Powering the Future of AI. Building Decentralised Cloud Compute Infrastructure. https://teapawt.net/t.co/0q8024pYbg https://teapawt.net/t.co/9TF9m3zDSE
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Major ATH tokenomics update: we’re introducing daily ATH burns, funded by a share of network fees.
Every burn goes to a public burn address, where tokens are permanently removed from supply. The mechanism goes live in November.
We’re also replacing the fixed platform fee with a floating network fee, set by a published formula that reads market conditions. When the market weakens, the fee falls and the share that gets burned rises.
Cloud Hosts are protected by design: the fee can never exceed the value of Cloud Host rewards.
The underlying host emissions schedule remains unchanged. The update changes the fee mechanism applied around those rewards.
A dashboard tracking daily, monthly and cumulative ATH burned will be available in November.
Burns that follow real network activity, and you can check every one of them.
Read the full announcement below👇
Swarm riders using 1,000x the tokens of a median engineer is a compute story as much as a talent story.
Every agent in a swarm needs GPUs
teapawt.net/matthuang/status/21202…
Swarm riders and Swarm-native companies
AI-use even among highly AI-native people is power-law distributed.
There seems to be a new class of people — call them “swarm riders” — who are capable of consistently commanding large swarms of agents to productive ends.
This is a new kind of managerial skill, analogous to the skill it takes to manage large organizations of people, but wholly different in nature and of course more technical.
Based on the swarm riders we know (including many at @Tempo and @Paradigm), these people are capable of productive token spend that is 1000x-10000x higher than median software engineers, and 100x higher than top 5% Claude Code users.
The art of swarm riding is different from software engineering and more akin to machine learning, where practitioners grapple with black box systems: defining reward functions and evals, transmuting non-verifiable problems into verifiable problems, running experiments, etc.
Swarm-native companies are a new class of company founded by and populated by swarm riders. These companies are organized around swarms in a way that conventional companies will struggle to replicate, as the cost of internal human coordination is sand in the gears of an effective swarm. Effective swarm-native companies will define the right reward functions and evals, remove functional and bureaucratic barriers, and then let the swarms cook.
We’ve gotten to see a few swarm-native companies up close — reshaping financial rails @Tempo, building a new gaming platform @Spawn, cyber swarms at Cathedral Systems — and it’s clear these teams wield a significant advantage over pre-AI teams.
Some questions I’ve been wondering about:
1) How fast will swarm riding diffuse through the economy? The implications for token spend could be significant.
2) Can swarms be effectively adopted by existing companies or will it generally track the formation of new swarm-native companies?
3) What becomes scarce as swarm capabilities themselves commoditize (through frontier models becoming better at swarming, swarm capabilities built into harnesses, etc)? Cultures and teams that can define reward functions well and otherwise get out of the way?
Keep an eye out on @AxeCompute's Q3 financial results next year to learn about the company's latest achievements at the forefront of AI compute innovation.
Axe Compute (NASDAQ: $AGPU) will host its Q3 2026 Financial Results Conference Call on November 17 at 8:30 AM ET.
Join our leadership team for financial results and business updates.
Dial-in: +1 301 715 8592
ID: 88188227988
Passcode: 457947
Aethir retweeted
McKinsey projects AI data center power demand growing from 44 gigawatts in 2025 to 156 gigawatts by 2030.
BloombergNEF’s tally of capacity under construction worldwide stood at 23.1 gigawatts, and up to half of what’s scheduled for 2026 faces delays.
Multi-year campus builds can’t close that gap on schedule.
Aethir ACCELERATE is built for the timeline.
Its upcoming facilities are engineered for NVIDIA B300 and GB300 clusters and designed to go from site selection to live capacity in months, with a live global network serving customers in the meantime.
Speed to capacity is now part of the product.
Cumulatively, 4,364,413 ATH has been burned so far, with 629,516 ATH in the last 24 hours, and everyone can now check it.
We've done daily burns, and those burns offset 18% of total emissions over the past week and support the long-term stability of the ATH network.
All these ATH burns contribute to 25% of the protocol fees burned.
Each burn is on-chain and visible on the Burn Dashboard, with daily, monthly, and cumulative totals. The mechanism was audited by CertiK.
Real compute demand, written straight into the token supply.
The Aethir tokenomics update is live: the ATH burn dashboard is live, along with the first daily burns.
