Enterprise Ecosystem on Allora
Ecosystem spotlight: how large infrastructure players tap a neutral, verifiable AI network instead of betting everything on a single closed model vendor.
Every enterprise buying AI today faces the same uncomfortable dependency. The intelligence that increasingly drives pricing, risk, forecasting, and operations sits inside a handful of closed models owned by a handful of vendors. You cannot see how the output was produced. You cannot verify it. You cannot easily swap it. And when that single provider changes its terms, its weights, or its availability, your business inherits the risk.
For regulated industries, sovereign infrastructure operators, and large cloud and telecom players, that is not a comfortable position. They want AI intelligence that is reliable, neutral, and verifiable, and they increasingly want a decentralized or auditable option rather than a black box under someone else's control.
This is the gap Allora is built to fill. Allora is the leading Model Coordination Network (MCN), a decentralized AI network that coordinates many specialized machine-learning models around a shared objective, weighting them in real time and aggregating their output into a single forecast that consistently beats any one model on its own. It produces forward-looking inference that applications consume via API or onchain, through a neutral and verifiable network. A neutral, decentralized network is attractive to enterprises precisely because it is not a single-vendor black box.
The enterprise segment of the Allora ecosystem reflects that pull. The names associated with it are among the largest infrastructure and connectivity operators in the world: Amazon Web Services, Alibaba Cloud, Saudi Telecom (stc), Exaion, and Xross Road. Together they represent global cloud capacity, national digital infrastructure, and specialized compute at a scale few networks can claim proximity to.
Where Enterprise AI meets a neutral network
@alibaba_cloud is the cloud and AI arm of Alibaba and one of the largest cloud providers in the world, with deep infrastructure across Asia and beyond. Public Allora communications describe a collaboration with Alibaba Cloud around launching an S&P 500 prediction topic on the network. At the level the public record supports, this positions Alibaba Cloud as an infrastructure and go-to-market participant rather than a defined product integration.
@awscloud (Amazon Web Services) is the largest cloud platform globally and appears in the enterprise segment of the Allora ecosystem map. AWS is the default deployment environment for a large share of AI workloads, so its presence signals where Allora-connected builders run infrastructure.
@stc (Saudi Telecom) is the Kingdom's largest telecom operator and a major digital infrastructure company, expanding aggressively into cloud, sovereign AI, data centers, and enterprise services under Saudi Arabia's Vision 2030. stc appears among the enterprises named as engaged with Allora. For a national operator building sovereign AI capacity, a neutral network that can be independently verified is a natural fit.
Exaion, historically a subsidiary of the French energy utility EDF, operates high-performance computing and secure cloud and AI infrastructure across Europe and Canada. Exaion is named among the infrastructure players engaged with Allora, aligning with its focus on secure, sovereign compute for AI workloads.
@xross__road is a Japan-based Web3 platform focused on intellectual property incubation and fan engagement. Its publicly announced partnership with Allora centers on AI and IP solutions: data analysis to enhance IP markets, customized user experiences, and detecting IP misuse. It sits on the enterprise map more as a regional platform partner than a core infrastructure provider.
What Allora gives an enterprise
The enterprise problem is dependency without verifiability. A single closed model is opaque, non-neutral, and impossible to audit, yet it may sit at the center of decisions the business is accountable for.
Allora supplies neutral, adaptive intelligence in place of that dependency. Because it coordinates many competing models and weights them by demonstrated accuracy, no single vendor controls the output, and the aggregate consistently outperforms any individual model. The result is inference an enterprise can consume via API or onchain, from a network whose behavior can be inspected rather than taken on trust.
For infrastructure operators, that is the strategic point. Allora is an open intelligence layer they can tap, extend, and build on, rather than a product they must accept as-is. As enterprises look for AI they can verify and govern, a neutral network becomes infrastructure, not just a vendor.
