugin directly adds a new capability module to your Agent. Combined with MEMO’s native memory layer, MEFS, any data generated during Skill usage can be permanently stored and accessed at any time.

Your Agent doesn’t just use tools — it remembers context.

6. Seamless Integration with MEFS MCP

Skills in SkillsList can be used seamlessly with MEMO’s official MEFS MCP.

This means every Skill comes with built-in decentralized storage capabilities. Developers don’t need to build their own storage layer — they can directly leverage MEMO’s infrastructure.

What Does This Mean for Users?

In the past, what your Agent could do depended on what the platform allowed.

Now, what your Agent can do depends on what you choose to install.

SkillsList returns the power of capability selection back to users.

Need stronger data processing? Find it on SkillsList.
Want smarter content generation tools? Install them on SkillsList.

The limits of your Agent are now defined by you.

More importantly, early users will gain access to the full range of ecosystem benefits:

• Points incentives
• Ecosystem rewards
• Early access to high-quality Skills

These advantages belong to those who join early.

What Does This Mean for Developers?

If you’ve been looking for a truly developer-owned Skill distribution platform, SkillsList is your answer.

You don’t need to worry about shifting platform policies.
You don’t need to worry about your work being removed without reason.
You don’t need to build complex storage infrastructure yourself.

With MEFS MCP, your Skills come with built-in storage capabilities.
With DataDID’s user base, your application reaches real users.
With smart contracts, every dollar you earn is transparent and guaranteed.

You focus on building great Skills.
The MEMO ecosystem handles the rest.

What’s Next: Secure Your Position Early

SkillsList is now live and ready to explore.

Right now, there’s just one thing you need to do:

Register for DataDID and enter the ecosystem.

DataDID is your identity gateway into the entire MEMO ecosystem. Once registered, you can immediately install Skills or publish your own.

Developers can start submitting Skills today — once approved, they will be listed directly on SkillsList.

The DataDID ecosystem is being built layer by layer.

AppsList was the first step.
SkillsList is the second.

And this is just the beginning.

👉 Users: Browse & install Skills
https://datadid.memolabs.net/

👉 Developers: Publish your Skills
https://datadid-developer.memolabs.net/

There is a question that, after 30 years of the internet, is finally being taken seriously:

Who does data actually belong to?

This question has been set aside for so long not because there is no answer, but because the answer is inconvenient for too many parties. Platforms need data to sustain their business models, advertisers need data for precise targeting, and AI companies need data to train models. Across this entire value chain, the original creators of data — ordinary users — have always been at the very end, contributing the most while receiving the least.

But now, technological evolution is fundamentally changing this dynamic.

1. The 30-Year Data Paradox

The first era of the internet solved the problem of information distribution. Anyone could publish content and make it visible to the world. This was an unprecedented form of empowerment.

The second era of the internet solved the problem of connection. Social networks, e-commerce platforms, and search engines connected billions of people into a single network. Traffic became the most important resource, and attention became the scarcest commodity.

But in the process, something quietly happened: user data became systematically concentrated in the hands of a few platforms.

What you searched for, what you expressed on social media, what you purchased on e-commerce platforms — this data has been collected, analyzed, sold, used to train models, used to predict behavior, and used to influence decisions. And all of this usually happened without your knowledge, and without asking for your consent.

The deeper paradox is this: the value of data comes from its accumulation and circulation, yet in the current system, the benefits generated from that accumulation and circulation flow almost entirely to intermediary platforms, rather than to the original source of the data — the individual.

This is a systemic unfairness that has persisted for 30 years.

2. The AI Era Makes This Issue More Urgent

If over the past 30 years the data issue was background noise, the arrival of the AI era has made it a central issue that must be addressed head-on.

The reason is simple: AI’s demand for data is unprecedented.

The capabilities of large language models depend, to a large extent, on the quality and scale of their training data. Every article, every conversation, every accumulation of knowledge can become part of a model’s capabilities. This means that every digital trace left by humanity over decades now carries greater economic value than ever before.

At the same time, a structural contradiction has become more pronounced: data creates enormous value for AI, yet that value is captured by a small number of AI companies, with no mechanism for the original contributors of the data to share in that value.

Furthermore, as AI agents begin to act on behalf of humans — automatically completing tasks, executing transactions, and generating content — the questions of data origin, quality, and ownership become critically important. If the data used by an AI agent has unclear provenance or ownership, then its actions lack a trustworthy foundation.

The issue of data sovereignty has never been so closely tied to everyone’s real interests.

