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$2 Trillion: Countdown to AI's Largest IPO in History

The countdown for the largest IPO in AI history, a potential $2 trillion listing for Anthropic, is underway for October. The staggering valuation, reportedly projected by several investors, contrasts with the company's own internal restraint on setting a public target. Founded five years ago by former OpenAI core members, Anthropic's growth has been meteoric. Annual recurring revenue (ARR) surged from ~$9B in late 2025 to $47B by May 2026, with Q2 2026 revenue of $11.5B marking a 14x year-over-year increase. Bank valuations are even based on internal 2028 revenue forecasts of $190-200B. A key growth driver is Claude Code, its AI coding assistant. Its ARR quintupled in five months to $2.5B by February 2026, now constituting nearly 20% of total revenue. Surveys indicate Anthropic commands roughly 40% of enterprise LLM spending, doubling OpenAI's share in programming-specific use. However, alongside this explosive growth, reports detail significant internal cultural strife. Critics describe a divisive "priesthood" of PhD executives, led by CEO Dario Amodei, who promote a "save humanity" narrative that some employees find cult-like and alienating. This has reportedly created a demoralized workforce and a covert "underground network" of dissent among engineers torn between lucrative pre-IPO equity and a toxic work environment. Anthropic now faces a pivotal paradox: pursuing its mission of "safe" AI requires immense capital for compute, yet that capital demands relentless commercial growth. As it approaches its historic IPO, the company must navigate intense regulatory scrutiny, soaring operational costs, and internal tensions—any of which could destabilize its post-listing trajectory, much like SpaceX's significant post-IPO stock drop. The stage is set for a defining moment in tech history.

marsbitYesterday 03:51

$2 Trillion: Countdown to AI's Largest IPO in History

marsbitYesterday 03:51

Kerbrat from Robinhood Promotes Tokenization While Memecoins Dominate Its Tokenless L2

Robinhood Crypto's senior vice president, Johann Kerbrat, has emphasized the company's focus on building the technical network infrastructure for tokenization, calling it "just the beginning," rather than on launching tokens. The centerpiece is Robinhood Chain, a tokenless, EVM-compatible layer-2 network built on Arbitrum technology, which settles on Ethereum and uses ETH for gas fees. The network's primary offering is stock tokens, providing 24/7 blockchain-based exposure to companies like Nvidia and Apple for users outside the U.S., though these tokens confer no legal shareholder rights. Despite its focus on tokenized real-world assets (RWAs), currently valued at around $12.81 million, memecoins overwhelmingly dominate trading volume on Robinhood Chain. Research indicates over 99% of trading volume comes from memecoins, with the network's mascot-inspired token, $CASHCAT, surging over 5,500% in a week. Kerbrat, who recently called assets without utility "not a long-term purpose," acknowledged the chain is also "great for memes." The network has seen significant growth since launch. DeFiLlama data shows a Total Value Locked (TVL) of approximately $536 million, a stablecoin market cap of around $634 million, and 24-hour DEX trading volume of about $440 million. Notably, Ethena's USDe stablecoin has grown to constitute nearly 43% of the network's stablecoins. In mid-July, the chain was processing over 7 million daily transactions, surpassing Coinbase's Base. Robinhood is currently covering gas fees for eligible wallet users on swaps, bridges, and perps, but this subsidy is set to end in September. Separately, Robinhood reported Q2 cryptocurrency transaction revenue of $100 million, a 38% year-over-year decline, while its prediction markets generated $156 million, exceeding crypto revenue for the first time.

cryptonews.ru2 days ago 20:08

Kerbrat from Robinhood Promotes Tokenization While Memecoins Dominate Its Tokenless L2

cryptonews.ru2 days ago 20:08

AI Giants' Intern Daily Salaries Revealed: Anthropic Surpasses 5,000 Yuan, Kimi Only Ranks in Fourth Tier