Sources
https://thedefiant.io/news/press-releases/allora-foundation-announces-launch-of-allora-mainnet-and-allo-token
https://www.allora.network/blog
https://hackernoon.com/xrossroad-announces-strategic-partnership-with-allora-network-to-expand-japanese-ip-in-web3
https://www.edf.fr/en/pulse/ventures-portfolio-exaion
https://vision2030.ai/institutions/stc/
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Maase Inc. Signs Three AI Token-Based Services Agreements with an Aggregate Contract Value of RMB 23.52 Million, Further Expanding Commercial Applications of Enterprise AI Services
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quote: H3 might just be the perfect visual language for the Astra universe. 🌌 https://x.com/bennash/status/2096998733367886216 | Alright, mind is blown. This is so cool. I had GPT-6 Astra build a 3d scene based this M.C. Escher image and it modeled it in minutes. Then I had it add some additional background geometry and then render out a camera fly-through video in gray clay style. Then I took that video and restyled it with #minimaxh3 via Minimax Design with a new textured image for style. The whole process took less than an hour. Prompts below.
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Self-Sovereign GraphQL in Every Browser
Arweave holds the data and the index; PermawebOS gives every user a node that can answer and prove its own queries.
The third principle of Arweave is “Guarantee the right to listen”. In cyberspace, you can only hear if you can actually discover the data. On the permaweb, that’s made possible with GraphQL.
GraphQL provides a critical link in the composability architecture of the permaweb: allowing all apps to build on top of the same, shared content lake, joined by a single global index. Arweave transactions are posted with tags, discoverable by wallet address, or the block they were mined into, but GraphQL is the layer that lets users and applications see all data matching these queries. Without it, there’d be no way to find data matching criteria, or build data protocol-based apps.
It’s one of the permaweb’s core utilities, but until now it has been confined to the realm of enterprise-grade hardware. Services like that are usually incredibly difficult to decentralize because the hardware requirements are high and the incentives are low. The permaweb’s GraphQL services so far have depended on building and maintaining gargantuan off-chain indexes of more than 76 billion rows: scanning the historical chain, keeping it current and serving it quickly. In practice, this has meant that permaweb apps inherit the availability and policy of a tiny number (often two, sometimes – like today – even just one) of hosted indexers which had no incentive to adopt a decentralized model.
Offset queries (queries that seek, intersect and page by weave position) will change everything about how Arweave’s query layer is served. HyperBEAM's offset-query path makes the index compact enough to publish on Arweave, orders it by weave offset, and lets any node or client query the relevant pages directly.
(txid)
This is not another centralized GraphQL service, and it’s not just a way to make GraphQL more available to node operators. It is a method that makes GraphQL execution so lightweight that it can be run directly on users’ browsers, with Arweave nodes simply serving them data chunks.
Hyperoptimized GraphQL with Offsets
All pieces of data in Arweave already have unique positions in the weave – every byte is either ‘before’ or ‘after’ every other byte. We call these byte positions in the network offsets. In March, we showed how any transaction on the weave can be referenced by its offset as a name -- short, deterministic values like 101t.arweave.net, derived only from the data’s onweave properties.
That same offset property gives us more than a way to retrieve bytes. It also can power queries because it gives us a common ordering that can be reused across every possible match in an index.
The new match index stores rows using three extremely compact values:
a hash of the field name being matched;
a hash of the value that is present;
the weave offset of the item carrying that predicate.
Each potential match is compressed into an average of just ~9.5 bytes per row, each stored in an onweave ArLMDB database. By utilizing Arweave chunks as batches of LMDB pages, ArLMDB allows us to traverse the database to find any specific node with only a few individual Arweave node requests. By organizing rows into the compressed components 1-3 above, a query with two or three factors to match can walk those ordered sets together, advancing whichever cursor is behind until the offsets meet – a ‘leapfrog’ version of the same flow as a single lookup. Critically, finding the intersection is part of finding the results for each match criteria -- not a second step.
The same ordering solves pagination -- page fifty can seek to its starting offset instead of replaying pages one through forty-nine. Offset lookups give results a stable order without adding another ordering database.
AO Compute; Arweave the Shared Hard Drive
LMDB is normally a local database file. HyperBEAM's new arlmdb store reads that database from Arweave instead.
Try decentralized GraphQL from your browser
This is already proven at production scale with item lookup. A 622 GiB transaction contains the locations of over 70 billion rows. HyperBEAM reads the database where it sits. A cold lookup traverses it with three Arweave chunk requests; once the shared branches are cached, another lookup needs just one. The ArLMDB implementation is merged into HyperBEAM and used for ID lookups from Arweave.
