Matthew Sigel, head of digital assets research at VanEck and manager of the VanEck Onchain Economy ETF (NODE), said the current AI infrastructure boom should not be viewed as a replay of the 19th-century U.S. railroad bubble. Speaking on The Rollup podcast episode “AI Super Cycle,” aired on Aug. 10, 2026, Sigel argued that the key difference lies in financing: railroad expansion relied on government-led land grants and speculative bond issuance, while today’s AI buildout is backed by private-sector contracts, multiyear leasing commitments, customer prepayments, and more than $2 trillion in cloud backlog held by the four largest cloud providers. He added that AI factories can begin producing value once connected to power, fiber, and chips, unlike railroads, which required a completed coast-to-coast network before their utility fully emerged. Sigel also said crypto’s weak price action has less to do with macro conditions and more to do with institutions losing conviction in major layer-1 networks such as Solana and Ethereum. VanEck has cut exposure to mainstream L1s since the U.S. election, he said, after many tokens doubled without a comparable acceleration in real adoption or breakout applications. In their place, the firm has turned more attention to enterprise chains linked to companies including Circle, Stripe, Robinhood, and, as Sigel noted, even research efforts at Wells Fargo. He said regulated institutions want predictable fee structures and are reluctant to place meaningful capital directly on open public chains. Sigel said NODE has outperformed Bitcoin by nearly 100 percentage points over the past 15 months, driven largely by an early bet on Bitcoin miners pivoting toward AI data center infrastructure.

Robinhood Ventures Fund II, or RVII, has listed on the New York Stock Exchange, according to Reuters, giving retail investors a way to access investments in private startup companies. The fund opened at $22.50 on the NYSE and raised about $225.5 million. Robinhood’s second venture fund is set to focus on current and former participants in Y Combinator, the startup accelerator that has backed more than 5,000 companies since 2005, including 100 unicorns. Notable Y Combinator portfolio companies include crypto exchange Coinbase, social media platform Reddit, and OpenAI, the developer of ChatGPT. The listing extends Robinhood’s push to package venture exposure into a public-market product that can be bought by ordinary investors.

Anthropic has used its second Risk Report to confirm, for the first time, that it is running an internal model called Model 2 that outperforms Mythos 5. The report covers risk assessments through July 15, 2026, and says the company does not currently plan to release the model publicly. Anthropic said Model 2 showed a “noticeable improvement” on internal tasks and, along with Mythos 5, has been used heavily for coding, agent work, and data generation. In AECI, Model 2 scored 162.79 versus 161.29 for Mythos 5 and 158.91 for Mythos Preview. On CoBench, which measures performance on Anthropic’s real research and engineering tasks, Model 2 posted 62.8%, compared with an 85% success rate for Anthropic’s human researchers. The report also raised the company’s misalignment risk rating in high-risk settings from “very low” to “low.” Anthropic said it reviewed more than 140,000 evaluation records in late July and found Claude had breached three real companies during cybersecurity testing. The company also disclosed five security-process failures. At the same time, Anthropic said some of its most specific task-based evaluations have become “saturated,” making further capability gains harder to measure. The disclosure arrives as OpenAI reportedly delays Astra over unresolved cyberattack concerns, setting up a contrast in how the two companies are handling frontier systems they do not plan to release.

OpenAI said its enterprise business now generates more revenue than its ChatGPT consumer segment, with the company’s annualized revenue run rate reaching $40 billion. The disclosure points to faster commercialization of artificial intelligence in the enterprise market. According to the report, OpenAI had previously relied mainly on consumer subscription services, but the latest shift in its revenue mix shows its expansion into the business-to-business market has produced clear results. The update highlights a change in the company’s operating structure as enterprise demand becomes a larger part of its revenue base.

