Sleepless AI Price(AI)

In his speech "Training Sand to Think: Artificial General Intelligence & Future of Physics," Adam Brown, a core contributor to Gemini, outlines the rapid and transformative evolution of AI. He describes how large language models (LLMs), grown rather than programmed through pre-training and fine-tuning, have progressed from performing poorly on high-school math tests to achieving gold-medal level at the International Mathematical Olympiad and recently making a genuine mathematical breakthrough by disproving a decades-old conjecture. Brown attributes this acceleration to the "Scaling Law," where predictable performance gains come from increasing compute, data, and model size. He draws parallels to the history of chess AI, predicting a similar trajectory for scientific research: moving from tools to "centaur" human-AI collaboration, and eventually to autonomous, superhuman "AI scientists." Even if progress halted today, AI already reshapes physics as a tireless tutor, powerful programming assistant, and exhaustive literature reviewer. However, Brown argues progress will continue due to immense economic runway and technical optimizations. He envisions a near-future golden age of human-AI collaboration in science, potentially leading to billions of replicated, superhuman AI researchers, making the coming years the most exciting in physics' history.

The article discusses the dual reality of AI token costs in Silicon Valley. While research firm SemiAnalysis reports spending 30% of its employee salary budget on internal LLM tokens—translating to massive productivity gains like converting complex Excel models in minutes—other giants are struggling with ballooning, uncontrolled AI bills. Uber exhausted its annual AI budget in months after rapid engineer adoption, and Microsoft is cutting third-party AI tools due to high costs. NVIDIA's CEO argues tokens are becoming "means of production" and plans substantial AI budgets per engineer. Despite current cost concerns, the analysis emphasizes that cost collapse is just beginning. Through software optimizations (like 14x throughput boosts) and next-gen hardware (e.g., GB300 NVL72 with 17-32x H100 performance), real token costs can fall far below list prices. Anthropic's gross margins reportedly soared as token prices dropped. Gartner predicts a >90% inference cost drop by 2030. The piece highlights a split: massive AI capex ($740B announced) contrasts with tech layoffs and minimal measured economic impact so far. The transition mirrors past infrastructure shifts—investment precedes widespread productivity. For early adopters like SemiAnalysis, tokens already deliver high leverage; for others, the choice is to adopt now or risk falling behind.

Arena, the AI model ranking platform, has become a $100 million annual revenue business just eight months after launching its commercial service. Originally a UC Berkeley open-source research project called Chatbot Arena, it created a "battle arena" where users blind-test and vote on anonymous AI model responses. This has generated a highly trusted, community-driven leaderboard based on over 10 million user evaluations and 82 million votes. Major AI companies like OpenAI, Google, and Anthropic submit their flagship models to be ranked. The core monetization strategy is its AI Evaluations service, where model developers and large enterprises pay for in-depth performance analysis from Arena's massive user community. This provides real-world feedback on model strengths, weaknesses, and hallucinations—a critical service as models become more complex. The company, spun out from Berkeley in early 2025, quickly raised $100 million in seed funding at a $600 million valuation and later secured a $150 million Series A at a $1.7 billion valuation. The founding team includes CEO Anastasios Angelopoulos, a mathematician focused on rigorous model evaluation; CTO Wei-Lin Chiang, creator of the popular Vicuna chatbot; and co-founder Ion Stoica, a renowned Berkeley professor. Arena is now expanding beyond chat benchmarks into "Agent Mode," evaluating AI agents on complex, multi-step tasks like coding and research. The company's success illustrates the growing value and cost of independent, real-world AI model evaluation as the industry intensifies.

Funding Roundup Weekly: 9 public financing events, with Venice AI securing a $65 million Series A led by Dragonfly. Last week saw slightly reduced activity in the crypto primary market, with capital continued to flow into on-chain trading and Web3+AI. There were a total of 9 financing events, raising over $506 million. Key highlights: - **DeFi**: 3 events. On-chain perpetuals exchange Extended secured a $12.5M strategic round led by eToro. - **Web3+AI**: 3 events. Privacy-first AI platform Venice AI raised $65M in Series A funding at a $1B valuation, led by Dragonfly. - **Prediction Markets**: 1 event. Index provider Adjacent raised $2.5M in Pre-Seed funding. - **CeFi**: 1 event. Lion Group plans to invest up to $12M in Indonesian stablecoin developer PT Nusantara Bumi Sangkara. - **Other**: 1 event. Bitcoin miner Ionic Digital closed a ~$400M private funding round. - **Acquisition**: Privacy computing project Sunscreen announced its acquisition by Fhenix.

China added 67 new unicorn companies in the first half of 2026, reaching a total of 517 unicorns with a combined valuation of approximately $2.39 trillion. This surge marks a significant rebound after a post-2022 slowdown and sets a new semi-annual record. The growth is primarily driven by Artificial Intelligence (AI) and Robotics, which together account for over 53% of the new entrants. Specifically, 19 new unicorns are in robotics and 17 in AI. Notable companies include DeepSeek ($615.38B) and Kling AI ($18B). The trend indicates a decisive shift from internet consumer models to hard tech innovation. Geographically, new unicorns are highly concentrated in four cities: Beijing (19), Shanghai (18), Shenzhen (9), and Hangzhou (5), which together host 76.1% of the new companies. Hangzhou's overall valuation is boosted significantly by DeepSeek. Valuation distribution among new unicorns is pyramidal: 77.6% are valued between $1B and $2B, indicating early-stage status, while only two exceed $10B. There is a notable "speed divide": many AI/robotics startups achieved unicorn status in under three years, often via corporate spin-offs or led by star founders, while hard tech companies in semiconductors or biotech typically took over eight years. The report concludes that this wave reflects China's accelerating transition into an AI and robotics-powered innovation cycle, characterized by faster company formation, heightened geographic concentration, and a clear focus on foundational technologies.