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

By Zhao Ying

Source: Wall Street Insights

Benedict Evans, a16z former partner and renowned tech analyst, recently published an in-depth analysis article, directly pointing out four fundamental strategic dilemmas facing OpenAI behind its surface prosperity. He believes that despite OpenAI’s large user base and ample capital, issues such as lack of technological moat, insufficient user stickiness, rapid competition from rivals, and product strategy being constrained by laboratory R&D directions threaten its long-term competitiveness.

Evans notes that OpenAI’s current business model does not have a clear competitive advantage. The company lacks unique technology and has not formed network effects. Only 5% of its 900 million weekly active users 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 technically with OpenAI and are leveraging their distribution advantages to capture market share. Evans believes the true value in AI will come from new experiences and application scenarios that have yet to be invented, and OpenAI cannot create all these innovations alone. This forces the company to operate on multiple fronts simultaneously, from infrastructure to application layers, with comprehensive deployment.

Evans’ analysis reveals a core contradiction: OpenAI attempts to build a competitive barrier 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.

Disappearing Technical Advantages: Increasing Model Homogeneity

Evans points out that currently about six organizations can launch competitive cutting-edge models with performance that is largely comparable. Companies frequently surpass each other every few weeks, but no one can establish 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 breakthroughs, most notably in continuous learning capabilities, but Evans believes OpenAI currently has no plans in this direction. Another potential differentiator is the scale effect 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, though often top-ranked in benchmarks, has near-zero consumer recognition due to a lack of consumer strategy and product presence.

Evans compares ChatGPT to Netscape, which once held early dominance 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 an output box, with limited room for product innovation.

User Base Is Fragile: 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 cultivated it into a daily habit.

Data shows only 5% of ChatGPT users are paying, and even among American teenagers, the proportion using it several times a week or less far exceeds those using it multiple times daily. OpenAI disclosed in its 2025 annual summary that 80% of users sent fewer than 1,000 messages in 2025, which, at 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 in personality or focus among models, 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 might provide usage data advantages, when 80% of users use the service only a few times a week, the value of such data is questionable.

OpenAI itself admits there are issues, citing a “capability gap” between model abilities and actual user usage. Evans sees this as a way to avoid confronting the unclear product-market fit. If users can’t think of what to do with it in their daily lives, it indicates the product has yet to fundamentally change their routines.

The company’s launch of advertising projects, partly to cover costs for over 90% of non-paying users, is more strategically aimed at providing the latest, most powerful (and most expensive) models to deepen user engagement. However, Evans questions whether giving users better models today or this week will truly change their lack of use.

Platform Strategy Is Questionable: Lacking True Flywheel Effect

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

Evans considers the flywheel a clever, coherent strategy: capital expenditure creates a virtuous cycle, forming the foundation for building a full-stack platform company. Starting from chips and infrastructure, each layer of the tech stack is built upward, helping others use your tools to create their own products. Everyone uses your cloud, chips, and models, and higher layers 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. The flywheel diagram does not truly depict 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. OpenAI claimed a future commitment of $1.4 trillion and 30 gigawatts of compute (without a clear timeline), but actual usage by the end of 2025 was only 1.9 gigawatts. Lacking large-scale cash flow from existing business, the company relies on financing and leveraging others’ balance sheets (partly involving “recurring revenue”) to meet these goals.

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

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

Lack of Product Control: Strategy Is Lab-Driven

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 we use it in chat? How does it fit into our enterprise products?’”

This contrasts sharply with Steve Jobs’ famous 1997 statement: “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 roadmap, and your ability to set product strategy is very limited. You wake up in the morning to find the lab has made some discovery, and your job is to turn it into a button. The strategy is happening 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 activities as a sign that Sam Altman is acutely 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 “do everything simultaneously, execute immediately.” Application platforms, browsers, social video apps, collaborations with Jony Ive, medical research, advertising, and more. Evans sees some as “full-scale assaults” or simply rapid recruitment of aggressive talent. Sometimes it feels like copying the form of successful previous 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: “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 system more, regardless of what the system actually does? Microsoft, Apple, Facebook, and Amazon once had this ability.

He suggests a good way to interpret Bill Gates’ quote is that a platform truly leverages the entire tech industry’s creativity, allowing you to build more at scale without inventing everything yourself, all within your system and under your control. Foundation models are indeed multipliers, enabling the creation of many new things. But is there a reason everyone must use your product, even if competitors have built similar ones? Is there a reason your product must always be better, regardless of how much competitors invest?

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