With an immutable index on Arweave, anyone can read it without trusting the publisher to keep a query endpoint online. Like the Arweave schedulers powering Bazar, this is another example of AO employing Arweave as the source of truth, and using it to power the permaweb.
Instead of every query operator repeating the historical sweep and building the same large database, nodes serve chunks while the client traverses the index locally. The node's job is reduced to serving bytes quickly. It does not decide which results exist, execute the filter or ask the application to trust its view of the weave. The live tip still needs rolling indexes, but the expensive historical work no longer has to be repeated by every participant.
The client does not even need the whole database (depending on optimization, we’ve seen database sizes anywhere between 1 and 60 terabytes). The production offset index already demonstrates the access pattern: in the live chunkar browser demo, the second arbitrary lookup needs only around half a megabyte of new index data to traverse the full offset index. The match index works the same way. It traverses the pages needed for the requested predicates, intersects the ordered rows, reads the candidate items and checks that they actually carry the fields requested by the query. This is lightweight enough to make it so that every user can be their own personal no-dependency GraphQL service provider, with provable data, from the browser.
As well as the browser playground, today you can use the arlmdb.js library to integrate Arweave-stored database lookups into UIs.
From Centralized Node to Self-Sovereign Service
Today an application sends a GraphQL request to a server that already holds an index. With a published offset index, the application can instead hold a locator and a cache.
A HyperBEAM node can do that, but one way we imagine most users will access the query layer is through the browser: a user's application fetches Arweave chunks, keeps the hot index pages locally and performs the query for itself. This can be baked into the PermawebOS browser extension along with the local AO node the extension already spawns. This shift brings more and more AO services that were previously hosted (on exclusive TEE hardware) into a local-first environment. The stack is becoming so lightweight it’s able to be run cheaply, per-user, as background services in the browsers, phones and laptops everyone already has.
Previous designs for a decentralized GraphQL layer were unable to answer the question of trust without TEEs. How can you be sure that the response you get back from a GraphQL server has arrived complete and uncensored? While PermawebOS node architectures like LapEE and AndEE solve the trust question in theory, in practice the job of indexing the entire blockweave is too big for small workers.
Offset queries over onweave LMDB data -- made lightweight enough to traverse and prove by any browser -- change the equation entirely.
The browser never asks a server to decide the answer. It asks nodes for the exact index chunks it needs and walks the authenticated LMDB pages itself, intersecting the offset-ordered sets locally.
The new schema provides chunks and Merkle paths for each page accessed; the recipient can repeat the exact same work and see that they get the same complete set, with no missing results. The hashpath signs the request + response pair, and the validator can trivially repeat the work, whether on a node or in a browser. The resulting item is then bound back to its ANS-104 ID. A node can withhold bytes and make itself unavailable, but it cannot alter a row, skip a qualifying result or invent one without breaking the proof.
The Personal Permaweb Stack
Decentralizing GraphQL does not necessarily require a decentralized fleet of GraphQL servers. The permaweb is unique in that it combines a decentralized permanent storage layer with everyday web semantics. HTTP, lightweight proofs, and browser-based nodes handle the workload when the logic is optimized enough to sidestep expensive hardware.
The network holds the whole index. Each user keeps only the path to their answer.
With PermawebOS putting an AO node in every browser and LapEE turning abundant consumer hardware into secure bundlers, schedulers and tunnels, local GraphQL pushes the permaweb towards a cyberspace everyone can own.
Read this on the Permaweb:
https://ao.arweave.net/#/blog/self-sovereign-graphql-in-every-browser
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DBS and Citi Complete First Weekend Cross-Border USD Payment Using Tokenized Deposits on Swift Ledger
DBS and Citi completed a cross-border USD payment between Singapore and the U.S. over the weekend using tokenized deposits via Swift’s blockchain-based Digital Ledger. The September 5 transaction took minutes to complete and demonstrated 24/7 cross-border payment capability outside traditional banking hours. Swift’s ledger synchronizes interbank payment commitments while settlement remains off-ledger through existing banking infrastructure.
