NVIDIA Investment enthusiastGemini can replicate GPT's capability curve, but it's difficult to skip the expression curve, product curve, and user feedback curve that GPT has already paid its dues for.
The most crucial point here is: after the model's capabilities improve, it will instead enter a period of expression collapse, which is also my research direction.
Excessive safety, over-explanation, stiff tone, answers that seem complete but lack soul, and a loss of sharpness in code/writing scenarios.
On the path from GPT-4o to early GPT-5, OpenAI has already been scolded, fixed, rolled back, and tuned by real users; it's not surprising that Gemini 2.5 has also started encountering similar issues. Google officially also acknowledges that Gemini 2.5 is a thinking model, relying on pre-response reasoning to improve accuracy, but being able to reason and "knowing how to express in a way acceptable to humans" are two different things. 
OpenAI is definitely not forever number one, but the value of a pioneer lies in knowing which optimizations will upset users, which safety strategies will kill expression, which cost compressions will harm the experience, and which benchmark improvements won't translate into retention. This part won't appear in financial reports, nor in rumors; understanding tech stocks in the end still requires understanding the essence of technology.
1. Not reaching 1 billion weekly active users doesn't equal growth failure.
1 billion weekly active users was an extremely aggressive target from the start. Missing internal targets only indicates overly optimistic expectations; it doesn't directly imply that product demand has peaked.
2. The cash-burning model isn't unique to OpenAI.
The entire industry is burning cash. The difference lies in who has managed to convert computing power into cash flow, and Codex has actually done quite well in this regard.
3. It's true that Anthropic is grabbing enterprise and code market share, but that doesn't mean OpenAI has lost.
Claude Code is expensive, excelling in high-end development experience; Codex's advantages are cost-effectiveness, ChatGPT distribution, and ecosystem access. The enterprise market isn't winner-takes-all; the coexistence of multiple models is an objective reality.
5. Google's chain rising 300% doesn't directly prove the failure of OpenAI's narrative.
Google's rise is a combination of advertising cash flow + TPU + Gemini + Cloud + valuation recovery, not simply because the market abandoned OpenAI. This comparison is meaningless for OpenAI. If OpenAI can still compete with Google without these industries, it precisely proves Google's failure in the model space.
6. The Altman and Musk feud is noise, not a core variable.
What we should really look at are ARR, paid user retention, enterprise penetration rate, the speed of inference cost reduction, and Codex usage duration, not the squabbling between entrepreneurs.
OpenAI has already passed the productization inflection point of GPT-4o. Among the top three, ChatGPT is the earliest system to withstand massive real users, multimodal interaction, code scenarios, API calls, enterprise deployment, and consumer subscription pressures. This experience isn't something that can be immediately matched by leaderboard scores. Claude Code is strong but expensive, Gemini is catching up fast but is still undergoing the transition from model capability to user habits, but OpenAI has repeatedly verified its monetization path within ChatGPT/Codex/API/Enterprise Edition. From a developer's perspective, OpenAI isn't even close to failure.
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