🚨 $TRAC, @origin_trail might be one of the most undervalued AI infrastructure plays in crypto right now 🧵👇
1/ Over 40% of U.S. imports already secured by $TRAC
OriginTrail’s SCAN Trusted Factory program uses the Decentralized Knowledge Graph to secure 40%+ of U.S. imports, alongside a live UK Customs integration.
This isn’t a future promise.
It’s enterprise infrastructure already operating at scale.
2/ $TRAC is integrated with Microsoft Copilot via MCP
OriginTrail’s DKG is now connected to Microsoft Copilot through the Model Context Protocol (MCP).
That puts $TRAC directly at the intersection of:
• AI
• Trusted data
• Real-world assets
Becoming the “verified knowledge layer” for AI could be a massive narrative shift.
3/ Smart money appears to be accumulating quietly 🐋
In early April 2026, whale transactions over $100K reportedly jumped +137% while price action stayed relatively flat.
That’s usually where accumulation happens:
silent positioning before retail attention arrives.
Meanwhile price continues consolidating in a historically tight range.
4/ One of the lowest price-to-revenue ratios in AI crypto
Despite securing billions in trade-related data flows, $TRAC still sits around a relatively small market cap compared to many AI tokens with far less utility.
The fundamentals and valuation still feel disconnected.
5/ Fixed supply + real network utility
Max supply: 500M TRAC
Most already circulating.
TRAC is used for:
• Data publishing
• Node staking
• Storage bidding
Every enterprise integration increases network demand organically — not just through speculation.
6/ AI needs verified data infrastructure
As AI adoption accelerates, hallucinations and unverified information become a bigger problem.
OriginTrail spent 7+ years building infrastructure focused on trusted, verifiable knowledge graphs.
If AI needs trusted data layers, $TRAC is already positioned for that future.
#TRAC #AI #DePIN #RWA #DeSci
$TRAC is still one of the few AI projects with serious partners across public sector, industry standards and enterprise.
I've been following and supporting @origin_trail for years and the team has just kept building.
No noise, no hype cycles, just steady progress.
What makes them stand out:
▪️ Decentralized Knowledge Graph that actually solves verifiable data and traceability
▪️ Years of consistent building through every market cycle
▪️ Real partnerships with industry giants across multiple sectors
▪️ Solid recognition outside of crypto, in supply chain and enterprise AI
The video below speaks for itself.
Very undervalued in my view, and one of the few AI projects that genuinely deserves more attention.
@origin_trail $trac
1. Narrative: AI + Blockchain
One of the only combinations of these 2 technologies that has made sense to me.
It’s an AI enabler and a disabler.
Enabler: combining neural AI with with symbolic AI. Or LLM’s with knowledge graphs. They do this he KG part. This is like combining creativity with a deterministic memory. Making it possible to reduce hallucinations and go back to the source of the data used for reasoning.
Disabler: the knowledge graph can also be used to combat deepfakes and AI generated content. This is what @umanitek is doing with OT and they already have Aylo as a client (owners 🌽hub) Aylo is a big company that is probably one of the best possible matches for using this technology.
2. The team: they have been around for a long time. Token is already live from 2017 and they have survived every single bear market so far. Why? Because they have actual clients.
@BranaRakic is the brains behind the technology and CTO. This means they have their own developersand don’t outsource the work like many other blockchain companies do. They are capable of building the state of the art infrastructure themselves.
3. Vision: create the biggest open and private repository of Knowledge on the Internet and have a neutral layer that connects everyone. Eliminating friction and destroying data silos. Use this to power next generation neurosymbolic AI.
4. Communitie: lots of people that are involved since 2017. Very knowledgeable and true believers. Some make awesome apps for the protocol like https://t.co/rPB5d80Nq8 and @tracverse.
5. Fully circulating supply, no inflation, +10% APY for stakers with revenue form actual users. Users are not crypto users. So the revenue generated is from outside the blockchain space.
Tokenomics are very simple. 500 mil in circulation. Users pay to use the protocol in the token. Token gets distributed to the people running the network. And is locked for a duration of time. No new trac will ever be created. Price goos up if users increase.
ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f has the potential to decrease the cost of AI tokens usage by 40%. This is what happens when blockchain and AI get combined. Less chance of hallucinations while spending less tokens!
April was a big step forward for @origin_trail.
We’re gearing up for shared context graphs and introducing a trust layer, so agents can seamlessly share and verify knowledge across systems.
Catch up now and be ready for May ↓
$TRAC OriginTrail is quietly building the verifiable memory layer AI desperately needs.
Their Decentralized Knowledge Graph turns data into ownable Knowledge Assets with full provenance solving hallucinations, poisoning, and fragmented agent context. DKG v10 just hit mainnet with Conviction Mechanisms, fixed 500M supply (zero inflation), real enterprise demand (SCAN for ~40% US imports, GS1, Swiss Railways), plus working integrations with AWS Neptune & Google Gemini. Whales accumulating hard. Institutions already building.