3. Three New Shifts Are Quietly Emerging

Over the past two years, discussions around data assetization have begun shifting from conceptual debates to infrastructure development. Three notable changes are emerging:

First, data is being viewed as an asset, not just a raw input.

In the past, data was defined as a production input — a raw material that drives economic activity. But the defining feature of raw materials is that once they are used, the value transfers to the user, and the original owner no longer benefits.

Now, with the maturation of blockchain technology, data is being redefined as an asset that can be owned, priced, and circulated repeatedly. The fundamental difference between an asset and a raw input is that ownership of an asset is persistent, and the value it generates can continuously flow back to its owner.

This shift in understanding is giving rise to entirely new models of the data economy.

Second, verifiability is becoming a core component of data value.

In an era flooded with AI-generated content, the value of a piece of data increasingly depends on whether it can be verified: where it comes from, whether it has been tampered with during circulation, and whether its usage history is clearly traceable.

Data without verifiability carries a high trust cost in high-value scenarios. Blockchain’s immutable records and timestamps naturally provide this verifiability, enabling a complete lifecycle record for each piece of data — from creation to circulation, with every step traceable.

Third, data ownership is shifting from platforms to individuals.

This trend is still in its early stages, but the signals are clear. Users are becoming aware of the value of their data, regulators are paying closer attention to data ownership, and technological tools are beginning to offer better support.

Real change will not come from platforms voluntarily giving up control — that is unrealistic. It will come from a redesign at the architectural level. When data storage, ownership, and circulation occur within on-chain protocols directly controlled by users, platforms lose the ability to unilaterally determine the fate of data.

4. Four Key Dimensions of Data Assetization

For data to truly become an asset rather than remain a concept, capabilities must be established across four dimensions:

Ownership: Data must have clear attribution.

The prerequisite for any asset is clear property rights. The first step in data assetization is to establish on-chain ownership records for each piece of data — who created it, when it was created, and what modifications it has undergone. This information must be permanently anchored in immutable infrastructure.

Only once ownership is established does everything else become meaningful. Data with unclear ownership cannot circulate in markets or generate returns for its original creator.

Circulation: Data needs a trusted marketplace.

After ownership is established, data must be able to circulate freely. This requires a transparent and efficient marketplace where buyers and sellers can discover each other, complete transactions, and settle through smart contracts.

However, there is a subtle balance: data circulation must occur while protecting privacy. Buyers need to verify the quality and provenance of data, but they do not necessarily need access to all raw information. This “usable but not visible” requirement is driving innovation in data trading models.

Revenue: The value generated by data should return to its creators.

This is the core proposition of data assetization. When your data is used — whether for AI training, research, or enterprise decision-making — you should receive corresponding returns.

This requires the automated execution capabilities of smart contracts. Each use of data can trigger a payment to the original creator, making the process automatic, transparent, and free of intermediaries.

Protection: Data assets require reliable security mechanisms.

Like any other type of asset, data assets need protection. This includes not only preventing hacking but also avoiding permanent loss due to unforeseen circumstances.

In this regard, decentralized storage offers greater reliability than centralized servers. Data is distributed across multiple nodes, eliminating single points of failure. At the same time, account recovery mechanisms are an essential part of the data asset security system.

5. The Historic Convergence of Two Technology Curves

The reason this moment represents a critical window for data assetization is that two technological trajectories — long evolving independently — are now converging in unprecedented ways.

One is the AI curve. AI is evolving from a tool into an agent, from answering questions to acting on behalf of humans. In this process, the demand for high-quality, traceable, and clearly owned data is growing exponentially.

The other is the blockchain curve. Blockchain is evolving from a carrier of speculative assets into infrastructure for identity, payments, permissions, and auditing. Its capabilities — immutable records, automated contracts, and decentralized trust — are exactly what data assetization requires.

The convergence of these two curves creates a new possibility: data can become a true asset, rather than just a passive production input.

6. The Evolution Path Over the Next Three Years

If this direction holds, the evolution path over the next three years is relatively clear:

Short term (1–2 years): ownership infrastructure matures first.

The first step in data assetization is to establish clear on-chain ownership records. Relevant standards, tools, and platforms will mature first, allowing users to easily turn their data into assets. This is the direction closest to real demand and with the least technical friction.

Medium term (2–3 years): data marketplaces become active.

As ownership infrastructure matures, data marketplaces will begin to see real activity. AI companies, research institutions, and enterprises will become the primary sources of demand, while individuals and organizations become the main suppliers. Pricing mechanisms will gradually emerge through market interaction.

