A recent in-depth analysis by former a16z partner and renowned technology analyst Benedict Evans highlights four fundamental strategic challenges facing OpenAI, despite its apparent prosperity. Evans argues that while OpenAI boasts a massive user base and ample capital, it lacks a technological moat, suffers from weak user stickiness, faces rapid competition, and has a product strategy constrained by its laboratory's research direction—all of which threaten its long-term competitiveness.

Evans points out that OpenAI's current business model lacks a clear competitive advantage. The company possesses neither unique technology nor network effects. Of its 900 million weekly active users, only 5% are paying subscribers, and 80% of users sent fewer than 1,000 messages in 2025—averaging less than three prompts per day. This "a mile wide and an inch deep" usage pattern indicates that ChatGPT has not yet become a daily habit for users.

Simultaneously, tech giants like Google and Meta have caught up technologically with OpenAI and are leveraging their distribution advantages to capture market share. Evans believes the real value in the AI field will come from new experiences and application scenarios that have yet to be invented, and OpenAI cannot single-handedly create all this innovation. This forces the company to fight on multiple fronts simultaneously, deploying a comprehensive strategy from infrastructure to the application layer.

Evans's analysis reveals a core contradiction: OpenAI is attempting to build competitive barriers through massive capital investment and a full-stack platform strategy, but without network effects or user lock-in mechanisms, the effectiveness of this approach remains questionable. For investors, this means reassessing OpenAI's long-term value proposition and its true position in the AI competitive landscape.

**Erosion of Technological Edge: Intensifying Model Homogenization** Evans notes that currently about six organizations can launch competitive frontier models with roughly equivalent performance. Companies leapfrog each other every few weeks, yet none has established a technological lead that others cannot match. This contrasts sharply with platforms like Windows, Google Search, or Instagram, which achieved self-reinforcing market share through network effects, making it difficult for competitors to break the monopoly regardless of investment.

This state of technological parity could change with certain breakthroughs, most notably the achievement of continuous learning capabilities, but Evans believes OpenAI cannot currently plan for this. Another potential differentiating factor is the scale effect of proprietary data, including user or vertical industry data, but existing platform companies also hold advantages here.

With model performance converging, competition is shifting to branding and distribution channels. The rapid market share growth of Gemini and Meta AI confirms this trend—for the average user, these products appear largely similar, and Google and Meta possess strong distribution power. In contrast, while Anthropic's Claude model often tops benchmark tests, its consumer awareness is near zero due to a lack of a consumer strategy and product.

Evans compares ChatGPT to Netscape, which held an early lead in the browser market but was ultimately defeated by Microsoft's distribution advantage. He argues that chatbots face the same differentiation problem as browsers: they are essentially just an input box and an output box, with extremely limited room for product innovation.

**Fragile User Base: Scale Masks Lack of Stickiness** Despite OpenAI's clear lead with 800 to 900 million weekly active users, Evans notes this data obscures a serious user engagement problem. The vast majority of users who already know about and know how to use ChatGPT have not made it a daily habit.

Data shows only 5% of ChatGPT users pay, and even among U.S. teenagers, the proportion using it a few times a week or less is much higher than those using it multiple times daily. OpenAI's "2025 Year in Review" disclosed that 80% of users sent fewer than 1,000 messages in 2025, which, taken at face value, equates to an average of less than three prompts per day, with even fewer actual conversations.

This shallow usage means most users do not perceive differences in personality or focus between different models, nor do they benefit from features like "memory" designed to build stickiness. Evans emphasizes that memory features can create stickiness but not network effects. Meanwhile, usage data from a larger user base could be an advantage, but its significance is questionable when 80% of users only engage a few times per week at most.

OpenAI itself acknowledges a problem, citing a "capability gap" between model abilities and actual user usage. Evans views this as sidestepping the fact that product-market fit is unclear. If users cannot think of what to do with it on an ordinary day, it hasn't changed their lives.

The company launched an advertising program partly to cover service costs for the over 90% of non-paying users, but more strategically, this allows it to offer these users the latest, most powerful (and most expensive) models, hoping to deepen engagement. However, Evans questions whether giving users a better model will change the situation if they cannot think of a use for ChatGPT today or this week.