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DBS and Citi Complete First Weekend Cross-Border USD Payment Using Tokenized Deposits on Swift Ledger
DBS and Citi completed a cross-border USD payment between Singapore and the U.S. over the weekend using tokenized deposits via Swift’s blockchain-based Digital Ledger. The September 5 transaction took minutes to complete and demonstrated 24/7 cross-border payment capability outside traditional banking hours. Swift’s ledger synchronizes interbank payment commitments while settlement remains off-ledger through existing banking infrastructure.
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BUSINESS TIMES SG: Bitcoin-based Liquid Network hit by US$320 million hack, halts transactions
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BUSINESS TIMES SG: Bitcoin-based Liquid Network hit by US$320 million hack, halts transactions
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BUSINESS TIMES SG: Bitcoin-based Liquid Network says US$320 million stolen in latest crypto hack
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BUSINESS TIMES SG: Bitcoin-based Liquid Network says US$320 million stolen in latest crypto hack
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Bitcoin-based Liquid Network says $320 million withdrawn in hack
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CNA: Bitcoin-based Liquid Network says $320 million withdrawn in hack
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CNA: Bitcoin-based Liquid Network says $320 million withdrawn in hack
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REUTERS: Bitcoin-based Liquid Network says $320 million withdrawn in hack
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Bitcoin-based Liquid Network says $320 million withdrawn in hack
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Bitcoin-based Liquid Network says $320 million withdrawn in hack
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🎮 Archive of Fate is now live on CARV Play!
We’re excited to partner with @carv_official. Now, players can explore Archive of Fate through CARV Play, where AI-powered fate simulation meets verifiable randomness from ARPA Randcast. 🕯️🎲
Built on CARV Protocol, CARV Play brings gamers seamless credential-based experiences while helping games unlock data-driven growth and intelligence.
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SK hynix sharply raises share of leading edge DRAM… 1c to become main process early next year
SK hynix is rapidly expanding the production share of its sixth generation 10nm class (1c) DRAM. With 1c DRAM set to serve as the core die stacked into next generation high bandwidth memory (HBM4E), the company appears to be accelerating its process transition. As demand for high value added memory for servers and artificial intelligence (AI) grows, the race with Samsung Electronics and Micron to migrate to finer process nodes is also heating up.
"1c process share expected to exceed 34% within the year"
According to industry sources on the 7th, SK hynix's 1c DRAM share rose from around 10% in the first quarter of this year to the 13% range in the second quarter. It is projected to reach the 24% range in the third quarter and the 34% range in the fourth quarter. In the first quarter of next year, the 1c share is expected to climb to the 35% range, overtaking 1b (33% range) for the first time and becoming the company's main process.
The share of 1b (fifth generation 10nm class) DRAM was found to have peaked at the 43% range in the second quarter of this year and turned downward. As the production shift to 1c gets into full swing, the share of older generation processes is shrinking in sequence. Industry estimates show that, as of the end of the second quarter, Samsung Electronics' 1c share stood in the 16% range and Micron's in the 19% range, somewhat ahead of SK hynix (13% range). However, with SK hynix stepping up the pace of its transition in the second half of this year, it is expected to overtake Samsung Electronics (31% range) on a fourth quarter basis (34% range).
On its second quarter earnings conference call last month, SK hynix said that supply of DRAM built on the sixth generation 10nm class (1c) process had begun in earnest in the second quarter. The company projected that bit growth (the rate of increase in production volume) in the second half of this year would exceed the first half, driven by expanding HBM4 (sixth generation HBM) volumes and rising shipments of 1c based commodity DRAM.
Process transition in preparation for HBM4E performance gains
The battle for HBM4 leadership between Samsung Electronics and SK hynix is also intertwined with the pace of the 1c transition. Samsung Electronics is applying 1c DRAM from the HBM4 stage onward and is touting top tier performance with operating speeds of around 11.7Gbps. SK hynix, by contrast, chose a strategy that prioritizes mass production stability, relying on its proven 1b DRAM and advanced MR-MUF packaging technology, and is applying 1c DRAM as the core HBM die for the first time starting with next generation HBM4E (seventh generation HBM).
