Underneath the surface, OpenAI's "Four Major Dilemmas"

Benedict Evans stated that the lack of technological moats, insufficient user stickiness, platform strategies lacking flywheel effects, and product strategies constrained by laboratory R&D directions are threatening OpenAI’s long-term competitiveness.

Article by: Zhao Ying

Source: Wall Street Insights

Former a16z partner and renowned tech analyst Benedict Evans recently published an in-depth analysis article, directly pointing out four fundamental strategic dilemmas facing OpenAI behind its surface prosperity. He believes that although OpenAI has a large user base and ample capital, issues such as the absence of technological moats, weak user engagement, rapid competition catching up, and product strategies limited by lab R&D are endangering its long-term competitiveness.

Evans pointed out that OpenAI’s current business model does not have a clear competitive advantage. The company neither possesses unique technology nor has established network effects. Of its 900 million weekly active users, only 5% are paying, and 80% of users sent fewer than 1,000 messages in 2025—equivalent to less than three prompts per day on average. This “wide but shallow” usage pattern indicates that ChatGPT has not yet become a daily habit for users.

Meanwhile, tech giants like Google and Meta have caught up with OpenAI technologically and are leveraging their distribution advantages to capture market share. Evans believes that the true value in AI will come from yet-to-be-invented new experiences and application scenarios, which OpenAI cannot create alone. This forces the company to operate on multiple fronts simultaneously, deploying across infrastructure and application layers.

Evans’ analysis reveals a core contradiction: OpenAI attempts to build competitive barriers through large-scale capital investment and a full-stack platform strategy, but without network effects and user lock-in mechanisms, the effectiveness of this approach remains uncertain. For investors, this means reevaluating OpenAI’s long-term value proposition and its true position in the AI competitive landscape.

Disappearance of Technical Advantages: Increasing Model Homogeneity

Evans notes that currently about six organizations can launch competitive cutting-edge models with similar performance. Companies frequently surpass each other every few weeks, but no one has established a technological lead that others cannot match. This contrasts sharply with platforms like Windows, Google Search, or Instagram—where network effects reinforce market share, making it difficult for competitors to break monopolies regardless of investment.

This technological equalization could change with certain breakthroughs, most notably the realization of continuous learning capabilities, but Evans believes OpenAI currently has no plans in this direction. Another potential differentiator is the scale effects of proprietary data, including user data or vertical industry data, but existing platform companies also hold advantages here.

As model performance converges, competition shifts toward branding and distribution channels. The rapid growth of Gemini and Meta AI market shares confirms this trend—these products appear similar to ordinary users, while Google and Meta possess strong distribution capabilities. In contrast, Anthropic’s Claude model often ranks highly in benchmarks but lacks consumer strategy and product presence, resulting in near-zero consumer awareness.

Evans compares ChatGPT to Netscape, which once held an early advantage in the browser market but was ultimately defeated by Microsoft leveraging distribution advantages. He believes chatbots face the same differentiation challenge as browsers: they are essentially just an input box and output box, with limited space for product innovation.

User Base Fragility: Scale Cannot Mask Lack of Engagement

Despite OpenAI’s obvious lead with 800 to 900 million weekly active users, Evans points out that this data masks serious user engagement issues. The vast majority of users who are aware of and know how to use ChatGPT have not turned it into a daily habit.

Data shows that only 5% of ChatGPT users are paying, and even among American teenagers, the proportion who use it several times a week or less far exceeds those who use it multiple times daily. OpenAI disclosed in its “2025 Year-End Summary” that 80% of users sent fewer than 1,000 messages in 2025, which, on face value, equates to less than three prompts per day, with actual chat frequency being even lower.

This superficial usage means most users do not see differences between models in personality or focus, nor benefit from features like “memory” designed to build stickiness. Evans emphasizes that memory functions can only foster stickiness, not network effects. While a larger user base’s usage data might be an advantage, when 80% of users use the system only a few times a week, the value of this advantage is questionable.

OpenAI itself admits there are issues, citing a “capability gap” between model abilities and actual user usage. Evans believes this is an evasion of the unclear product-market fit. If users can’t think of what to do with it in everyday life, it indicates it has not yet changed their routines.

The company has launched advertising projects, partly to cover the costs of serving over 90% non-paying users, but more strategically, to provide these users with the latest and most powerful (and most expensive) models, hoping to deepen engagement. However, Evans questions whether giving users better models today or this week will truly change their inability to think of uses.