**Questionable Platform Strategy: Lack of a True Flywheel Effect** Last year, OpenAI CEO Sam Altman attempted to consolidate the company's initiatives into a coherent strategy, presenting a chart and quoting Bill Gates: "A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it." Simultaneously, the CFO released another chart illustrating a "flywheel effect."

Evans views the flywheel effect as an elegant, 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, building each layer of the technology stack upward, the higher you go, the more you help others use your tools to create their own products. Everyone uses your cloud, chips, and models, and then, at higher layers, the stack layers reinforce each other, creating network effects and an ecosystem.

However, Evans states bluntly that he believes this is not the correct analogy. OpenAI does not possess the platform and ecosystem dynamics that Microsoft or Apple once had. That flywheel chart does not actually depict a true flywheel effect.

Regarding capital expenditure, the four major cloud computing companies invested approximately $400 billion in infrastructure last year and announced at least $650 billion for this year. OpenAI claimed months ago to have commitments for $1.4 trillion and 30 gigawatts of computing power in the future (with no clear timeline), while actual usage at the end of 2025 was 1.9 gigawatts. Lacking large-scale cash flow from an existing business, the company achieves these goals through financing and using others' balance sheets (partly involving "recurring revenue").

Evans argues that massive capital investment might only buy a seat at the table, not a competitive advantage. He compares AI infrastructure costs to aircraft manufacturing or the semiconductor industry: no network effects, but each generation's process becomes more difficult and expensive, eventually leaving only a few companies able to sustain the required investment at the frontier. However, while TSMC has a de facto monopoly on cutting-edge chips, this does not give it leverage or value capture ability upstream in the technology stack.

Evans points out that developers had to build applications for Windows because it had almost all the users, and users had to buy Windows PCs because it had almost all the developers—that is a network effect. But if you invent a great new application or product using generative AI, you simply call a base model running in the cloud via an API; users do not know or care which model you use.

**Lack of Product Control: Strategy Constrained by the Lab** Evans begins this section by quoting OpenAI's Head of Product, Fidji Simo, from 2026: "Jakub and Mark set the long-term research direction. After months of work, something amazing emerges, and then the researchers contact me and say, 'I have something cool. How are you going to use this in chat? How for our enterprise product?'"

This stands in stark contrast to Steve Jobs's 1997 statement: "You've got to start with the customer experience and work back toward the technology. You can't start with the technology and try to figure out where you're going to sell it."

Evans contends that when you are the product lead at an AI lab, you do not control your own roadmap; your ability to set product strategy is very limited. You open your email in the morning to find out what the lab has produced, and your job is to turn it into a button. Strategy happens elsewhere, but where?

This issue highlights a fundamental challenge for OpenAI: unlike Google in the 2000s or Apple in the 2010s, OpenAI's smart and ambitious employees do not have a truly effective product that others cannot replicate. Evans suggests one interpretation of OpenAI's activities over the past 12 months is that Sam Altman is deeply aware of this and is trying to convert the company's valuation into a more durable strategic position before the music stops.

For much of last year, OpenAI's answer seemed to be "everything, all at once, immediately": application platform, browser, social video app, partnership with Jony Ive, medical research, advertising, and more. Evans believes some of these efforts look like "throwing everything at the wall," or simply the result of rapidly hiring many ambitious people. At times, it also gives the impression that people are copying the forms of previously successful platforms without fully understanding their purpose or dynamic mechanisms.

Evans repeatedly uses terms like platform, ecosystem, leverage, and network effects, but he acknowledges these terms are widely used in the tech industry with rather vague meanings. He quotes his university medieval history professor, Roger Lovatt: "Power is the ability to make people do what they do not want to do." That, he argues, is the real question: Does OpenAI have the ability to make consumers, developers, and businesses use its system more, regardless of what the system actually does? Microsoft, Apple, and Facebook once had this ability; Amazon did too.

Evans believes a good way to interpret Bill Gates's statement is that a platform truly leverages the creativity of the entire tech industry so you don't have to invent everything yourself; you can build much more, at scale, but all of it is done on your system, under your control. Base models are indeed multipliers; a lot of new things will be built with them. But is there a reason everyone must use your product, even if a competitor builds the same thing? Is there a reason your product should always be better than a competitor's, no matter how much money and effort they invest?

Evans concludes that without these advantages, all you have is daily execution. Executing better than everyone else is certainly an aspiration, and some companies have done it over long periods, even convincing themselves they have institutionalized it—but that is not a strategy.