Each burn is funded by a share of the network fees customers pay to book compute.
In weak markets, the floating network fee falls and the burned share rises. It can never exceed Cloud Host rewards.
Every completed burn is publicly verifiable: daily, monthly, and cumulative totals plus full transaction history.
The full announcement, burn dashboard, and burn contract are below👇
The Anthropic S-1 is the first filing to price what it costs an AI lab to outrun its compute plan.
Q3: $5B of short-term compute, bought above multi-year rates. Compute rose to 58% of revenue, and the quarter swung to a $567M operating loss.
Labs book compute 12-18 months ahead. When revenue grows ~47% in one quarter, the gap gets filled at short-term prices.
Aethir runs 430K+ GPU containers in 94 countries, with 48-hour provisioning for selected GPU types.
ACCELERATE plans to build mid-sized data center capacity against signed demand.
Read our new blog to learn all the details👇
Chris Miglino, @AxeCompute's CEO, gave @Benzinga an exclusive interview on the outlook for AI infrastructure investment.
Axe Compute ($AGPU) CEO Chris Miglino spoke exclusively with @Benzinga on why AI infrastructure investment remains strong despite political shifts.
“I remain extremely bullish on AI infrastructure spending, regardless of the election outcome.”
Full article: benzinga.com/markets/tech/26…
Aethir retweeted
A GPU-hour is now a major industrial input, yet the market still has no reference price.
Buyers instead get vendor list prices that move without warning.
A single vendor’s price list reflects one company’s margin target, not a functioning market.
Aethir’s decentralized GPU cloud aggregates capacity across many independent operators, which is what real price discovery requires: multiple sellers, visible utilization, and a rate shaped by actual supply.
Aethir's co-founder, @MRRydon, dived into the future of compute demand and the growing need for accessible AI inference.
A few things from the Claude Fable 5.2 launch that tell you where compute demand is heading.
1/ One Anthropic engineer gave it an empty folder and asked for an operating system. It coordinated over 1,000 subagents working around the clock for eight days, and the OS boots on a laptop.
2/ A team of 1,000 Fable agents formalised Fermat's Last Theorem in 30 hours. Anthropic's first attempt took 11 days.
3/ Over 90% of the changes merged into Anthropic's core codebase are now written by Claude.
4/ Fast mode runs up to 2.5x faster at double the token price. Speed is now something teams pay extra for.
The unit of work is turning into a thousand agents running for days, and every one of them is burning inference the whole time.
Doesn't look like demand for compute is slowing down anytime soon. I think most people still underestimate how much inference this kind of work is going to need
Aethir retweeted
An enterprise GPU fleet that sits idle between internal jobs is capital doing nothing.
Selling that capacity alone, to whatever buyer happens to show up, rarely covers the cost of finding one.
Aethir aggregates enterprise demand across 200+ locations in 94 countries into one pool, backed by 2+ billion compute hours delivered.
A Cloud Host joining that pool trades a scattered, occasional buyer for standing access to demand it could never reach alone.
Aggregation turns downtime into a revenue line instead of a sunk cost.
CoreWeave reported about 1.5 GW of active power at the end of Q2 2026, compared with roughly 3.7 GW contracted.
That is one of the strongest operators in the sector, and more than half of its contracted power was still waiting to run.
Across AI infrastructure, the headline number and the rentable number keep drifting apart, and buyers end up planning against the wrong one.
Aethir ACCELERATE is measured on capacity that goes live: We plan to deploy mid-sized sites designed to move from site selection to running B300 and GB300 clusters in months.
Count what runs.
A credit squeeze could make GPU access more expensive while making it harder to bring new GPU online
@SemiAnalysis_ sees tighter financing supporting rents. If that holds, existing players gain pricing power while new sites struggle to fund
Cast AI: enterprise Kubernetes clusters average 5% GPU utilization.
The Meta Research SuperCluster runs at 83- 85%.
That spread is stranded GPU time, and depreciation is completely indifferent to whether a chip is busy.
Aethir’s GPU network runs at a constant 95%+ utilization rate.
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The ramp is shortest when workloads wait at go-live with demand already present.
That sequencing decision is made long before commissioning, and Aethir ACCELERATE plans to build out data centers exclusively when demand is present, instead of searching for clients after the buildout is finished.
Full breakdown: aethir.com/blog-posts/from-g…
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