Long term (3+ years): the data economy becomes a core part of the internet.

Once data ownership and trading become foundational capabilities, a complete data economy will gradually take shape. Users will no longer be just consumers of internet content, but participants in — and beneficiaries of — the data economy.

7. Conclusion: The Second Data Revolution

The first revolution of the internet solved the problem of information distribution. It allowed content to flow freely and gave everyone a voice.

The second data revolution will solve the problem of value ownership. Data will no longer exist merely as numbers on platform balance sheets, but will return to each real creator as an asset that can be owned, protected, traded, and monetized.

This revolution will not happen dramatically. It is quietly reshaping the logic of data ownership through the establishment of technical standards, the creation of data marketplaces, and the adoption of ownership tools.

When all of this becomes reality, one truth will be self-evident:

The data you create should work for you.

In the Web3 era, data sovereignty returns to the individual. Every on-chain identity is your passport in the decentralized world.

DataDID is a decentralized identity (DID) platform built by MEMO, dedicated to giving every user true ownership of their Web3 digital identity and digital assets.

Today, we're attaching a surprise gift package worth $2,000 USDT + massive points to your identity activation journey.

The DataDID Mystery Box Season is officially live.Register to draw, invite friends for unlimited chances — and 100% win rate, no empty boxes.

II. How to Play: Clear Participation Steps

Step 1: Complete registration and create your unique DID identity

No matter which platform you use to register your MEMO DataDID account, once you complete DID creation you instantly receive 1 exclusive Mystery Box draw:

● Web: Wallet registration → Complete DID creation

● H5: Email registration → Complete DID creation

● TG / Pi: First login and complete DID creation

The whole process takes under 5 minutes — no prior experience needed.

Step 2: Visit the website or install the plugin

👉 Web: https://datadidapp.memolabs.net/

👉 H5: https://datadid.memolabs.net/home

Install the DataDID browser extension and connect your wallet for the first time — instantly receive 1 exclusive Mystery Box draw.

Special bonus 🎁: The first 10,000 users to install the plugin and connect their wallet receive an extra 200 DataDID points on the spot.

👉https://chromewebstore.google.com/detail/datadid/mklejljmlgjnknaodkikbmcbpbmabdfo?hl=zh-CN&utm_source=ext_sidebar

Step 3: Share your exclusive referral code

This is the most exciting mechanic of the campaign.

Every time a new user installs the plugin and binds your referral code, you earn 1 additional Mystery Box draw — with no cap. In theory, the wider your network, the more draws you accumulate, and the higher your odds of winning a big prize.

In Web3, action is your competitive edge.

Your DID identity is waiting to be activated. Your Mystery Box is waiting to be opened.

III. Full Prize Breakdown: From 100 Points to 300 USDT

The Mystery Box prize pool is divided into two categories: USDT cash rewards and DataDID points rewards — 100% win rate guaranteed.

USDT Cash Rewards:

● 1st Prize: 300 USDT — 1 winner

● 2nd Prize: 200 USDT — 2 winners

● 3rd Prize: 100 USDT — 3 winners

● Lucky Prize: 20 USDT — 50 winners

Points Rewards:

● 1st Prize: 1,000 points — 300 winners

● 2nd Prize: 500 points — 3,000 winners

● 3rd Prize: 200 points — 60,000 winners

● Lucky Prize: 100 points — unlimited winners

Even if you haven't referred anyone, simply log in and stay active daily — 100 points are waiting for you.

IV. Points Are More Than Points — They're Tied to the $MEMO

There's one thing worth emphasizing: the DataDID points you earn are far more than just a number.

MEMO is building an AI-powered decentralized data network with data sovereignty at its core, and points are the engine of value flow across the entire ecosystem. The DataDID points system serves as the recognition mechanism for early ecosystem contributors, and will directly influence future $MEMO airdrop allocations.

In other words: every point you earn today is a key credential for your future $MEMO airdrop.

How to Claim a Major Prize — USDT Winner Verification Process

To ensure prizes go to genuine community contributors, USDT winners must complete three verification steps:

1.  Twitter interaction: Quote-retweet the official campaign post with hashtags #DataDID #MemoLabs

2.  Screenshot proof: Attach a screenshot of your winning screen and your created DID in the tweet

3.  DM confirmation: Message @MemoLabsOrg within 7 days to confirm your prize wallet address

The process is transparent and publicly verifiable.

Join now → https://datadidapp.memolabs.net/

Campaign dates: April 24 – May 23, 2026

Follow @MemoLabs for campaign updates and winner announcements.