OpenAI’s Dilemma: Fully enhancing ChatGPT’s “deep research” capabilities, but end users “can’t use them”

OpenAI is facing a profound strategic dilemma: Although the company is advancing AI models toward deeper scientific and mathematical capabilities, these cutting-edge developments do not seem to translate effectively into the mass appeal of its core product, ChatGPT, resulting in a disconnect between R&D and market demand. According to The Information, this challenge has raised alarms among company leadership. Earlier this month, CEO Sam Altman issued a “code red” internally, demanding a refocus of resources to enhance ChatGPT’s appeal to a broader user base. This move came as company employees noted that, despite ChatGPT’s growing user base, most users do not make use of its breakthroughs in complex reasoning. This strategic pivot highlights the dual pressures OpenAI faces. On one hand, its user growth is under strain, making it difficult to achieve the original target of 1 billion weekly active users (WAU) set earlier this year. Reports say that as of earlier this month, WAU was under 900 million. On the other hand, competitive pressure from giants like Google is mounting; Google not only matches OpenAI on AI model capabilities, but also boasts stronger distribution channels and cost advantages. Financially, however, OpenAI remains robust. Its annualized revenue has soared from $6 billion in January to over $19 billion, driven primarily by paid subscriptions from individual and enterprise users. The company is moving toward its year-end goal of $20 billion annualized revenue, and plans to seek a new round of funding at a valuation as high as $750 billion, a 50% increase compared to two months ago. For the market, this raises a core question: Can strong technological prowess and a lofty valuation be sustained amid slowing user growth? Research and Product “Gap” A clear “gap” has emerged between OpenAI’s R&D focus and mainstream user needs. According to sources cited by employees, this year the research team has concentrated on developing “reasoning models,” which excel at handling complex math, science, and programming competition-level problems, even achieving gold medal-level performance at the International Mathematical Olympiad. However, these formidable capabilities are “too esoteric” for most ChatGPT users. Peter Gostev, head of AI capabilities at LMArena, noted that ordinary users are more likely to ask simple questions, like about movie ratings, rather than needing a model that “thinks for half an hour.” Reasoning models often take seconds or even minutes to generate results, which is not a friendly experience for users accustomed to Google’s instant search results. More importantly, ChatGPT’s current text-centric design has been compared by its product lead Nick Turley to the MS-DOS operating system of the 1980s, limiting users’ ability to discover other features such as image analysis. OpenAI’s Head of Apps, Fidji Simo, also admitted that ChatGPT needs to move from a text-and-dialogue-dominated interface to a more generative and intuitive user experience to win mass-market favor. Growth Troubles and Financial “Booster” On user growth, OpenAI is experiencing difficulties. At the start of the year, when the company had 350 million weekly active users, it set a goal to reach 1 billion within the year. However, as of earlier this month, that number was still below 900 million, making the target a challenge. To realize the company’s projection of $200 billion revenue by 2030, converting vast WAU numbers into daily active users will be a key to future commercialization. Even so, OpenAI’s financials are a potent “booster.” Through selling premium subscriptions to individuals and businesses, the company’s revenue has exploded. Its annualized revenue has increased from $6 billion to over $19 billion in less than a year, and it’s poised to reach the $20 billion target by year end. At the same time, the company is seeking funding at a staggering $750 billion valuation, demonstrating capital markets’ strong belief in its long-term potential. This divergence between slowing user growth and booming financials forms a core contradiction for investors evaluating OpenAI. In the short term, higher conversion rates to paid users are enough to support its financials, but in the long term, stagnant user base growth will be the biggest risk to its business model and high valuation. Google’s Pursuit and Internal “Code Red” Competitive pressure from Google was the direct reason for Altman’s “code red.” Reports indicate that Google’s AI models now rival ChatGPT in image generation, code processing, and other capabilities. More crucially, Google possesses unrivaled distribution channels in Search, Chrome, Gmail, and benefits from cost efficiencies due to proprietary AI chips. A typical example occurred in the image generation sector. According to two employees, OpenAI earlier this year deprioritized image generation model development. However, after Google launched its consumer-acclaimed image generation AI “Nano Banana” in August, OpenAI leaders scrambled to catch up. The event even reportedly caused a rift between CEO Altman and research director Mark Chen. The leadership is increasingly concerned that regular users will struggle to distinguish between ChatGPT and Google’s Gemini. Unlike social apps with powerful network effects, the stickiness of chatbot users is relatively low, making OpenAI’s market position more vulnerable. Altman’s “code red” was issued precisely in this context, aiming to redirect part of the team back to the ChatGPT project to meet imminent competitive threats. Organizational Structure Challenge Deep organizational structure issues are said to be at the root of the current predicament. Reportedly, OpenAI has a research department of over 1,000 people, largely “isolated” from other company divisions. The new Apps head Fidji Simo even wrote in a blog post that OpenAI is essentially still a research-centric company—“the product itself is not the goal.” Meanwhile, CEO Sam Altman has spent most of this year dividing his attention among multiple frontier projects such as Sora video app, music generation AI, web browser, consumer hardware, and robotics. Several researchers said these projects drained precious resources that could have been used to boost ChatGPT’s popular appeal. This organizational divide also brings technical challenges. For example, when integrating the new GPT-5 model, researchers found its performance declined due to interference with ChatGPT’s personalization functions (like tailoring answers based on user data). While these issues were ultimately resolved, they revealed that coordination within OpenAI remains an obstacle when translating new technologies into mature products. Risk Warning and Disclaimer The market is risky; investments need to be made with caution. This article does not constitute personal investment advice, nor does it take into account individual users’ specific investment goals, financial circumstances, or needs. Users should consider whether any opinions, views, or conclusions in this article are appropriate for their own situations. Investment decisions based on this content are at the user’s own risk.