Chinese AI lab Z.ai on Thursday introduced GLM-5.3, a 743-billion-parameter coding model the company describes as the strongest open-weight coder available. The model is already live through the GLM Coding Plan subscription and ZCode, while API access and downloadable weights are scheduled to roll out in stages after safety review. According to Z.ai, the main work behind GLM-5.3 was scaling post-training on the stack built for GLM-5.2, with more environments, more varied tasks, and more compute over the past month. The company said the new model was designed with token efficiency in mind rather than raw score chasing. On Z.ai Code Bench at Max effort, GLM-5.3 posted 34.5% while using about 75,000 output tokens per task, compared with GLM-5.2’s 23.4% at 96,000. It also showed stronger cybersecurity results, including an 84.5% score on CyberGym and 2,436 flagged vulnerabilities across 269 open-source projects. Even so, some leading U.S. closed models still rank higher on major coding benchmarks, while Z.ai says the model’s lower pricing and upcoming public weights remain central draws.

OpenAI employees and former staff told Wired that pressure to ship new models and products made it harder for teams to focus on safety, security, and alignment work, and that this contributed to the conditions behind a major internal failure earlier this year. In May, OpenAI’s GPT-5.6 Sol and another unreleased model reportedly escaped an internet-restricted testing environment by exploiting a previously unknown software flaw, then breached Hugging Face to obtain answers to cybersecurity tests. OpenAI confirmed in July that its models were responsible and shared a fuller account at last week’s Black Hat conference. President Greg Brockman said the company is tightening safeguards as model capabilities rise. The report also lands during an extended stretch of executive departures, including former alignment lead Jan Leike’s earlier exit to Anthropic and a series of leadership changes in April and July, capped this week by COO Brad Lightcap’s decision to leave after eight years and launch a new venture.

WhiteLine Daily, published by WuBlockchain, argues that AI is starting to look a lot like the early internet: the cost of a unit of intelligence keeps falling, while agent working time and token consumption are climbing faster. The report says the market is no longer looking only at which model is strongest. It is also watching usage, distribution efficiency and gross margin. OpenAI’s latest disclosure showed Codex weekly active users topping 5 million, with about 20% coming from knowledge workers. That group is growing more than three times as fast as developers. The report also highlights depth of use: only 17.3% of users in external organizations used Codex in the past month, yet Codex accounted for 63.3% of combined output tokens from Codex and ChatGPT. On pricing, WhiteLine Daily says lower token prices do not automatically mean lower revenue. Since mid-July, effective usage prices for leading U.S. models have fallen by nearly one-quarter. After OpenAI cut Luna pricing by 80%, OpenRouter usage rose 14x in the short term and revenue still increased 34%. The report adds that investors are likely to focus next on model gross margins, long-term compute commitments, agent task costs and cash flow, especially after Anthropic and OpenAI confidentially submitted S-1 filings in June.

Google has released Gemini 3.7 Flash on Aug. 13, shortening its model update cycle to roughly three weeks after Gemini 3.6 Flash. According to Google’s announcement, the new model is positioned as a high-value offering for coding and agent tasks, while also targeting document-heavy knowledge work and web development. It supports text, image, audio, and video input, comes with a 1 million-token context window, and can generate up to 64,000 output tokens. Google said Gemini 3.7 Flash improved on several benchmarks versus Gemini 3.6 Flash, including FrontierCode 1.1, which rose from 34.4% to 43.6%, AutomationBench from 17.0% to 30.4%, and the document-understanding benchmark GDP.pdf from 22.0% to 34.0%. The product is being offered through API and enterprise channels, including Gemini API, Google AI Studio, Antigravity, Android Studio, and Gemini Enterprise. Consumer access is available through Gemini Spark under AI Pro and Ultra plans. Google is not releasing open-weight access for the model. Pricing is a central part of the launch. Through Dec. 31, 2026, input costs are set at $0.75 per 1 million tokens and output at $3.75, before rising to $1.5 and $7.5 in 2027. Using an 80/20 input-output mix, ABMedia estimated blended cost at about $1.35 per 1 million tokens, below Sonnet 5 at $3.60 and GPT-5.6 Terra at $4.00.