The OpenAI Dilemma

Enterprise Customers and Productivity Tools

From the Wall Street Journal on March 16th:

“We cannot miss this moment because we are distracted by side quests,” Simo told staff last week, according to remarks reviewed by The Wall Street Journal. “We really have to nail productivity in general and particularly productivity on the business front.”

According to the Wall Street Journal, OpenAI’s leadership, including CEO Sam Altman and chief research officer Mark Chen, is actively determining which product lines to deprioritize. The era of launching Sora, Atlas, an eCommerce layer, and a Jony Ive hardware device simultaneously is over. OpenAI is going after the enterprise.

The obvious question is: why? OpenAI has more than 800 million weekly active users. It is, by almost any measure, one of the fastest-growing consumer products in history. Why would a company with that kind of distribution pivot away from consumers and toward selling software to businesses?

The answer has less to do with OpenAI’s strengths than with the fundamental reality that monetizing AI for consumers and for enterprises requires two entirely different businesses.

For most users today, AI acts as a productivity tool. It drafts emails, writes code, summarizes documents, and analyzes data. OpenAI’s enterprise report confirms this, reporting that AI has improved the speed or quality of output for 75% of surveyed enterprise workers, with average time savings of 40-60 minutes per day.

Historically, enterprise customers have been the largest customer segment for productivity tools. This pattern is remarkably consistent. Notion started as a note-taking app for consumers. But today, it generates $600 million in annual revenue, with approximately 90% of its business coming from team and enterprise usage. Similarly, both Dropbox and Figma started as tools for individual consumers, but eventually pivoted into the enterprise. For productivity tools, consumer adoption only creates awareness, but it is enterprises that write the checks.

The reason is straightforward: enterprises are more rational than consumers. A company can put a precise dollar value on the time a productivity tool saves each user. Let’s say there is a productivity tool that saves each user one hour per day. For a company that pays its employees $30 an hour across 20 days per month, the hour saved per day is worth $600 per user per month in recaptured labor. So if the tool costs less than $600 PUPM, the purchase justifies itself for the company.

Consumers, by contrast, rarely pay for productivity tools because for consumers, productivity isn’t measured in monetary terms. Imagine the same tool that saves each user an hour per day. How much would you pay for that hour? For most consumers, the answer is $0. Fundamentally, consumers are not businesses that need to optimize for productivity; therefore, they have little incentive to pay for productivity.

This fundamental asymmetry in consumers’ and enterprises’ willingness to pay for productivity tools is the most important dynamic for understanding where a significant portion of AI revenue will come from, and it points squarely at the enterprise.

Consumers and AI

None of this means consumers don’t matter. They are a massive segment of AI users. Consumers are rapidly integrating AI into their daily lives, using it for everything from drafting emails to planning meals to asking for life advice. After all, ChatGPT alone has 800 million weekly active users, clear evidence of the size of the AI consumer market.

But the way to monetize those 800 million consumers is fundamentally different from the way you monetize enterprise seats. Out of ChatGPT’s 800 million weekly active users, only 5% actually pay for a subscription. Consumers simply don’t pay for productivity tools with money. They pay with their attention, which means they pay through ads.

The analogy here is Google. Imagine if, in 2002, Google had launched as a $20-per-month subscription search engine. It would never have become the company it is today. Search can be thought of as a kind of productivity tool that helps the user find information faster. However, consumers don’t value productivity monetarily. Instead, Google monetized those consumers by building the most lucrative ad business in history. In FY 2025, Google generated over $264 billion in total ad revenue, with search accounting for roughly $200 billion.

The reason this works is that consumers, at the moment of a Google search, are expressing intent. They’re close to a decision: what to buy, where to eat, or which product to choose. That intent is extraordinarily valuable to advertisers, valuable enough that advertisers will effectively subsidize the consumer’s access to Google in exchange for reaching them at their moment of decision.

AI can be an even better version of this. When a user asks ChatGPT what the best running shoe for a marathon is, they are expressing higher-quality intent than in a typical Google search. A Google search for ‘best marathon running shoes’ tells an advertiser that this user is interested in running shoes. A conversation with a chatbot might reveal that the user is a beginner training for their first marathon, has flat feet, runs on pavement, and has a $150 budget. Essentially, ChatGPT can assemble a customer profile in real time through natural dialogue. This massively increases the advertisers’ ability to target their ideal customer profile, resulting in ads with far better returns.

OpenAI’s Dilemma

For OpenAI, it is caught between two enormous opportunities that require two entirely different capabilities, and in the short term, it has to choose.

On the consumer side, the TAM is staggering. Google’s search ads alone generate over $200 billion in annual revenue. If OpenAI can capture that consumer market, it would dwarf enterprise revenue. Truist estimates OpenAI will generate under $1 billion in ad revenue this year and scale to over $30 billion by 2030, a 134% CAGR. And search advertising is built around a powerful flywheel: as people use the product, they generate data that can be used to improve responses as well as ad targeting. This attracts both more users and more advertisers, which generates more data, and the flywheel continues. This is the exact mechanism that allowed Google to own more than 90% of the search market for over two decades.