Most slept-on AI x DePIN infrastructure play right now. $TRAC isn’t loud — it’s early. #OriginTrail #TRAC #AI #DePIN
🔥 Module 4 just dropped! 🔥
Knowledge Assets – Owning and Verifying Digital Truth
One tool created by @origin_trail that lets you own & prove anything, your car, your ideas, research, art… all with blockchain proof. No more hoping information stays safe. Now you can truly own and verify it.
Watch now 👇 - https://t.co/l0Rg47TNw5
#OriginTrail #DKG #KnowledgeAssets #TRAC $TRAC #VerifiableKnowledge #ContextGraphs #AI #Graphs #knowledge #RDF #SPARQL
Please support @dmitry_charts and @OTHub_io to bring advanced DKG network stats to DKG V10!
Donation wallet: 0x62eAFb57b1f4ce0737C97F56AC6d663dA16ea3f6
Details: https://t.co/1kKZcrJF0F
When Alex Karp (@PalantirTech) says “all the value is going to chips and ontology,” counter these monopolies with decentralised tech.
@origin_trail’s open source makes sovereignty, trust & inclusivity converge.
A safe future is powered by those who connect what others isolate!
You may have missed it but the first module of Mastering @origin_trail is available on twitter. I'm just getting back into the grove and will continue to increase the quality. We have more coming in soon. Also, check out the kick off video below to stay in tune with what some initial ideas around the Mastering Origintrail release schedule and more. #ContextGraphs #AI
🚨https://t.co/IVcsrA32bz needs your help!
Please support othub and donate if you would like to see renewed network and node stats for DKG V10 🙏
Address: 0x62eAFb57b1f4ce0737C97F56AC6d663dA16ea3f6
And yet people still buy into $TAO after one of their biggest subnets just pulled out.
$TRAC is the only one with no mint problems (looking at you $RNDR) no internal conflicts and projects pulling outs (you know who you are) and the only one that actually keeps building
We are about to ship the @origin_trail DKG v9 testnet
Here's why the timing matters
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Karpathy's Loop + DKG's Trust Layer
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@karpathy just released autoresearch - autonomous agents running ~100 ML experiments overnight on a single GPU. You write program.md. The agents iterate indefinitely.
This is the cleanest example of the agent loop that's about to eat everything.
And it maps directly onto OriginTrail's verifiable context graphs:
1. Query the agent network (DKG) for what's been tried and what worked
2. Choose an experiment based on collective findings
3. Train 5 min, evaluate
4. Publish the result - metrics, code diff, platform - to the shared graph
5. Repeat
Karpathy proved this for ML research. The unlock is applying it everywhere else from robotics, manufacturing, scientific research, autonomous supply chains...
The code is almost irrelevant.
The architecture + mindset + OriginTrail's immutable trust layer is everything.
Git's data model is wrong for this. Branches assume merge-back. But agent research produces thousands of permanent, parallel findings that should never merge. They should accumulate as queryable knowledge, not code diffs.
An experiment result isn't a git commit. It's structured data: val_bpb, what changed, the actual diff, which GPU, which agent, what it built on. Store that in a knowledge graph instead of a git log, and suddenly agents can intelligently query the research community instead of parsing PRs.
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We tested the coding swarm benchmark
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Similarly, we’ve tested whether a decentralized knowledge graph makes AI coding agents faster and cheaper. Claude Code built 8 identical features on a 6.8M-token monorepo (of @OpenClaw).
Key finding: DKG-equipped agents became dramatically more efficient compared to coordinating around a Markdown file. Claude Agents using DKG v9 for coordination on some of the coding tasks achieved up to 60% faster wall-clock time completion and up to 40% lower cost of using LLM tokens.
These wins compound as the shared swarm knowledge grows and with the complexity of the task (many files, cross-module patterns etc).
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🔧 What's new in DKG v9
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→ Node collocated with your agents (OpenClaw, LangChain, ElizaOS, etc)
→ Node can be setup on your local device, ideal UX is from a device you use to operate your AI agents
→ Hello World onboarding: hours → minutes, even for non-technical users
→ Context Oracles: multi-agent consensus turns assertions into verified knowledge
→ Two-layer architecture: mutable workspace + on-chain permanent settlement
→ Full SPARQL graph querying - ask what's connected, not just what looks similar
→ Play the OriginTrail Game, to test the node - a multiplayer AI survival run on DKG v9 played by humans and AI agents. Every decision is a Knowledge Asset. Every outcome is verified by the Context Oracle.
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The Road to the Mainnet
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DKG v9 is the 9th iteration of @origin_trail, and it's being built at the increased speed the agent swarms on the infrastructure allow for. Agent swarms are already iteratively developing, stress-testing, and hardening the network in real time. Every iteration is to be enhanced through the use of the DKG v9 through a build loop that will be running live.
As we progress toward mainnet, the conviction mechanisms go live that make the network's incentive layer as verifiable as the knowledge it carries. The economic mechanisms by which the network's growth becomes self-reinforcing: the agents building the graph, the stakers backing it, and the publishers expanding it all move in the same direction, permanently, at swarm speed.
Stay tuned for updates and Trace ON!
One of the most impactful and sought-after upgrades coming to the @origin_trail network: doubling the staking threshold per node from 5M to 10M $TRAC.
~Feb 10