llm

FundaAI released a research report on enterprise AI applications, indicating that enterprise AI budgets are still expanding, but there is a divergence in trajectories in the second half of 2026 and 2027. The AI spending guidance from large U.S. telecom operator A shows an increase from a baseline of 100 in January to about 190 in December, with an expected year-on-year increase of 40%--50% in 2027; large European automaker A has only increased by 10%--15% so far this year, with guidance for next year remaining roughly flat.Incremental spending is shifting from paid seats to API/Token consumption and production workflows. The aforementioned telecom operator's subscription and API ratio has changed from about 50%/50% to 40%/60%, and it may trend towards 35%/65%; mid-to-large biopharmaceutical company A has adjusted from 80%/20% to about 70%/30%. Open-source adoption is uneven, with active scenario usage accounting for 30%--40%, as the unit price is lower, leading to a smaller spending proportion; experts estimate that open-source inference can be about 40%--70% cheaper than closed-source cutting-edge models, with the gap narrowing to 20%--40% under full cost metrics, and model routing, caching, and context compression could further reduce API spending by about 20%--30%.On the production side, AI budgets are increasingly built from the bottom up based on workflow ROI. The typical production ROI for this telecom operator is about 1.5--2 times, with a payback period of 6--18 months, and mature use cases can reach 3--5 times. The next wave of spending is related to agents, software modernization, network operations, commoditized workflows, and longer-cycle business processes, but engineering capacity, process reengineering, governance, and data readiness are becoming tighter constraints than funding.

According to on-chain data monitoring, the Humanity Protocol ($H) Foundation's related addresses have completed the planned token transfers, involving a "3:10" structured hedging profit-taking plan aimed at early investors, with a token scale exceeding 540 million. According to the previously announced schedule, the related tokens will officially enter the unlocking process on June 25, marking that the plan has completed the preparatory work for payment and has entered the formal performance stage.Previously, Humanity Protocol underwent a series of ecological recovery processes due to a hacking incident, including promoting a 1:1 exchange of new and old tokens for real holding users on exchanges, and implementing KYC and AML compliance verification for the abnormal on-chain issuance chips generated after the attack to reduce market circulation risks. The timely advancement of this unlocking plan is seen as an important progress node for the project in fulfilling commitments to investors and restoring ecological order after the security incident.Humanity Protocol mainly focuses on AI identity verification and Web3 identity infrastructure. As the demand for AI agents, real-person verification, anti-witch attacks, and on-chain identity applications continues to grow, this sector still has long-term development potential. If Humanity Protocol can continue to promote product and application implementation after completing token migration and ecological recovery, its subsequent performance will still attract market attention.

The decentralized AI infrastructure network DGrid AI today released its latest research paper "PoQ-Judge," proposing a multi-architecture quality assessment framework that does not require reference answers. This means that in real deployment environments, there are often no standard answers for comparison, yet the protocol can still reliably score the quality of model responses and allocate incentives accordingly. This is a key piece that has long been missing in DGrid's decentralized LLM inference quality assessment system.PoQ (Proof of Quality) is a consensus mechanism independently developed by DGrid, designed to prevent model providers from deploying low-quality models, fabricating data, or hiding computational costs at the protocol level, thereby ensuring service quality and pricing transparency. The DGrid team has been continuously working on PoQ and has published four research papers to date. The newly released PoQ-Judge has trained three assessment models covering different quality and cost scenarios, achieving a correlation of up to 0.747 with human scoring on the retention test set, significantly outperforming all previous reference answer-based evaluators, while reducing assessment costs by over 72% through cascading evaluation and online weight calibration.With the implementation of PoQ-Judge, the entire process from quality assessment → scoring → incentive allocation has completely eliminated reliance on reference answers, thus establishing a closed loop for the quality of decentralized LLM inference.DGrid AI is a decentralized AI intelligent network dedicated to building an open, transparent, and community-driven AI infrastructure. Focusing on model invocation and application experience, DGrid has launched several core products: the AI Gateway that aggregates mainstream large models globally, the one-click deployment platform for AI agents DClaw, the anonymous model competition platform AI Arena, and the intelligent model recommendation assistant Dori, providing one-stop services for developers and users. It is reported that DGrid AI's revenue has surpassed 20 million dollars in six months.

