OpenAI scales back side projects and bets on coding and the enterprise market

OpenAI is preparing a notable strategic shift: the company no longer wants to storm all possible AI markets simultaneously and plans to set priorities more strictly. The main goal — not to lose pace in the race with Anthropic and other competitors who are faster at turning AI products into viable business.

Why the course is changing

After ChatGPT's explosive success in autumn 2022, OpenAI could afford almost everything at once: launch new consumer scenarios, test video, think about a browser, trading tools and even hardware devices. Such an approach helped the company establish itself as the main symbol of generative AI, but as the market matured, this approach showed its downside. When a team tries to cover too many directions, some resources go not into strengthening the core, but into experiments that are difficult to monetize quickly and even harder to integrate into a unified strategy.

In an internal meeting, employees were given to understand that the time of broad expansion is ending. Leadership led by Sam Altman and Mark Chen is reviewing the portfolio of initiatives and determining which ones should no longer be considered priorities. Inside the company, this is explained simply: OpenAI cannot miss the moment while the market is distributing leadership in the most lucrative segments — among developers and corporate clients.

"We cannot miss this moment because secondary tasks distract us."

OpenAI's new bets Two main directions now look obvious: code generation and products for business.

This is where competitors started pressing especially hard. Anthropic significantly strengthened through developer tools and corporate deployment, and these are segments where companies are more willing to pay large checks and remain customers longer. For OpenAI, this is no longer a question of image, but a question of revenue, market share and preparation for the next major stage of growth.

Therefore, OpenAI wants to quickly regain initiative among professional audiences. This should be helped by fresh releases like Codex and the GPT-5.4 model, which is more tailored to work and engineering scenarios.

Codex, according to company internal data, is already used by more than two million people per week — roughly four times more than at the beginning of the year. In parallel, OpenAI is strengthening contact with clients and external consultants to develop models not only from laboratory logic, but also for real business tasks. Special emphasis is placed on productivity.

The new strategy boils down not to an abstract "do more AI," but to users — especially corporate ones — writing code faster, automating routine and getting measurable benefits. In this sense, OpenAI is trying to transform from a company that launches many loud experiments into a company that systematically turns its models into a working tool.

What will fade into shadow This doesn't mean OpenAI will immediately close all ancillary products.

But some initiatives will clearly receive less attention, resources, or independence. Last year the company managed to enter several disparate directions at once, and now these are the first to come under review. A telling example is Sora: after a separate launch, interest in the product declined, and now the service is to be more deeply integrated into the ChatGPT ecosystem instead of being developed as a standalone application.

Under pressure from the new strategy, the following may move to the background: separate consumer experiments that do not provide rapid revenue growth; standalone applications outside the core ChatGPT; projects at the intersection of e-commerce and AI-assisted shopping; browser and hardware initiatives, if they don't directly strengthen the core business. This shift is influenced not only by competition, but also by financial logic. OpenAI, as reported, is considering going public before the end of the year, and in such a situation companies typically need to show clear discipline: exactly where they earn, which directions scale and why more investment should go there.

For OpenAI leadership, this means less scattering and more focus on products that are easier to sell to large clients and simpler to explain to investors.

What this means

OpenAI is entering a more mature phase: the era of endless AI experiments gives way to competition for specific markets and stable monetization. For the industry, this is a signal that the main battle in the coming time will unfold not around the most impressive demos, but around code, corporate scenarios and who first transforms AI into an essential working layer for business.