Platform Strategy in Doubt: Lack of Genuine Flywheel Effect

Last year, OpenAI CEO Sam Altman attempted to unify the company’s initiatives into a coherent strategy, presenting a diagram and quoting Bill Gates: “A platform is defined as creating more value for partners than for itself.” Meanwhile, the CFO released another diagram illustrating a “flywheel effect.”

Evans considers the flywheel effect a clever, coherent strategy: capital expenditure itself creates a virtuous cycle and forms the foundation for building a full-stack platform company. Starting with chips and infrastructure, each layer of the tech stack is built upward, enabling others to use your tools to create their own products. Everyone uses your cloud, chips, and models, and at higher levels, the layers of the tech stack reinforce each other, forming network effects and ecosystems.

However, Evans bluntly states that this is not the right analogy; OpenAI does not possess the platform and ecosystem dynamics that Microsoft or Apple once had, and the flywheel diagram does not truly demonstrate a real flywheel effect.

In terms of capital expenditure, the four major cloud providers invested about $400 billion in infrastructure last year and announced plans to spend at least $650 billion this year. A few months ago, OpenAI claimed future commitments of $1.4 trillion and 30 gigawatts of computing power (without a clear timeline), but actual usage by the end of 2025 was only 1.9 gigawatts. Due to a lack of large-scale cash flow from existing business, the company relies on financing and leveraging others’ balance sheets (partly involving “circular revenue”) to achieve these goals.

Evans believes large-scale capital investment may only secure a seat at the table rather than provide a competitive advantage. He compares the costs of AI infrastructure to aircraft manufacturing or the semiconductor industry: no network effects, but each generation’s process becomes more difficult and expensive, ultimately requiring only a few companies to sustain the necessary investments at the frontier. TSMC, despite its de facto monopoly in advanced chips, has not gained leverage or value extraction in the upstream tech stack.

Evans points out that developers build applications for Windows because it has nearly all users, and users buy Windows PCs because it has nearly all developers—this is a network effect. But if you invent a great new application or product with generative AI, you only need to call an API to run the underlying model in the cloud; users don’t know or care what model you use.

Lack of Product Dominance: Strategy Limited by Labs

At the start of the article, Evans quotes OpenAI product lead Fidji Simo from 2026: “Jakub and Mark set the long-term research directions. After months of work, amazing results emerge, and then researchers contact me: ‘I have some cool stuff. How do you plan to use it in chat? How can it be used for our enterprise products?’”

This contrasts sharply with Steve Jobs’ famous 1997 quote: “You have to start with the customer experience and then work backwards to the technology. You can’t start with the technology and try to figure out where to sell it.”

Evans believes that when you are an AI lab product manager, you cannot control your own roadmap; your ability to set product strategy is very limited. You wake up in the morning and find that the lab has made some discovery, and your job is to turn it into a button. The strategy happens elsewhere—where exactly?

This highlights a fundamental challenge OpenAI faces: unlike Google in the 2000s or Apple in the 2010s, OpenAI’s talented and ambitious staff do not have a truly effective product that others cannot replicate. Evans interprets the past 12 months of OpenAI’s activities as a realization that Sam Altman is deeply aware of this and is trying to turn the company’s valuation into a more durable strategic position before the music stops.

Most of last year, OpenAI’s approach seemed to be “doing everything simultaneously, executing immediately.” Application platforms, browsers, social video apps, collaborations with Jony Ive, medical research, advertising, and more. Evans sees some of these as “full-scale assaults” or simply results of rapidly hiring many proactive people. Sometimes, it feels like copying the form of previously successful platforms without fully understanding their purpose or dynamic mechanisms.

Evans repeatedly uses terms like platform, ecosystem, leverage, and network effects, but admits these terms are widely used in tech and their meanings are often vague. He quotes medieval history professor Roger Lovatt from his university days: power is the ability to make people do what they don’t want to do. That’s the real issue: does OpenAI have the ability to get consumers, developers, and enterprises to use its systems more, regardless of what the system actually does? Microsoft, Apple, Facebook once had this ability, and so did Amazon.

Evans suggests a good way to interpret Bill Gates’ quote is that a platform truly leverages the entire tech industry’s creativity, so you don’t have to invent everything yourself; you can build more at scale, but all within your system, under your control. Foundation models are indeed multipliers; many new things will be built with them. But is there a reason everyone must use your product even if competitors have built the same thing? Is there a reason your product must always be better than competitors’ regardless of how much they invest?

Evans concludes that without these advantages, the only thing you have is daily execution. Doing better than others is of course desirable; some companies have achieved this over long periods and even institutionalized it, but that is not a strategy.