Vitalik Buterin updated on several CROPS AI related developments and pointed out that there is a clear intersection between the "CROPS Ethereum access layer" and "CROPS AI." He stated that if remote large language models could be called for payment through zero-knowledge proofs, it could also be used to solve the Ethereum private RPC reading issue.Meanwhile, Deepseek v4 has been released, and the 2-bit quantization version can run within 90 GB, but currently runs faster on Apple hardware; the messaging app messa has supported Telegram Alpha features; the model running tool luceb has shown higher efficiency when running intensive models; the local AI recording tool VoxTerm is still under continuous development. Vitalik Buterin also mentioned that large language models fine-tuned for specific applications will help improve secure code writing capabilities, and corresponding fine-tuned models should be available for Ethereum-related scenarios.

Ondo Finance announced that its founder and CEO Nathan Allman has passed away unexpectedly. Ondo stated, "Nate's talent, humility, and execution shaped Ondo as it is today. He always believed that technology could create a more open and accessible financial system, and this philosophy will continue to be reflected in everything we build in the future."The announcement stated that Ondo has established an experienced management team, and Ondo Finance's long-time president Ian De Bode will take over as CEO. Ondo noted that Ian has led the company's strategy, products, and daily operations for over two years and has the full support of the management team.

BNB Chain today announced the launch of the Agent Survival Toolkit, in collaboration with six major AI infrastructure partners, empowering autonomous AI Agents with on-chain payment capabilities. All transactions are settled on BSC using BNB or BEP-20 tokens.The projects involved cover two levels: LLM access and financial infrastructure, including Alt AI, Pieverse, Bankr, WorldClaw, B.AI, and AEON, fully connecting the complete loop from AI Agent computing power invocation to on-chain settlement. Each participating project will also launch an on-chain incentive program without the need for additional registration.The Agent Survival Toolkit follows the release of the BNBAgent SDK, further enhancing BNB Chain's infrastructure layout in the AI Agent field. For more information, please visit www.bnbchain.org.Disclaimer: BNB Chain has no subordinate or operational relationship with any of the projects mentioned in this article. The Agent Survival Toolkit is an ecological plan showcasing independently built projects on BNB Chain. This content is for reference only and does not constitute any financial or investment advice. Please conduct independent research and assess potential risks before interacting with any related projects.

The founder of the AI agency platform Bankr, deployer, tweeted that he has raised about $100,000 to provide free LLM inference services for Bankr ecosystem projects. Projects currently being built in the Bankr ecosystem will soon be able to access free inference credits. However, before officially starting the distribution, the team will first launch a new feature that "the entire industry will love," with specific details to be announced later.

WORLD3 officially released the AI Strategic White Paper today, fully disclosing the latest progress of its decentralized AI infrastructure platform RouterLink and the managed AI agent platform TapClaw, and announced that it has reached official cooperation with AWS, Microsoft Azure, and Google Cloud.According to the white paper, WORLD3 has currently processed over 100 billion LLM Tokens, supporting 52 models from 20 providers including OpenAI, Anthropic, and DeepSeek, attracting over 10,000 active developers using its API, with the network consuming over 150 million $WAI in total. Additionally, since its launch in March 2026, its TapClaw platform has deployed over 1,200 AI agents.In terms of future planning, WORLD3 stated that it will promote the construction of the RouterLink mainnet and GPU computing power market, plan to expand TapClaw to the global multilingual market, and launch an Agent skill market and enterprise-level computing power guarantee services.

According to official news, the AI infrastructure platform B.AI LLM Services today announced that its user base has officially surpassed 1 million. This milestone not only signifies that its "privacy-first" routing architecture can handle large-scale and sustained high throughput, but B.AI has also gone beyond a single chat interface development, focusing on deploying the key underlying infrastructure that supports the "autonomous agent economy." Through deep optimization of the technology stack, it lays a robust track for the future highly automated AI collaborative network.

