AxonDAO

OFFICIAL $AXGT TOKEN CLAIM UPDATE Hey Axonians, Quick update on the $AXGT claim. We’ve further simplified the claim process. There is now one all-in-one claim dashboard that works for all chains (ETH, BASE, and ARB) No matter which chain you originally held your $AXGT on, you can now claim using a single link: 🔗 massdrop.multisender.app/airdrop/or2l8d… Previous $AXGT contracts: 🔹Ethereum: 0xDd66781D0E9a08D4FBb5eC7BAc80B691BE27F21D 🔹Base: 0x9B700B043e9587ddE9a0c29A9483e2F8FA450d54 🔹Arbitrum: 0xE0Ee18EacAfDDAeb38f8907C74347C44385578aB New unified $AXGT V2 contract: 🔹0x6112C3509A8a787df576028450FebB3786A2274d As always, please verify contract addresses and ensure you’re only using links shared by our official accounts. Thanks for your patience as we complete this upgrade.

Medical records are worth more on the dark web than credit card numbers. How much do you think one complete medical record sells for?

AI can accelerate science, but only if the infrastructure is already there. Compute, consented data, decentralized coordination, and research workflows need to be connected before a global threat appears. Researchers need this today, not just when a pandemic forces the question. That's what we're building.

Bringing a new medicine to market has been estimated to cost around $1.1B before approval. And the timeline can stretch beyond a decade. That is the scale of the problem. Drug discovery and development now depend on computational biology, molecular simulation, protein modelling, and AI systems that can analyze patterns far beyond what humans can process manually. But AI does not run on ambition. It needs compute. It needs infrastructure. It needs environments built for scientific workloads. That is the layer we are building around. Source: Wouters et al., JAMA, 2020. doi.org/10.1001/jama.2…

For people running ML, bioinformatics, simulation, or scientific compute workflows: What slows you down most often?

Compute should start with the workload. For research and AI teams, the first question is simple: What are you trying to run? From there, everything else should follow: the environment, the tools, the GPU access, the storage, the controls, and the workflow around it. That is the direction we are building toward with AxonOS. A more practical path from research workload to usable compute environment.

Axon Academy Episode 3 is live. DeSci can open access to more data, more research, and more collaboration. But access alone is not enough. The next step is turning that information into something researchers can actually use, and that is where AI, compute, and infrastructure become essential. AxonDAO is building for that next step: the infrastructure that helps open research become usable research.

Compute only matters if researchers can use it. They need capacity, but also a practical way to access it, run workloads, and manage experiments without fighting the stack underneath. That’s where AxonOS fits in. GPUs supply the compute. AxonOS helps make it usable.

The AXGT burn sequence did not stop at 4.5M. The original target was 4.5M AXGT across 3 burn days. With bonus burns added, the final verified total is now 5.29M AXGT removed from circulating supply, bringing the sequence to 118% completion. Day 1: 2.29M AXGT burned Day 2: 1.5M AXGT burned Day 3: 1.5M AXGT burned, bringing the final total to 5.29M This burn sits inside the wider AxonDAO execution cycle across compute demand, AI infrastructure, AxonOS, and $AXGT utility.

Day 3 burn is complete 🔥 4.5M / 4.5M AXGT has now been permanently removed from circulation. This is part of the infrastructure work we’re building around Axon’s protocol. For us, the point isn’t just the burn itself. It’s the loop behind it: - GPU demand leads to compute usage. - Compute usage supports real research. - More infrastructure enables us to expand capacity. - More capacity gives research workflows more space to run. Compute, research, infrastructure, repeat. Burn tx: 0xaa56820ac2425d05c89e1110cb34e1c8cec999c58a7306bb5a55df5434fbafd7

Burn Day 2 proof: etherscan.io/tx/0x71a61ec3c… If you are working in health AI, life sciences, computational biology, bioinformatics, or adjacent DeSci fields and want to test AxonOS Beta, apply for the private beta here: docs.google.com/forms/d/e/1FAI…

The kinds of requests we are seeing are practical: hundreds of GPU hours, dozens of training and fine-tuning runs, storage for datasets and checkpoints, isolated environments, and a Python ML stack that is ready to use. Researchers get access to compute. AxonOS gets tested against real workloads. AXGT continues to be tied to useful infrastructure.

Burn Day 2 is complete. 3M / 4.5M AXGT has now been burned and verified. But today's update is bigger than the burn itself: AxonOS Beta is now being shaped by real researchers, supported through compute grants from AxonDAO.

Burn Day 1 is complete - and we already went beyond what we promised. 1.5M AXGT has been burned. But Day 1 did not stop there: An additional 790,012 AXGT has also been burned as a bonus burn. That’s 2.29M AXGT removed on Day 1 alone. Day 2 next. Etherscan: etherscan.io/token/0x6112C3…

AxonDAO’s compute layer is already working. GPU capacity is currently running at full utilization, with demand supporting rentals above typical market pricing. The upcoming AXGT burn is happening in the context of real infrastructure activity: live compute demand, active rentals, and a clearer path toward ecosystem alignment. Compute, AI infrastructure, AxonOS, and AXGT are starting to move closer together.