@BlocksmithLabs @ionet Lead Brand Ambassador AstroGills , a #SOL bozo for eternity
Bali
Joined February 2022
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One of the best formulas for winning in Web3 is finding projects that walk the walk, not just talk the talk.
Let me give you a hint: it starts with io and ends with net. @ionet
1️⃣ Upbit listing ($IO/KRW) has opened the door to one of the fastest growing Web3 communities in the world, Korea
2️⃣ While $BTC has been under pressure, $IO continues to show strength, climbing from its ATL around $0.07 and now trading near $0.18 in a new price discovery range.
3️⃣ The AI narrative isn't going anywhere. As GPU demand keeps rising, decentralized compute becomes increasingly valuable, offering infrastructure thats up to 75% cheaper, faster, and more scalable than traditional cloud providers.
4️⃣ The long-awaited IDE is finally live. 50% of io.net's net revenue (after supplier payouts) will be used to buy back and burn $IO. Over the next year alone, an estimated 12M tokens could be burned, roughly 1.5% of total supply. More usage = more scarcity.
5️⃣ IDE also protects GPU suppliers. Regardless of crypto market volatility, suppliers receive predictable USD denominated rewards, creating a sustainable ecosystem for long-term growth.
6️⃣ io.net recently secured an $8M enterprise contract. Bridging Web2 and Web3 is incredibly difficult, yet io.net is doing exactly that. This single deal alone generates approximately $650K in monthly revenue.
7️⃣ io.net is already processing up to 4 billion AI tokens daily through OpenRouter, proving it can compete alongside major cloud infrastructure providers.
Let's be real, most people would rather gamble on memecoins because they promise overnight riches. But what many forget is that they can make you poor just as quickly.
I'd rather invest in projects with real utility, real adoption, and real revenue. Price action may take longer, but the upside from sustained growth can be far greater.
And here's the crazy part:
$IO is sitting at roughly a $62M market cap.
Just $62M.
Use that information however you want, but the opportunity here is hard to ignore.
I'll come back to this post next bull run.
I'm Astro. Ciao. #solana #depin #ai
The future of AI compute doesn't run on hype.
It runs on real utility, real burns, and a network that gets stronger every time it's used.
The Incentive Dynamic Engine (IDE) is now live!
io.net/blog/a-new-tokenomics…
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@Ptahtrade
From ptah.trade
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io.net has been on Solana since day one.
They just focused on the actual product: Cheap GPU compute for AI.
Compared to others DePIN on Solana io.net generally leads in terms of compute side. Currently #1 in Solana DePIN revenue.
They offer more than just GPU rentals
• IO Cloud → rent GPU clusters
• IO Intelligence → pay-per-use access to open models
• Agent cloud for AI agents
The demand is real it’s not just a narrative!
Helium moved. Render moved. Akash is voting on it, 98% yes with the vote still open.
Different networks, same conclusion. Build the product, not the chain.
@solana is where DePIN goes to scale.
We’ve been here since day one.
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Centralized data centers have real structural limits power, utilization, and access.
Power is the actual bottleneck. You can buy more chips and lock in customers, but you can’t force a grid connection to happen faster.
Utilization matters more than most people admit. Having capacity doesnt mean much if demand and supply arent matched properly.
The GPU “shortage” is mostly just a queue. The biggest buyers already skipped the line. Everyone else gets the leftovers, higher prices, and long leases.
Thats why distributed networks like @ionet are interesting they are built to work around these limits by using existing supply instead of waiting years for new capacity to come online.
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In the current competition, the edge isn’t just cheaper GPUs or bigger clusters. It’s matching flexible demand with existing capacity without locking customers into contracts that outlast their product roadmap.
Providers that force rigid terms create friction. Providers that can keep existing hardware busy while staying flexible on the demand side have a practical advantage over both capital heavy hyperscalers building from scratch and traditional suppliers stuck in landlord style contracts.
The teams that solve all three problems access, payment flexibility, and utilization without overcomplicating the demand side are the ones that stay relevant.
@ionet
I have had a front row seat to this market at @ionet since 2024.
One thing a GPU price index cannot tell you is whether a funded startup, cash in hand, will get told no. Or asked for 30-40% upfront and a guarantor (preferably the lead VC or top client).
Compute increasingly behaves like a landlord business. Parts of AI landscape still behave like a VC growth bet. They do not want the same things.
The compute supplier has financed hardware and needs a tenant who keeps paying. A fresh funding round of the customer helps, but it does not guarantee that the customer will survive the contract period.
The AI players want adoption measured typically by subs and LTV on retained users. A new model or agent workflow brings traffic. Their customers experiment, change direction and switch providers. A feature that attracts users this month can become a basic expectation next month.
The capacity commitment is much harder to change.
That leaves the AI solution provider in the middle committing cash upfront while selling to their customers the freedom to leave.
Token volume can easily grow with more compute while more cash gets locked into supplier advances. Engineering improves efficiency, competition pushes prices down, and the customer gets much of the saving. The AI solution provider still carries the risk of empty GPU hours.
Two teams can run the same hardware and serving stack yet have very different economics because one gets better payment terms and has customers who commit.
That is where I expect the market to move: committed enterprise demand alongside flexible API traffic, more minimum spend agreements and a premium for guaranteed capacity.
Turning recurring usage into contracts that suppliers can finance could become a meaningful cost advantage.
It also adds another dimension to the @silicondata charts. The hourly rate needs to be read alongside the advance, guarantees and buyer approval requirements. A higher rate paid monthly can be easier to fund than a lower rate that ties up the cash needed to grow.
