GTHT released a research report stating that it maintains an "Overweight" rating on the overseas AI computing and application industry chain. The ultra-high-speed growth of OpenAI and Anthropic, on one hand, validates the continuous expansion of AI model capability boundaries, and on the other hand, drives demand growth across the entire AI industry chain. It is recommended to focus on three key directions: (1) Hardware supply chains such as GPU/ASIC, CPU, storage, advanced processes and packaging, and semiconductor equipment, which benefit from the computing power expansion of OpenAI and other leading global model companies; (2) Cloud providers and infrastructure platforms that undertake AI workloads and model distribution; (3) Application companies with the capability to implement across the entire ecosystem and all scenarios. The main views of GTHT are as follows: The long-term value of OpenAI lies in its potential to become the largest consumer-facing gateway in the AI era, with advertising monetization and hardware carrier supplementation jointly raising its revenue ceiling. Consumer-facing advertising has enormous monetization potential, currently not yet realized. The firm estimates that OpenAI's advertising revenue could reach $133.1 billion in a Bull Case scenario by 2030, and its set target of $102 billion in advertising revenue has high credibility. Market concerns that "free" consumer users are a significant burden on the profit side are addressed by the firm's calculation that OpenAI may achieve positive operating profit by 2029/2030. Although later than Anthropic, it could accelerate its path to profitability by shifting more free traffic to smaller models to reduce inference costs, among other methods. Meanwhile, OpenAI's training costs are consistently higher than Anthropic's in the long term, which temporarily drags down profit margins, but these investments may translate into leading capital in the next round of model paradigm shifts. Due to later strategic focus and diverging technical routes, OpenAI's coding capabilities lag behind Anthropic's, but it remains within a catch-up range. Although OpenAI has a larger consumer user base and a higher revenue ceiling, the currently most profitable high-value scenario is business-facing coding, where Anthropic has already established a phased lead. The firm believes: (1) The capability gap among model companies in coding scenarios mainly stems from differences in strategic timing and the divergence between RL and Harness technical routes. Anthropic focused on coding earlier and chose the Harness route, embedding Claude Code into real development workflows; OpenAI initially emphasized the RL route, relying on evaluations, test pass rates, and reinforcement learning to enhance model capabilities, resulting in relatively lagging coding productization and workflow penetration. (2) The coding scenario has now become a consensus among model companies. The firm believes that after accelerated investment, OpenAI is expected to close the coding capability gap. Current Codex user data already indicates this trend: in the first week of May, Codex weekly downloads increased by approximately 1400% month-over-month to 86.1 million, while Claude Code downloads fell 38% week-over-week to 7.2 million during the same period, reflecting a trend of developer migration to some extent. (3) The firm estimates that, after adjusting for measurement differences, the revenue gap between OpenAI and Anthropic in the business segment is $5-6 billion, still within a catch-up range. Computing power determines the revenue ceiling, and OpenAI's computing power reserves will constitute an important comparative advantage. OpenAI's computing power reserves are relatively aggressive, with capacity expected to increase from 1.9 GW in 2025 to 30 GW in 2030, covering diverse architectures including NVDA GPU, AMD GPU, OpenAI's self-developed ASIC (Broadcom), Amazon Trainium, and Cerebras. In contrast, Anthropic has currently secured approximately 12.3 GW of computing power cumulatively. Against the backdrop of long-term computing power scarcity, the massive computing power secured by OpenAI in advance will become a key advantage for model iteration and sustained revenue growth. Risk warnings: Risks of commercialization progress falling short of expectations; Risks of intensified competition and technological iteration; Risks related to cash flow and balance sheets; Risks of overly optimistic financial estimates.