High costs and low profits threaten artificial coding startups

Launching a startup in the field of artificial intelligence has its own challenges. One of the main challenges facing startups in this field is the high cost of profit margins.

Despite all the popularity of coding artificial intelligence assistants, backup startups of these models can be very harmful in practice. Some informed sources have said that the development of such tools may be so costly that their gross profit margins are very negative; That is, the cost of executing the product is more than the amount that a startup can receive from the customer.

Artificial Intelligence startups make very little profit

Last February, it was announced that Windsurf’s artificial intelligence startups, which are active in coding, are negotiating to attract a large value of $ 1.5 billion led by Kleiner Perkins Investment Company. This was twice the value of the startup six months ago. However, the deal never finally came to fruition, and in April it was said that Windsurf intended to sell it worth about $ 2 billion to Openai.

The low profit of artificial intelligence startups is due to the high costs of using large language models (LLM). Coding artificial intelligence assistants are specifically under pressure to always deliver the latest, most advanced and most expensive models, as the makers of the models optimize their latest versions in order to improve coding and relevant tasks such as disruption.

This challenge is more complex with the intense competition in the interactive coding market and the coding assistants. There are competitors in this area that already have a very large customer base, such as Cursor from Anysphere and GitHub Copilot.

The easiest way to improve the profit margins in artificial intelligence startups is to build proprietary models by startups themselves, thus eliminating payment costs to foreign suppliers such as Anthropic and Openai. But this also has its own risks, including the high cost of building a dedicated model and related technical challenges.