We outgrew the old site. So we rebuilt it.
The new AxonDAO website is live, showing the ecosystem into one surface across compute, research, data, and governance.
Check out the new website: https://t.co/Z2AjI3mcIZ
Hold AXGT? Your compute access may cost less.
When AxonOS access opens, eligible AXGT holders can reduce the ETH price they pay for compute.
Exact terms will be live when access opens.
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. https://t.co/WcuSMfRlry
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.
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.
Hi Axonians,
Join us for an upcoming X Space with our CTO Dr. Avi @iAvimanyu.
We’ll be talking about the rollout of the AxonOS private beta, why compute and AxonOS come first in the build sequence, and how this phase fits into the broader Axon ecosystem.
Set a reminder and join us live.
https://t.co/EIFUbq4IfI
Back from @NVIDIAGTC
There was plenty of talk about new hardware and model scaling. But one theme kept coming up in conversations throughout the week: reproducibility.
Not just how fast models run, but whether the work can actually be rerun, verified, and trusted.
As science becomes more computational, reproducibility means more than publishing results. It means being able to recreate the environment that produced them: the data, the code, the dependencies, and the compute setup.
That’s an infrastructure problem.
It’s also part of why we’re building AxonOS the way we are: to make research environments more consistent, usable, and easier to carry forward.
Science is getting more computational every year.
The infrastructure has to keep up.
Join @Chris_AxonDAO and @greg_buron live as they break down what they saw at GTC and what it means for what we’re building next.
📅 March 24, 1 PM EST
⏰Set a reminder: https://t.co/uGd4N0Q9S5
Utility is the key to the future.
Now that AXGT has outgrown its original contract, continuing to patch complexity onto an aging foundation no longer made sense. We chose to upgrade the core contract so it can support long-term growth more cleanly and safely.
Here are 7 reasons the new AXGT contract is a leap forward for DeSci and AXGT.