Powering the Future of AI. Building Decentralised Cloud Compute Infrastructure. https://xitter.twitterapi.xyz/t.co/0q8024pYbg https://xitter.twitterapi.xyz/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
xitter.twitterapi.xyz/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
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'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
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
Hardware stocks sold off on data centre slowdown fears. Korea's chip exports tell the other story: demand is still expanding.
When the forecast swings, buyers need GPUs that are ready now. That's the gap Aethir fills.
A lot of AI hardware stocks got sold yesterday on worries about a data centre slowdown. Korea's export numbers tell a very different story.
Korea shipped $56.4B of semiconductors in October, up 259% on a year ago. That's nearly half of everything the country exported last month.
Most of it is memory, at about $50B. Computer exports, driven by AI servers and server SSDs, were up 497% to $5.85B.
Prices and volumes both stayed high as big tech keeps spending to stay ahead in AI. Total exports hit $115.3B, and Korea now expects to pass $1 trillion for the year sometime this month.
This is the closest thing we have to a live read on AI hardware demand. It lands on the 1st of every month, weeks before anyone's earnings call.
Stocks can have a rough day for all sorts of reasons. The chips are still shipping at record pace, and I don't see a slowdown anywhere in these numbers
Chris Miglino, @AxeCompute's CEO, highlighted the key elements of GPU compute deployment.
Join @AxeCompute at @SiGMAworld_ to discuss the future of AI innovation and how Axe is providing the infrastructure for AI builders worldwide.
Axe Compute is at @SiGMAworld_ 2026 in Rome.
We’re meeting with technology leaders, investors, and innovators to discuss the infrastructure required to support the next generation of AI workloads.
Attending SiGMA? Connect with the Axe Compute team.
$AGPU #AIInfrastructure #SiGMAWorld
Even the best-planned AI lab can't book all its compute 18 months out. When demand outruns the contracts, it needs GPUs that are ready now.
That's the gap Aethir fills: on-demand compute for training and inference, without the long wait.
Anthropic spent $5.0B on short-term compute in Q3 alone.
That's the line in the new S-1 I keep coming back to.
Anthropic books most of its compute on multi-year deals, 12 to 18 months before it's available. When demand runs past that plan, it buys short-term capacity, and that capacity costs significantly more.
So in the quarter revenue hit $17.0B, demand ran far enough past what they'd booked that they paid up for $5.0B of extra compute. Along with a $2.0B non-cash charge, that's what swung them to a $567M operating loss after a $974M operating profit in Q2.
And it's going to keep happening. They expect to use more short-term compute in the near term.
The bears will call this a margin problem. To me it's the clearest demand signal in the whole filing.
It also tells you where the next dollar goes. The biggest buyers of compute are already running past their long-term plans, and every provider with GPUs live today is part of the answer
Go-live is when a GPU cluster starts costing money, not making it.
A fully costed cluster breaks even somewhere between 55% and 65% once you account for three-year depreciation.
Depreciation runs from the day hardware arrives, and enterprise Kubernetes clusters average 5% GPU utilization, while Aethir’s GPU network runs at a constant 95%+ utilization rate.
Aethir ACCELERATE plans to build out data centers exclusively when demand is present, instead of searching for clients after the buildout is finished.
The ramp is the number nobody reports👇
Fitch kept SpaceX at BBB+ while modeling its compute going from 1.7 GW to 9.5 GW by 2028. Lenders and rating agencies now treat GPUs like any other equipment lease. The hard part left is getting that capacity live and into customers' hands, and that's where Aethir focuses.
One update on the SpaceX GPU lease I walked through last week.
Fitch looked at the $40B commitment and left SpaceX at BBB+, because that compute was already in its forecast. It's treating the deal like any other equipment lease.
The numbers underneath are the part that got me. SpaceX had 1.7 GW of nameplate compute at the end of September, up from 0.6 GW a year earlier. Fitch's rating case assumes an average of 5 GW next year and 9.5 GW in 2028.
When a ratings agency models a near 6x jump in compute in about two years and keeps the rating where it was, I think the question of whether AI compute is financeable is settled. The next one is how fast everyone else copies the structure
How do GPUs actually get paid for? Mostly by the customer who signs.
On recent IREN 3-year contracts, prepayments covered 45-55% of GPU capex.
Investment-grade debt at ~6% took the Microsoft deal to 96% funded, with a ~2-year payback.
The biggest lenders agree.
SpaceX just signed a ~$40B GPU finance lease with Apollo, repaid over ~5 years.
Aethir ACCELERATE plans compute buildouts where demand already exists: mid-sized B300 and GB300 sites in the US and Europe, placed where Aethir already has demand.
Read our new blog to learn more👇