Getting the GPUs, paying for them and keeping them busy are three different problems. An inference business has to solve all three.
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Just whacked Pip and won a drop on $INJ 🌱
Courtesy of the @injective Trench Treasury.
Think you're in? Find out with @sproutsomefun.
$SPROUT
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two gpu networks. same job. completely different price tags.
that’s roughly 13x more revenue per dollar of valuation. same sector. both actually earning. one of them is just priced like the business barely exists.
that model (IDE) is the part most people skip. after suppliers get paid, at least half of the leftover network revenue in @ionet gets burned. usage goes up, supply comes down. not a promise in a deck. it’s already running, and they already burned tokens off the first months of it. they also closed an $8M enterprise contract that alone is roughly $650k a month in network earnings.
none of this means unlocks don’t matter or that GPU depin is easy. supply still has to clear, and hyperscalers aren’t going anywhere. but if you’re pricing these things on actual earnings instead of the 2024 narrative, $IO is the one that looks mispriced. the receipts are already on-chain. the multiple just hasn’t caught up.
Two GPU compute DePINs
Same product, Opposite shapes 🔽
⚪ Revenue, 365 days
→ $IO : $8.73M
→ $AKT : $2.01M
⚪ Revenue per $1M mcap
→ IO : $123K
→ AKT : $9.0K
⚪ Volume as % of mcap
→ IO : 31.1%
→ AKT : 3.39%
⚪ Supply issued
→ AKT : 100%
→ IO : 49.9%
⚪ Price vs all-time high
→ AKT : 9.38%
→ IO : 2.71%
⚪ CertiK Skynet
→ IO : 86.62
→ AKT : 84.21
Two live businesses in a sector full of ideas.
Both earning, both building.
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Open models are finally becoming capable enough to build around, and cheap enough to serve at scale. Getting both true at the same time is the engineering problem.
@ionet
hit 15B tokens a day is not just growth.
It’s proof that decentralized inference can compete where usage is real.
AstroG1089 retweeted
500M tokens a day in March 2026.
15B tokens a day yesterday.
That is daily volume across @ionet hosted models on @OpenRouter, served on an almost same mix of H100, H200 and B300 GPUs since Day 1.
Along the way, we became day zero launch partners for open source reasoning models including @Zai_org, @Kimi_Moonshot, @Alibaba_Qwen , Nemotron, @MiniMax_AI and GPT OSS.
Nvidia has built something useful with the recent Nemotron 3.5 Lightning. A fast, capable and efficient open model, with weights, datasets and recipes that give teams like ours room to optimise.
Yesterday, the OpenRouter provider token share showed @ionet at 64% token share for Nemotron 3.5 Lightning, the largest provider share.
Effective prices per million tokens:
Input: $0.0335
Output: $0.179
Our volumes and price reflect both usage and affordability. We are serving this workload profitably on an inference stack built and operated end to end in house.
Our small team still runs two to three experiments every week. We track tool calling changes, frontier lab updates and what communities on vLLM and Sglang are discussing, then test what matters on our own workloads.
Demand changes with time zones and days of the week. Launch excitement fades. New providers arrive and price wars start. You get used to revisiting your assumptions.
With GPU rental costs rising, compute efficiency and reliable tool calling matter more. A low token price only helps if the task gets done correctly.
Open models are becoming capable enough to build around and economical enough to serve at scale. There is a lot of engineering behind making both true at once.
Thank you @NVIDIA and the Nemotron team, @OpenRouter and @alexatallah for allowing providers like ourselves to work seamlessly, and our engineers, who know how much work sits behind a neat row in a pricing table.
Everyone talks about AI’s energy problem like it has one answer: build more data centers.
@ionet is arguing the other one. The second answer barely gets airtime: use GPUs that already exist.
AI is not only short on infrastructure. It is short on access to infrastructure that is already plugged in.
New build still matters for large scale training. But most enterprise jobs by count are inference, fine tuning, and batch. For those, using capacity that already exists is faster, cheaper, and cleaner at the margin.
That is the DePIN point that usually gets overlooked. Not the romance of decentralization. Matching real demand to GPUs that are already on.
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AstroG1089 retweeted
New data centers take 18-36 months and hundreds of millions in capex.
Idle GPUs take minutes.
They're sitting in enterprise clusters and data centers right now, already powered, and already depreciating.
AI's power problem has two answers. Most of the industry is sprinting toward one.
@ionet runs on the other.
AstroG1089 retweeted
Generative AI hit 53% adoption in three years.
Faster than the PC.
Faster than the internet.
But there's an access problem.
The US hosts 5,427 data centers. More than 10x any other country.
Demand is global.
The supply is concentrated.
io.net exists to change that.
Source: Stanford HAI, 2026 AI Index
AstroG1089 retweeted
We were just named one of the Top 5 Decentralized AI Compute Networks to Watch in 2026 by @cryptodailyuk.
Here's the scale behind that. Thousands of GPUs across 130+ countries, 32M compute hours delivered, and nearly $27M in total network earnings.
The future of AI compute is being built on @ionet.
cryptodaily.co.uk/2026/08/to…
AstroG1089 retweeted
AI's future shouldn't hinge on two companies.
But right now it does.
70% of AI revenue and 25-35% of cloud revenue for the four biggest tech companies ride on the same bet on @AnthropicAI and @OpenAI .
But as models multiply and open-source competition accelerates, that bet is looking riskier by the day.
What the market needs now is compute that's flexible, open, and globally distributed, not concentrated.
That's exactly what @ionet is building.
qz.com/steve-eisman-ai-boom-…
