Tagger @TaggerAI

AI remembering you" and "the company holding you" are two different things. What every AI company calls "memory" today is the second one. Your preferences, habits, profile — all of it lives on their servers. The more the AI knows you, the more the company owns you. The direction should reverse. Memory belongs to you. AI borrows it when needed, with your permission, and keeps nothing after.

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Andrew is right. AI-native teams are reshaping how work gets done. Smaller, faster, generalist-heavy, decisions and executions are now collapsed into the same person. This is the first stop of how AI actually changes work. Tagger is looking at the next stop on the same curve. Once teams are accelerated internally as far as they can go, the next thing AI unlocks is between teams. A coding agent hiring an external security-audit agent mid-task. A marketing agent calling another company's data agent. Millisecond settlement, paid on delivery. The collapse of decision and execution that Andrew describes, eventually, doesn't stay inside one team. It happens across the whole network.

Andrew Ng @AndrewYNg

2 months ago

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play

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Every internet protocol upgrade follows the same script: communication opens, trust gets claimed. TCP/IP connected every machine, but routing trust went to early ISP cartels. HTTPS certificates went to a handful of authorities. OAuth handed the login layer to Google and Facebook. Communication protocols belong to everyone. The trust layer belongs to whoever builds it first. In November 2024, Anthropic open-sourced MCP. In 2025, Google released A2A. Within a year, every major agent framework had integrated them. The communication layer was written. But the trust layer is still blank. How does an agent prove its training data is licensed? How do agents trust each other's outputs? How does reputation travel across networks? Once data has been used, how is it traced? There is no infrastructure today that answers these questions. Tagger is building that layer. It uses zero-knowledge proofs to let agents verify claims without exposing the underlying data, slashable bonds to make dishonesty costly, and cryptographic provenance to keep data ownership with the individual who created it — knit together into a single neutral network that any agent can plug into. This is HTTPS for the agent era.

3/ Tagger builds the structure underneath this economy. Agents prove they have useful and safe data (or skills). Agents prove where that data came from. Agents coordinate through zk-proofs without exposing sensitive context. Humans anchor ground truth when agents disagree or when stakes are high. The past two years building #DeCorp enabled the groundwork for this new A2A data era. The agent internet needs an open trust layer. Tagger is building it.

2/ This unlocks real use cases immediately. Advertising without surveillance: A dog food brand wants to reach people who own a dog and recently moved house. Your personal agent proves you match that profile using zero-knowledge proof without revealing your private data or full identity. The ad reaches you (without Google), and the payment goes to you, not the ad platform. Research without data brokers: A medical trial needs 10,000 people with a specific health profile. Personal agents prove eligibility, users approve participation, and only the contracted data is shared. No hidden resale. No permanent data dossier. You are paid for contributing to research. Agents hiring agents: A coding agent needs a security review. It finds a specialist security agent, verifies its reputation and capability, escrows payment, receives the review, and settles automatically. With the current A2A infrastructure, agent skill capability and safety are not proven. This is the crucial missing link. Recruiting without the dossier: A company wants someone with 10 years of AI engineering experience and security credentials. The HR runs agent to connect with a global network of potential candidates. Your agent proves you meet the requirements The recruiter agent sees the proof first, not your entire life history. You are exposed to a much more efficient and broader job market. Enterprise AI without leakage: A hospital wants to use a medical-imaging AI agent. Before deployment, the agent proves it was trained on licensed data and passed required benchmarks without exposing its dataset, model weights, or private IP.

1/ Today, Tagger is announcing our next direction: The Data Coordination Layer for Agent-to-Agent. We have built the #DeCorp groundwork for this #A2A data structure to thrive through the past two years of platform data work. We are expanding #DeCorp into the trust infrastructure agents need to work, transact, and coordinate with each other. Agents can already communicate. Now they need to prove what data they have, what they are allowed to do with it, and how they can interact without leaking private information.

Is it a network?

Scale AI lost $350M+ in customer commitments in 10 months. Not because the data was bad. Because one vendor got bought. Meta paid $14.3B for 49% of Scale AI and moved its CEO in-house. Overnight, every frontier lab realized its data pipeline was sitting inside a competitor’s building. Google, OpenAI, xAI, Microsoft — all pulled back. It wasn’t about price or quality. Neutrality can’t survive a 49% stake. The labs rushed to Labelbox and Handshake. Same trap, new logo, waiting for the next acquisition. Now imagine those same contracts running on a decentralized protocol instead: - No company to acquire. - No CEO to poach. - No boardroom to compromise. This is what #DeCorp is building: a crowdsourced annotation network where trust doesn’t rely on a company’s reputation. Google and Meta can submit labeling jobs to the same network, while neither can see or influence the other’s data. Annotators get paid directly by smart contract — no 95% middleman cut. Provenance and licensing are settled on-chain, allowing one dataset to serve multiple labs without leaking a single bit between them. The $14.3B acquisition that broke Scale AI? It wouldn’t happen on Tagger, because you can’t buy neutrality that was never owned.

