OpenAI falls short of financial targets as competitors chip away at share

The news: OpenAI missed sales and year-end user targets, leading to concerns about its future.

  • OpenAI has missed multiple revenue targets so far this year, according to sources familiar with its finances, per The Wall Street Journal.
  • The company fell short of its goal of 1 billion users by the end of 2025. As of February 27, it had 900,000 million users, per a blog post.

CFO Sarah Friar shared concerns recently about IPO plans and told company leaders that OpenAI might not be able to afford further computing contracts if revenues don’t increase, per internal sources. However, she and CEO Sam Altman released a joint statement Monday: “We are totally aligned on buying as much compute as we can and working hard on it together every day.”

Why it matters: OpenAI’s ChatGPT is no longer the default genAI. Google’s Gemini and Anthropic’s Claude have boosted their market share in the past few months.

Amid the rising competition, OpenAI cut “side quests” and models, including video generator Sora, to focus on business and productivity tools.

The challenge: OpenAI needs to increase its revenues, whether through consumer subscriptions, enterprise accounts, or advertising.

  • 50 million of ChatGPT’s weekly active users are paying subscribers.
  • Enterprise accounts make up 40% of its revenues, which total $2 billion a month, according to OpenAI.
  • Earlier this month, the company forecast $2.5 billion in ad revenues this year and $100 billion by 2030. Within six weeks of launching, its ad pilot surpassed $100 million in annualized revenues.

OpenAI said it would burn through $155 billion in cash through 2029, per The Information, an $80 billion increase over its previous projections.

What this means: The company can continue to hold funding rounds to boost its valuation and capital, but there will be a breaking point. As other AI models improve and potentially surpass ChatGPT in output and popularity, OpenAI could be left behind. Its lack of industry expertise and “spend, spend, spend” mentality could be its downfall.

Recommendations for brands: There are no sure things in the AI race, so best bets involve spreading budgets across the field and testing output to determine which models offer the tools needed for your specific tech stack. The most popular model might not be the one that works best for you.

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