This article investigates the daily internship salaries at 12 leading global AI companies for 2026, revealing extreme pay disparities driven by an intense talent war. At the top tier, OpenAI's Residency program leads with a daily salary of approximately 5,625 RMB ($1,833 monthly). Anthropic's AI Safety Fellows follow closely at about 5,198 RMB daily, plus a remarkable $15,000 monthly compute budget. Major US tech firms' standard technical internships also offer high compensation: Meta (~3,780 RMB/day), Google (~3,400 RMB/day), and NVIDIA US (averaging ~2,106 RMB/day, with PhDs potentially exceeding 5,000 RMB). Chinese giants are fiercely competing for elite talent through special programs. ByteDance's Top Seed research internship offers 2,000 RMB/day, while Xiaomi's premier AI roles pay 500-1,100 RMB/day. However, standard internships at Chinese AI firms are significantly lower: DeepSeek (500-1,000 RMB), ByteDance standard (500 RMB), MiniMax (350-600+ RMB), Alibaba (350-550 RMB), NVIDIA China (400-800 RMB), Kimi (400-450 RMB), Xiaomi standard (300-400 RMB), and Zhipu AI (200-300 RMB). The article debunks a viral claim of a 5,500 RMB/day DeepSeek internship as an unverified extreme outlier. Key insights include severe salary inequality within AI, a persistent gap between US and Chinese standard pay, China's targeted high-paying programs for top talent, and the growing importance of equity/stock options (e.g., at Zhipu, MiniMax, Kimi) alongside cash compensation. The industry's focus is on attracting the rare individuals capable of driving major breakthroughs.

Odaily星球日报2 days ago 04:31

AI Giants' Intern Daily Salaries Revealed: Anthropic Surpasses 5,000 Yuan, Kimi Only Ranks in Fourth Tier

Odaily星球日报2 days ago 04:31

Kalshi's Daily Stock Open Interest Hits Record High of $17.98 Million

Kalshi's daily open interest for its perpetual futures product hit a record $17.98 million on August 12th, just over two months after its launch. The CFTC-approved platform, which began with Bitcoin and Ethereum contracts in early June, now lists 13 crypto assets. Open interest remained below $5 million initially, saw mid-June spikes to $13-14 million, stabilized around $6-7 million, and has steadily climbed since July, consistently holding above $12 million in recent weeks. This growth indicates sustained trader engagement on a platform that recently offered only short-term binary contracts. Despite this rapid growth, Kalshi's total crypto perpetuals portfolio is small in scale, representing about 0.15% of rival Hyperliquid's $11.7 billion daily open interest. The comparison is structurally limited: each Kalshi contract requires federal regulatory pre-approval, resulting in a small list, whereas permissionless platforms like Hyperliquid support hundreds of pairs. The concentrated open interest on Kalshi's few approved contracts signals strong demand within its specific niche. Analysts suggest Kalshi is not cannibalizing existing decentralized exchange volume but is tapping a previously underserved pool of U.S.-based traders who lacked a legal, leveraged crypto platform. The true size of this new user pool remains unknown. While a market downturn could disrupt the trend, the open interest chart has so far shown consistent upward momentum.

cryptonews.ru08/14 11:22

Kalshi's Daily Stock Open Interest Hits Record High of $17.98 Million

cryptonews.ru08/14 11:22

GPT-5 Also Has Tip-of-the-Tongue Moments, Google Tested 4.5 Million Times: The Keys Are Lost

Google researchers have discovered that advanced AI models like GPT-5 and Gemini 3 experience a phenomenon akin to the human "tip-of-the-tongue" state, where they possess knowledge but fail to retrieve it. Their study, "Empty Shelves or Lost Keys?" (ICML 2026), introduces the "Knowledge Portrait" framework to analyze factual knowledge in models, distinguishing between failure to encode a fact versus failure to recall it. Testing on 13 models across 2.15 million queries from the WikiProfile benchmark revealed that state-of-the-art models successfully encode 95-98% of facts into their parameters. However, when asked directly, they fail to recall 26-34% of these known facts. Enabling chain-of-thought ("thinking") reasoning reduces this recall failure to 11-12%, recovering 40-65% of the previously unrecalled but encoded facts. The research identifies two key bottlenecks: recalling obscure ("long-tail") facts and answering reversed queries (e.g., "Who is Tom Cruise's mother?" vs. "Whose son is Tom Cruise?"). While scaling model size effectively reduces encoding failures, it does little to improve recall rates. In larger models, recall failure becomes the dominant source of factual errors, accounting for over 70% of mistakes in GPT-5.2. The findings suggest that for top models, the primary challenge is no longer storing knowledge but accessing it efficiently. Future accuracy gains may depend more on improved inference-time methods and "meta-cognitive" abilities, enabling models to recognize when they need to engage in deeper reasoning to retrieve information they already know.

marsbit08/14 08:15

GPT-5 Also Has Tip-of-the-Tongue Moments, Google Tested 4.5 Million Times: The Keys Are Lost

marsbit08/14 08:15

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