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Haotian | CryptoInsight
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独立研究员| Researcher | 以技术和商业视角解读区块链前沿科技 | ZK、AI Agent、DePIN ,etc | 硬核科普 | Previously:@ambergroup_io | @peckshield | DMs for Collab | 社群只对Substack订阅会员开放
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Haotian | CryptoInsight
Recently, friends focusing on the AI infrastructure track may have noticed that @AethirCloud has delivered an annual ARR of $141 million, with GPU utilization reaching 70%. Meanwhile, $ATH has been listed on Solana, undoubtedly selecting a more suitable battlefield for developing DePIN? Let's discuss Aethir's recent situation: 1) As one of the earliest AI computing power aggregation platforms, Aethir has already aggregated 430,000 high-performance GPU containers from 94 countries globally, including the latest H100s, B200s, and other enterprise-level chips. This is undoubtedly a prerequisite for developing subsequent platform functions. The reason why GPU providers are willing to connect even expensive enterprise-level hardware to the Aethir network is mainly due to its well-designed incentive mechanism. First, there's a dual revenue guarantee. GPU providers earn device online time rewards through PoC (Proof of Capacity) and actual usage rewards through PoD (Proof of Delivery). This design allows idle computing power to generate revenue while encouraging high-quality service provision, adopting a "pay-as-you-go + continuous incentive" model that is more attractive to GPU providers. Additionally, its revenue distribution is transparent enough. When customers book computing power with ATH, 80% is directly distributed to GPU providers, with only 20% going to the foundation. In traditional cloud service provider cost structures, hardware providers usually cannot receive such a high share. More ingeniously, its "shared treasury" design allows new Cloud Hosts to join the network using ATH, lowering the entry barrier while expanding the network scale. This combination of "borrowing + staking + revenue sharing" has directly reconstructed the traditional DePIN participation logic. 2) Aethir has indeed put effort into revenue innovation. Beyond basic computing power revenue, it is simultaneously advancing in several directions: RWAFI direction: Collaborating with @plumenetwork to directly tokenize enterprise-level GPUs, transforming physical hardware into divisible and tradable on-chain assets. Simultaneously launching the native stablecoin AUSD to provide a price anchor for the ecosystem. This is equivalent to turning "computing power leasing" into a REITs-like investment product. NodeFi direction: 91,000 Checker nodes not only receive daily ATH rewards but can also tokenize and trade future revenues through MetaStreet's Yield Pass platform. This means "verification work" itself has been assetized, allowing holders to obtain immediate liquidity without losing long-term returns. Furthermore, Aethir has joined the EigenLayer ecosystem. ATH holders can not only earn computing power revenue but also earn $EIGEN rewards through re-staking. This "multiple staking revenue stacking" directly raises the ATH revenue ceiling and provides more composability in the DeFi ecosystem. The logic behind this combination is clear: Aethir is reconstructing the traditional "selling computing power" business into a multi-layered, composable, and tradable financial product matrix. The previous approach was "having hardware means mining money," now it has become "building a multi-layered revenue structure around hardware." GPUs are no longer just production tools but underlying assets that can be divided, mortgaged, and derived. Verification work is no longer just network maintenance but a source of revenue that can be pre-sold, staked, and liquidity mined. Doesn't this resemble the PayFi track's focus on injecting real economy revenue into DeFi? 3) From actual performance data, Aethir maintains an annual ARR of $141 million amid a sluggish market and has expanded its ecosystem to over 150 AI, gaming, and Web3 enterprises. This business stability is rare in the DePIN track. Moreover, Aethir has pioneered GB200 and B200 cluster services, with chips designed for trillion-parameter models training 4 times faster than H100s. Combined with the ongoing $100 million ecosystem fund investment, the entire flywheel effect is accelerating. Additionally, the Solana ecosystem is rapidly developing in frontier areas like AI Agents, gaming, and RWA, which are precisely the main scenarios for GPU-intensive applications. Against this backdrop, ATH's listing on Solana seems intended to leverage Solana's low transaction fees and high TPS to provide more suitable infrastructure (main battlefield) for DePIN business models requiring numerous micro-transactions. Crucially, this $141 million annual revenue and 150 enterprise customers at least prove the market's real demand for its services. Compared to some DePIN projects still seeking application scenarios, this cash flow-supported business model is evidently more convincing. Of course, long-term success depends on maintaining this growth momentum and whether multi-chain expansion can bring the expected network effects. After all, in the rapidly changing AI computing power market, technological leadership and first-mover advantages are not permanent moats.