However, building and scaling an ad business is extraordinarily difficult. Google launched AdWords in 2000. And it took years of building auction systems, self-serve advertiser tools, and an entire organizational apparatus dedicated to ad tech for AdWords to become the core of a $200 billion business. ChatGPT launched ads in February 2026 with a $200,000 minimum buy-in and PCMs around $60, roughly three times Meta’s average rates. While the high buy-in amount already deters some advertisers, early reports also suggest low click-through rates and glitches, further disincentivizing advertisers. Ultimately, building an ad engine is a years-long organizational build and not a quarter-over-quarter revenue story.

On the enterprise side, the opportunity is more immediate, but the competition is also more intense. Unlike running advertisements, serving enterprises does not require building an entirely new revenue engine that needs to be optimized over multiple years. Instead, it’s about implementation, integration, and go-to-market execution. But the competition is fierce. OpenAI generates approximately $10 billion of its roughly $25 billion annualized revenue from enterprise customers. But its enterprise market share has been falling, dropping from around 50% in 2023 to approximately 27%. Meanwhile, Anthropic’s enterprise market share grew from 12% in 2023 to 40% in 2025. Out of Anthropic’s $19 billion of ARR, roughly 80% came from enterprise clients.

Unfortunately for OpenAI, it probably cannot pursue both the consumer market and the enterprise at full intensity simultaneously. There are real limitations to the amount of compute available, which means OpenAI must make real tradeoffs when considering where to allocate that compute. However, the deeper issue for OpenAI is organizational focus. An advertising engine and an enterprise platform require fundamentally different teams, different product priorities, different sales motions, and different cultures, especially at the scale OpenAI hopes to operate at. Ultimately, this is the fundamental reason OpenAI has decided to deprioritize side projects and refocus on a single business model.

For OpenAI, with a potential IPO as early as Q4 2026, the pressure to show high-quality revenue growth is immediate, and enterprise revenue is the fastest path to demonstrating high-quality revenue. Enterprise software revenue is predictable, high-margin, and defensible, which means Wall Street is willing to underwrite a much higher multiple on enterprise subscription revenue than on an early-stage ad business with unproven unit economics. And the opportunity for OpenAI to capture that revenue is fleeting. Anthropic’s accelerating enterprise dominance has created a now-or-never urgency. Every day that OpenAI doesn’t aggressively pursue enterprise is a day where Anthropic’s integrations get deeper, switching costs get higher, and its lead gets harder to close.

The Connection Between Enterprise and Consumer

From Ben Thompson on last week’s Sharp Tech podcast:

The size of [the enterprise] market may be larger than Adrian is giving it credit for, and that may be a sufficient flywheel to then go into the consumer market. And there is an analogy for this, which is Microsoft in the 80s and 90s. Microsoft was an enterprise company; they got the Windows flywheel going in enterprise and basically won the consumer market for free

Windows dominated the enterprise in the 1980s. Workers grew familiar with it at the office and saw no reason to pay for a different operating system at home, so Windows became the consumer default by sheer inertia. However, this analogy breaks down when you consider the fundamental economics of AI.

Windows won both markets because the capability it provided was fundamentally the same in both contexts. Whether it was running applications, managing files, or navigating a desktop, the experience was identical whether you were sitting in a corporate office or at your kitchen table. The difference between Windows for the enterprise and Windows for consumers was licensing and IT administration, not the core product. An accountant running spreadsheets at work and a teenager playing games at home were using the same operating system in the same way

AI is not like this. The value AI provides to enterprises and the value it provides to consumers are fundamentally different. For enterprises, AI is a productivity tool that either replaces or augments labor. The product needs deep integrations like Slack, SharePoint, GitHub, Salesforce, plus security compliance, auditability, and enterprise-grade reliability. The go-to-market is a sales-led motion with long cycles and high average contract values.

For consumers, the value AI provides is closer to Google Search than traditional enterprise productivity tools like Microsoft Office or Asana. It’s a search engine, a decision-making tool, and, increasingly, a source of entertainment. The product needs great conversational UX, broad knowledge, ecommerce features, and an ad infrastructure that can monetize intent at scale. The go-to-market is product-led with zero marginal revenue per user, and monetization happens through a third party: the advertiser.

These aren’t two versions of the same business. They’re two different businesses that happen to share an underlying technology, which means winning one does not hand you the other. Therefore, for OpenAI, the choice between enterprise and consumer is a real choice and not just a sequencing question where you win one first, and the other follows. And with a potential IPO on the horizon and an ad business still in its infancy, OpenAI has chosen the enterprise.

Whether that’s the right long-term call is genuinely uncertain. The risk is that OpenAI trades a potentially dominant consumer position, one with hundreds of billions of dollars of TAM, for a position in enterprise where it’s playing catch-up against a competitor that already has a meaningful lead. Meanwhile, Google, a major competitor, already has a mature ad infrastructure and its own bid to win the chatbot market in Gemini. For OpenAI, the consumer window may not stay open forever.

But the IPO clock is ticking, and enterprise revenue is the kind of revenue that bankers and investors are willing to underwrite. Sometimes the right strategic choice isn’t the biggest opportunity; it’s the one you can actually execute on in time.