Coinbase Markets said on Aug. 17, 2026 that it had added Cluster Protocol, whose token trades under CP, to its asset listing roadmap and published the project’s Base contract address. The move put fresh attention on a project that says it is building an orchestration layer for autonomous AI workflows on Base, combining model inference, tokenized data, GPU compute and x402-based micro-payments under one settlement stack. According to the source article, Cluster routes requests to more than 500 open-source models through an OpenAI-compatible API, stores datasets on IPFS, represents ownership with ERC-721 NFTs on Base, and uses smart contracts to split revenue, with creators receiving 85%. Its browser-native product, CodeXero, is designed to let users describe an idea in natural language and deploy a dApp while consuming Cluster’s underlying services. The same article also notes a gap between the project’s narrative and disclosed activity. Cluster said it recorded 130,000 inference payment requests and $79,000 in settlements over the past 30 days, while CodeXero has connected more than 300,000 wallets and deployed more than 25,000 onchain dApps. Those figures were self-reported by the project, with no independent dashboard verification cited. Cluster has raised $7.75 million in total, including a $5 million round led by DAO5 on April 23, 2026.

Jie Tang, founder of Z.ai and a professor at Tsinghua University, argues that asking only how many parameters a model has no longer says much about how strong it is. In his review of the evolution of scaling laws—from GPT-3 to Chinchilla and then Mixture of Experts (MoE)—he says model capability depends on more than parameter count. Training data volume, where compute is spent, and how a model is actually used all matter. Tang’s point is that the old training-first view of scaling is less useful once commercial AI systems are deployed and called billions of times a day. Under that setup, inference cost changes the optimization target. A smaller model trained for longer may make more sense than a larger one trained less efficiently. He cited Llama-2-7B and Gemma-2-9B as examples of models trained far beyond the classic Chinchilla ratio. He also said MoE makes headline parameter numbers even less informative, because total parameters and activated parameters describe different things. For reasoning-heavy workloads, Tang argued that effective depth in a single inference pass and post-training may now be more important scaling dimensions. He described GLM-5.3 as a controlled test of that idea, keeping the base model and parameter counts unchanged from GLM-5.2 while expanding long-horizon environments and reinforcement learning over a month.

Thrive Holdings said on Aug. 12 that it had raised more than $2 billion at a $12 billion valuation, bringing total funding to over $3 billion. The company is not an AI model developer; it buys traditional businesses, including accounting firms and IT services companies, and then embeds AI into core workflows such as tax preparation and technical support. It now owns and operates more than 70 businesses, and OpenAI has taken an equity stake while sending research, product and engineering staff to help with the transformation. The model is increasingly described as an AI-enabled roll-up: instead of selling software to professional services firms, investors buy the firms themselves and then use AI to raise productivity, margins and ultimately valuation. Current, Thrive’s accounting platform, said its Tax AI processed about 7,000 returns this tax season and cut preparation time by 31% at participating firms. Dwelly, a UK property company, has taken a similar path in real estate, while General Catalyst has backed a wider group of companies built around the same idea. The article also warns that the market may be pricing in productivity gains before they are fully proven. Thrive has not disclosed group-level revenue, EBITDA or free cash flow, leaving a wide gap between its $12 billion valuation and the operating metrics the market can already see.

DGrid AI is positioning itself as a decentralized AI infrastructure protocol built around three pieces: a unified access layer for model calls, an on-chain quality verification system called Proof of Quality, and an open marketplace for model providers. In the report cited by Foresight, the project argues that today’s centralized AI platforms still leave developers and enterprises exposed to three structural problems: opaque service quality, vendor lock-in, and closed value distribution. The article says DGrid has already served more than 15,000 paying users as of the first half of 2026 and generated $23 million in verification revenue, while its AI Arena has drawn more than 500,000 users to take part in model evaluation. With the release of the $DGAI token model and an upcoming token generation event, DGrid is presented as moving from an AI service product toward a decentralized infrastructure protocol. The piece also details DGrid’s broader product lineup, including AI Gateway for developers, Model Marketplace for suppliers, AI Arena for preference and evaluation data, and DClaw for agent deployment and on-chain identity. It describes $DGAI as a coordination token for staking, payments, incentives, and governance, while framing the next test for the project around whether existing revenue and user activity can translate into sustained decentralized network usage.

