Everlyn AI launched its $LYN token, introducing a L1 protocol for on-chain verification and control of AI-generated video content.

ChainOpera’s $COAI surged over 2000% this week amid new listings and hype from the AMD–OpenAI chip deal.

Recall launched its $RECALL airdrop, rewarding early contributors and partners of its decentralized AI skill market.

One thing I’d pay very close attention to this coming week: OpenMind (a web3 Robotics startup) is rolling out its FABRIC identity network. You’ll be able to mint your own badge and plug into the ecosystem early. Feels like the start of something big.

$COAI has officially entered meme-pump territory.

In just one week, the token surged more than 2000%, pushing its FDV past $6.5B and making it one of the highest-valued projects in the Crypto x AI space.

The timing was surgical.

ChainOpera launched across Binance, Bybit, Bitget, Gate, and Aster just as the BNB ecosystem hit peak activity. It capitalized on that wave to cross 3M AI users and become one of the fastest-growing communities in Web3.

The token has since been smashing all-time highs for five straight days. Easily one of the wildest charts we’ve seen from a crypto × AI project in a long time.

Its success also reflects a broader rotation into AI infrastructure plays.

ChainOpera runs a full-stack AI agent network on BNB Smart Chain, linking users, models, and data streams inside a live agent economy. That narrative synced perfectly with the OpenAI–AMD partnership news, which reignited investor appetite for AI compute networks.

Our take? Much of the valuation still prices in future execution.

Most AI models still run blind to the physical world. 375.ai wants to change that by turning real-world sensors into a decentralized intelligence network.

The company just raised $10M from Delphi Ventures, Strobe Capital, and HackVC.

The funds raised will go towards building out 375ai’s deployments and its ability to capture real-world data, especially its 375edge devices.

If they pull it off, AI stops training on internet leftovers and starts learning from the world itself. It’s an ambitious bet, but someone has to wire reality.

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The AI boom might have just started cannibalizing itself.

According to Boomblerg’s Report, OpenAI and Nvidia now sit at the center of a trillion-dollar feedback loop driving the AI economy.

In just two weeks, Nvidia pledged $100B to OpenAI, which in turn committed to buying millions of Nvidia chips. Then OpenAI struck a similar multibillion-dollar deal with AMD, becoming one of its largest shareholders. Oracle joined in with a $300B infrastructure pledge, much of it powered by Nvidia hardware.

Here’s how the cycle spins: Nvidia funds the companies that buy its GPUs. Those companies resell compute. Cloud providers lease it back, often with Nvidia’s capital. Valuations rise, liquidity circulates, but its questionable how much new value actually gets created.

The network of commitments now exceeds $1T, with Nvidia at the center of almost every deal. Morningstar called it “the early breadcrumbs of a potential AI bubble.

It’s a race to lock down compute before the next shortage. But the circular financing multiplies the same dollars across balance sheets instead of generating real productivity.

As one analyst put it, “Altman has the power to crash the global economy for a decade or take us all to the promised land.”

The AI boom is very real, but some parts of it are starting to look like a house of cards.

A few weeks ago, we wrote about 404-gen, a Bittensor subnet that turns text into 3D assets. Basically, Midjourney for world-building.

It’s already live and cranking out millions of objects. And once monetization switches on, it could rocket from $0 to $500M just as fast as Midjourney did.

Should we dig deeper into other Bittensor subnets next? Tell us 👇

DEEP Robotics’ new DR02 is the first all-weather humanoid, casually tanking buckets of water like it’s in a shampoo ad.

That’s about a human’s lifetime of input, and models are learning inside the prompt itself without ever updating their weights.

Trained across continents with zero communication between nodes, it matches SOTA quality using 14× less data and 16× less compute

A 7M-parameter model trained for under $500 outperformed giants on ARC-1, ARC-2, Sudoku, and Maze tasks.

New research suggests that models could match or even outperform elite human forecasters by late 2026.