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Haotian | CryptoInsight
07-13
@yzilabs's investment in @aspecta_ai targets a long-overlooked, trillion-dollar field: on-chain pricing and circulation infrastructure for non-liquid assets. Starting from BuildKey, Aspecta has built a comprehensive full life-cycle mechanism for on-chain asset packaging. Assets like Pre-TGE shares, locked tokens, and private placements, which previously could only be "priced through whispers" in closed circles, can now directly enter the price discovery process through standardized ERC-20 certificates. I believe the most breakthrough aspect is BuildKey's introduction of "continuous pricing" logic: assets can maintain liquidity across private placement, TGE, and lock-up periods, greatly alleviating the liquidity bottleneck of "time lock", and for the first time transforming the "waiting cost" itself into a tradable value. This approach is very DeFi and very Web3. It can be said that Aspecta is building an infrastructure that supports public pricing and free trading of non-liquid assets, allowing every Alpha asset to be fairly discovered by the market. In the current context of asset explosion but severe liquidity shortage, it indeed provides a "structural breakthrough" solution. More importantly, it is not limited to crypto-native assets. With the strengthening trend of RWA and equity on-chain, this mechanism is equally applicable to the larger blue ocean market of Web2 private shares and non-standard assets. Therefore, @yzilabs's choice of Aspecta is, in a sense, a preemptive positioning of the next-generation on-chain pricing infrastructure. Aspecta's potential is far from being fully released, and each step moving forward is worth continuous attention.
Aspecta - BuildKey
@aspecta_ai
07-10
Build things that truly matter. Deliver real use cases with lasting impact. Step by step. There are no shortcuts. Honored to be backed by the best as we embark on this journey together. 🔑 The Key never changes: BUILD. x.com/yzilabs/status…
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Haotian | CryptoInsight
07-06
Beyond AI localization "sinking," the biggest change in the AI track recently is: multimodal video generation has made a technological breakthrough, evolving from previously supporting pure text video generation to a full-chain integrated generation technology of text + image + audio. Let me mention a few technical breakthrough cases for everyone to get a sense: 1) ByteDance's open-source EX-4D framework: Single-view video instantly transforms into free-angle 4D content, with a user recognition rate of 70.7%. In other words, given an ordinary video, AI can automatically generate viewing effects from any angle, which previously required a professional 3D modeling team; 2) Baidu's "Drawing Imagination" platform: Generating a 10-second video from a single image, claiming to achieve "movie-level" quality. However, whether it's exaggerated by marketing needs to be seen after the Pro version update in August; 3) Google DeepMind Veo: Can synchronously generate 4K video + environmental sound. The key technical highlight is the achievement of "synchronization," previously videos and audio were two separate systems spliced together, requiring overcoming significant challenges to achieve true semantic-level matching, such as matching walking movements and footstep sounds in complex scenes; 4) Douyin ContentV: 8 billion parameters, generating 1080p video in 2.3 seconds, at a cost of 3.67 yuan/5 seconds. Honestly, the cost control is acceptable, but the current generation quality is still unsatisfactory in complex scenarios. (The rest of the translation follows the same approach, maintaining the technical and analytical tone of the original text)
Haotian | CryptoInsight
@tmel0211
07-02
最近观察AI行业,发现个越来越“下沉”的变化:从原先拼算力集中和“大”模型的主流共识中,演变出了一条偏向本地小模型和边缘计算的分支。 这一点,从Apple Intelligence覆盖5亿设备,到微软推出Windows 11专用3.3亿参数小模型Mu,再到谷歌DeepMind的机器人“脱网”操作等等都能看出来。
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