
World’s 1st Decentralised AI Ecosystem. backed by @Victusglobal.
UAE
Joined May 2024
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AI and blockchain are more connected than ever.
As AI agents evolve, they’ll need more than intelligence. They’ll need compute to operate, blockchain rails to transact, and digital assets to access programmable financial infrastructure.
Our latest research paper explores the growing connection between AI and digital assets and explains why broad AI adoption may drive new demand, utility and applications across the digital asset economy. blackrock.com/us/individual/…
Singapore, we’re coming. 🇸🇬
Excited to be joining the Founder × VC Summit in Singapore and connecting with the right minds.
THE FOUNDERS ARE COMING TO SINGAPORE
AND SO IS THE CAPITAL
We’re bringing the Founder × VC Summit to Singapore building a room where ambitious founders meet the capital shaping what’s next during @token2049 week.
Two days bringing together banks, institutions, investors, and leading founders across AI, Web3, RWA, and beyond.
October 6 and 9 | Singapore 🇸🇬
A limited number of Partner slots are still open.
If you want your brand in the room where the right conversations happen, let’s build together.
Apply now → backersstage.com/founder-x-v…
DePIN is down this year and the reason is not complicated.
Most of it was selling capacity nobody was buying. Supply first, demand someday.
Robotics teams are paying human teleoperators by the hour, today, because no alternative exists.
Demand-first network. Not the same trade.
Here is why compute is the answer, and why that is good news for you specifically.🤖
LLM training cannot run on home GPUs. Every layer talks to every other layer constantly. Without datacenter interconnect it collapses. This is why generic DePIN could never touch the highest-value AI workload.
Robot simulation is built differently. One task splits into millions of randomised practice worlds. None of them talk to each other.
Any shard. Any GPU. Any latency.
Which is how a gaming rig in singapore can help train a robot arm in Boston.
🤖 Made with AI
Everyone is collecting robot data. Nobody can turn it into skill.
A robotics team records 40 hours of demonstrations. Good data. Expensive data.
To make a robot actually perform the task, those 40 hours have to become millions of practice attempts in randomised simulated worlds.
It’s not just a data problem anymore. Robotics still needs more data, but before humanoids can learn from it, they need massive compute to turn that data into real-world intelligence.
Neurolov is the training ground.