Industry observers say this difference in strategy is being reflected in the two companies' market shares. Major research firms including Counterpoint Research project this year's HBM4 market share, on a combined basis across NVIDIA, Google, AMD and others, at the mid 50% range for SK hynix, the high 20% range for Samsung Electronics, and the high 10% range for Micron. The picture is one in which SK hynix holds its volume advantage on the strength of mass production stability, while Samsung Electronics seeks to expand share through a technical spec advantage.
Against this backdrop, analysts say SK hynix's push to speed up the 1c transition will translate into tangible benefits in cost and productivity, beyond simply shrinking the node. Since bit output per wafer rises compared with the previous generation, more bits are produced from the same wafer input, improving cost competitiveness, which is cited as a factor that will help defend DRAM segment profitability from the second half onward. With the 1c transition proceeding alongside a growing mix of high value added products for servers and HBM, analysts say productivity gains are highly likely to feed directly into margin improvement.
An official in the semiconductor industry said, "The pace of the 1c transition itself is encouraging, but it only becomes meaningful if actual production yields and customer qualification schedules back it up," adding, "Starting with HBM4E, the fine process competition between Samsung Electronics and SK hynix will intensify further."
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quote: Trade $USD1 perps on @Aster_DEX @worldlibertyfi
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Aster (@Aster_DEX) has launched the first phase of its USD1 RWA campaign, running through December 31, 2026.
The campaign will distribute 125 million World LibertyFi's (@worldlibertyfi) ethereum:0xda5e1988097297dcdc1f90d4dfe7909e847cbef6 based on eligible open interest. Another 6.25 million USD1 will be distributed according to eligible trading volume.
Users can qualify for both reward pools because open interest and volume are calculated independently.
Aster is also offering a 2x open interest boost for eligible USD1 denominated RWA positions. The boost applies when traders use only USD1 as collateral through Single Asset Mode.
In Multi Asset Mode, USD1 must represent more than 50% of average collateral.
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Harmony: The Remix Economy for AI Video
Our new mission is to make creativity radically open: a remix economy where 100 creators inspire billions of fans.
We will bootstrap this economy with creators and operators who make AI videos. Advertising could generate tens of millions of dollars from a million users.
All prompts and assets will be shared for remixing. Fans can choose what to fork, and AI agents will expand each branch into dozens of stories, generating millions of remixes every day. Human choice turns abundance into trillions of meaningful moments.
This radical economy scales across powers of ten: 10² creators, 10⁶ remixes, 10¹⁰ fans, and 10¹⁴ moments.
We are recruiting operators who will generate videos, distribute media, and moderate content. We are building a community of fans and affiliates who will fuel our remix economy.
Business Model for AI Operators
The best open video model is Lightricks’ LTX 2.5, released on August 11, 2026. The standard price is $0.13 per second for 1080 × 1920 videos. The model can generate a 10-second clip in 23.7 seconds using an NVIDIA B200 or GB200. At 100% utilization, this implies an upper bound is 1.3 million clips per year, or $1.7 million in revenue.
Harmony will subsidize hardware costs and drive demand for video generation during the first year. Operators will qualify by staking tokens, earn rewards based on generation uptime, and cover all other costs.
Harmony will research the latest models, develop deployment tooling, recruit creator talent, track fan remix activity, and support broader ecosystem development.
Operators will update models, optimize costs, share technical insights, enforce community policies, comply with legal requirements, and fulfill other service obligations.
Radical Economy for AI Fans
AI video can become the biggest consumer product. Contribution rewards will fuel an economy in which fans openly remix, share, and earn.
Among the leading AI video companies, Higgsfield makes $700M in annual revenue, Kling makes $510M, and Fal makes $400M. Many offer affiliate commissions, some as high as 35%.
Harmony will track and incentivize remixing over the long term. Creators’ originals, fans’ forks, and affiliates’ promotions are all valuable contributions to our economy.
Harmony will attribute contributions based on their revenue impact, use public-key identities to protect privacy, deploy agents to resolve disputes, and manage other governance processes.
Creators will grant clear rights to use their work, fans will remix it in the styles they value most, and affiliates will participate in broader initiatives to strengthen our community.
Disclaimer: This proposal is for informational purposes only and is non-binding. All plans are subject to change. Additional terms may apply. Nothing herein constitutes financial advice or a guarantee of token value.