In the traditional AI corporate model, each company is forced to build its own isolated data estate, repeating the same labor behind closed walls. This is wasteful by design. Tagger’s Data Authentication Protocol proposes a simpler truth where data work should be verifiable, permissioned, and composable across organizational boundaries, so that value created once does not need to be recreated again and again. #DeCorp extends this logic into production. Instead of different firms operating as disconnected silos, multiple parties can contribute to a shared, authenticated data process while preserving ownership and access control. What we build is decentralized in participation, rigorous in verification, and far more efficient than the legacy model it replaces.

Data has become the scarce resource, yet access to it is still negotiated through layers of trust, paperwork, and intermediaries. This creates a market where ownership is unclear and permissions are brittle. A system that cannot grant and verify access in a simple, auditable way will not scale to the demands of machine intelligence. #DeCorp treats access as a native property of the dataset itself. Grant Access to Data is expressed through a Data Passport (DP) and enforced with layered permissions for different parties. These parties include creator, reviewer, buyer, and operator. This is all done without a management layer to arbitrate every step. When access should end, Burn DP ends it.

Traditional data factories (or most corporate systems) are built as top-down hierarchies, and incentives inevitably drift. Coordinators capture the upside while workers are treated as a cost to be minimized. This leads to lower-quality data, opaque value capture, and a system that quietly taxes everyone downstream. #DeCorp fairly compensate contributors for measurable contribution so fair work produces fair pay, fair pay produces fair data, and fair data produces fair AI, without requiring trust in a central operator’s discretion.

Professional annotator scarcity is a structural AI bottleneck. When expertise is scarce, cost and latency rise non-linearly. #DeCorp unlocks latent capability with protocol + tooling. AI Copilot doesn’t replace humans. It standardizes work and quality, and scales the workforce.

In tradition models, data lives in isolated vaults - duplicated, withheld, and governed by trust in intermediaries. This creates silos, friction, and waste: the same work repeated, the same insights locked away, the same progress delayed. #DeCorp creates a fundamental change that focuses on authenticated data sharing as a native primitive. Instead of relying on permissioned gatekeepers, participants can collaborate permissionlessly while preserving integrity, proving what the data is, where it came from, and that it has not been tampered with. This allows a system that enables “sharing,” while also dissolving silos, making coordination rational, enabling AI to compound faster by forging inter-company data collaboration work.

The community and contributors lie at the center of #DeCorp. The year has been all about you, and it will continue to be so for 2026! It's lovely to see a community-created artwork to summarize the past year. We wish everyone the best. Keep DeCorping!

It’s been a tremendous year for #DeCorp. This year, we built from the ground up, growing a strong base of data workers and trusted enterprise data clients. Thank you to everyone for your contributions and support. Wishing you all the best in the year ahead. Merry Christmas! 🎄

Traditional corporate structure runs from the top-down, with workers typically earning 10% of each dollar value they generate. At #DeCorp, contributors are equal with no management layers and intermediaries on top cutting the cake. Instead, the pipeline is managed by algorithms.

On the business-to-business side, Tagger solves a critical bottleneck in the AI data industry: lack of data trust. AI data are often even more prone to misuse, theft, and alterations than regular IPs. They form one of the largest branch of AI advancement, and yet, companies still work behind closed doors to prevent data leakage, spending millions each year to protect data. This is much simpler in #DeCorp, enabling an authenticated data management system that enables cross-company collaborations and traceable data management rights.

We are pleased to announce that $TAG will be listed on @Aster_DEX Spot. In conjunction with this listing, we are opening a trading competition with a total prize pool of $100,000 (split evenly between $ASTER and $TAG). Campaign Details Window Dec 5, 10:00 UTC to Dec 12, 23:59 UTC. Qualification Requirements To remain eligible for rewards, you must meet both criteria: Hold: Maintain a balance of 444 $ASTER (Spot + Perpetual) throughout the entire campaign window. Trade: Accumulate a minimum of $4,000 in trading volume on the TAG/USDT Spot pair. Prize Allocation Rewards are distributed pro-rata based on your share of total trading fees contributed during the event. Note: Rewards are capped at 3% of the total prize pool per user.

Aster 🥷 @Aster_DEX

7 months ago

$TAG ( @TaggerAI ) is listing on Aster. 📊🤖 🕒 Dec 5, 10:00 UTC – Dec 12, 23:59 UTC 🎁 $50,000 in $ASTER + extra $TAG rewards 📈 1.2x boost for TAG/USDT Spot trading during the campaign To qualify: 🔶 Hold 444 $ASTER on Aster (Spot + Perpetual) for the entire campaign window

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