Bank of America says Nvidia shares remain materially undervalued even after accounting for the financing risks tied to the company’s expanding AI ecosystem strategy. In a new note, analyst Vivek Arya used a sum-of-the-parts free cash flow approach rather than a simple price-to-earnings framework and concluded the stock is trading 34% to 50% below fair value. BofA kept its buy rating and a $350 price target. The report says Nvidia has committed about $300 billion in capital support to ecosystem partners, including roughly $70 billion in direct equity investments and another $230 billion in residual value guarantees, financial guarantees, and backstop-type commitments. Arya argues those moves reflect Nvidia’s effort to secure key inputs for the AI buildout, including chip supply, land, and power, while reducing reliance on cloud companies developing custom chips. BofA also pointed to Nvidia’s agreement to provide up to $105 billion to support a data center campus in Ohio that OpenAI is expected to lease. Nvidia is scheduled to report fiscal 2027 second-quarter results after the close on Aug. 26 Eastern Time, with Wall Street expecting about $92 billion in revenue versus the company’s own $91 billion forecast.

AI-linked stocks sold off sharply overnight in the US, with the Nasdaq Composite closing down 1.33% and the Philadelphia Semiconductor Index falling nearly 5%, before the weakness spread into Asia-Pacific markets and China A-shares. In the article, author Gelong attributes the move to three overlapping pressures rather than a single trigger. First, rising tension between the US and Iran lifted oil prices and inflation expectations, pushing long-dated US Treasury yields higher and weighing on richly valued growth sectors. Second, newly disclosed second-quarter figures for OpenAI showed quarterly revenue growth of only 18% from the prior quarter, alongside widening operating losses and continued executive departures, raising fresh questions about the pace of large-model commercialization. Third, continued friction between South Korea and the US over semiconductor investment plans added uncertainty to the global memory supply chain, especially the HBM segment that sits at the center of AI computing infrastructure. The article argues that while the long-term need for AI compute remains intact, investors are becoming less willing to pay extreme premiums for distant growth stories and are shifting their focus toward actual earnings, financing costs and the direction of global supply-chain negotiations.

U.S. stocks fell for a third straight session Tuesday as higher long-term bond yields put fresh pressure on richly valued technology names. The Nasdaq Composite dropped 1.33%, underperforming the Dow Jones Industrial Average and the S&P 500, while the 30-year U.S. Treasury yield briefly touched 5.338%, its highest level since 2007. The move was part of a wider global bond sell-off that also pushed long-dated yields higher in France, Germany, Japan and the U.K. Markets are increasingly focused on the growing debt burden tied to artificial intelligence expansion. According to figures cited in the report, AI-related bond issuance has reached $489 billion so far this year, well above an earlier full-year 2025 estimate of roughly $322 billion, while The Wall Street Journal reported that nine major technology companies have about $3 trillion in off-balance-sheet AI commitments. That backdrop hit semiconductors, memory, optical communications and AI cloud-service providers especially hard. Investors are also weighing fiscal deficits, oil-driven inflation risks tied to the Iran situation, and a heavy event calendar that includes U.S. tariffs on some Canadian products, a 20-year Treasury auction, Federal Reserve minutes and China’s one-year LPR decision.

PANews’ August 18-19 digest was packed with policy, markets, and AI updates. The U.S. debt burden may cross $40 trillion sooner than expected after tariff-related revenue losses accelerated Treasury borrowing. The SEC also proposed “Regulation Crypto Assets,” a new framework that includes startup and fundraising exemptions plus a safe harbor for digital assets that stop all managerial activity. OpenAI said it is tightening safeguards on its unreleased models, adding sandboxing and faster alerts after recent security concerns, while also pausing parts of its latest reinforcement learning training for two weeks. Elsewhere, Bhutan’s government-linked address moved 300 BTC, USDC Treasury minted 250 million USDC on Solana, and Metaplanet said it will use 2,100 BTC and $2.5 million in cash to acquire Super League and build a U.S. Bitcoin treasury platform called Superplanet. Cash App expanded beyond Bitcoin and USDC by integrating MoonPay, Ripple Prime sold $275 million of senior unsecured notes, and FASB proposed treating qualifying stablecoins as cash equivalents. The digest also covered NoOnes’ shutdown plan, a Maya Protocol exploit, Solana’s slot-time reduction, and major AI and IPO updates from OpenAI, Anthropic, Temporal, and Zhiyu/Unitree-related market listings.