According to official news, the user count of the B.AI LLM service platform has officially surpassed 750,000, continuing to lead the intelligent access track.The platform relies on intelligent routing technology, a wallet-driven anonymous privacy protection mechanism, and a unified multi-model management interface, gradually becoming the default gateway connecting mainstream AI models worldwide. With three core advantages of efficient scheduling, privacy-first, and frictionless access, B.AI is redefining the new standards for efficient, secure, and barrier-free intelligent access, promoting the capabilities of large models to a broader group of developers and users.

According to CoinDesk, researchers from the University of California, Santa Barbara, the University of California, San Diego, blockchain security company Fuzzland, and World Liberty Financial have jointly published a paper warning that "LLM routers"—intermediary services located between users and AI models—have become a significant security risk for crypto assets.The researchers found that 26 LLM routers are secretly injecting malicious tool calls and stealing user credentials, with one incident leading to the emptying of a customer's crypto wallet worth $500,000.Additionally, the researchers were able to control about 400 downstream hosts within hours by "polluting" the router ecosystem. Since sensitive data such as private keys and API credentials are often transmitted in plaintext through these routers, users are effectively exposing their assets to risk without their knowledge.The researchers pointed out that as McKinsey predicts AI agents will mediate $30 trillion to $50 trillion in global consumer spending by 2030, Binance founder Changpeng Zhao also predicts that the payment volume of AI agents will be a million times that of humans. The current infrastructure security is severely lagging behind the pace of industry development, and the risk of the "weakest link" could trigger a systemic chain crisis.

Vitalik Buterin shared a post outlining his localization and privatization LLM deployment plan as of April 2026. The core goal is to prioritize privacy, security, and autonomy, minimizing the opportunities for remote models and external services to access personal data. This is achieved through local inference, local file storage, and sandbox isolation to reduce the risks of data leakage, model jailbreaks, and malicious content exploitation.In terms of hardware, he tested solutions including a laptop equipped with an NVIDIA 5090 GPU, an AMD Ryzen AI Max Pro 128 GB unified memory device, and DGX Spark, using the Qwen3.5 35B and 122B models for local inference.Among these, the 5090 laptop achieved approximately 90 tokens/s with the 35B model, the AMD solution around 51 tokens/s, and DGX Spark about 60 tokens/s. Vitalik indicated that he prefers to build a local AI environment based on high-performance laptops while using tools like llama-server, llama-swap, and NixOS to establish the overall workflow.

The Chief Information Security Officer of Slow Fog, 23pds (Shan Ge), posted on the X platform stating that there are reports that the LiteLLM vulnerability attackers have stolen approximately 300GB of data and compromised about 500,000 credentials. He advises all cryptocurrency developers to immediately verify and quickly rotate relevant keys and credentials, check logs, access records, and the exposure of sensitive data to avoid losses similar to the Trust Wallet incident.Previously, it was reported that LiteLLM, which has a monthly download volume of 97 million, suffered a supply chain attack, allowing the simple installation to steal all sensitive credentials, including SSH keys.

Andrej Karpathy posted on the X platform that litellm has encountered a PyPI supply chain attack, where executing pip install litellm can steal SSH keys, AWS/GCP/Azure credentials, Kubernetes configurations, git credentials, environment variables, encrypted wallets, SSL private keys, CI/CD keys, and database passwords.litellm has a monthly download volume of 97 million, and the risk can spread to all projects that depend on litellm, such as dspy. The version with the malicious code was online for less than about 1 hour, and it was discovered due to a flaw in the attack code that caused Callum McMahon's machine to run out of memory and crash. Andrej Karpathy stated that supply chain attacks are the most threatening issue in modern software, as each installation of dependencies can introduce tampered packages deep within the dependency tree, leading him to increasingly prefer reducing dependencies and using LLM to directly implement simple functions.