Launching a startup in the field of artificial ielligence 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 ielligence assistas, backup startups of these models can be very harmful in practice. Some informed sources have said that the developme 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 amou that a startup can receive from the customer.
Artificial Ielligence startups make very little profit
Last February, it was announced that Windsurf’s artificial ielligence startups, which are active in coding, are negotiating to attract a large value of $ 1.5 billion led by Kleiner Perkins Investme Company. This was twice the value of the startup six mohs ago. However, the deal never finally came to fruition, and in April it was said that Windsurf iended to sell it worth about $ 2 billion to Openai.
The low profit of artificial ielligence startups is due to the high costs of using large language models (LLM). Coding artificial ielligence assistas 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 releva tasks such as disruption.
This challenge is more complex with the iense competition in the ieractive coding market and the coding assistas. 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 ielligence startups is to build proprietary models by startups themselves, thus eliminating payme costs to foreign suppliers such as Ahropic and Openai. But this also has its own risks, including the high cost of building a dedicated model and related technical challenges.
