The Business Model of AI Startups

The business model surrounding artificial intelligence (AI) is a captivating and complex topic. With the emergence of OpenAI APIs, the possibilities for integrating AI into various applications have expanded asignificantly. However, unlike the standards Software-as-a-Service (SaaS) model, which has matured enough to implement paywalls and upfront fees, the AI world presents a different challenge.

The AI-as-a-service Business Model

In the AI landscape, executives, marketers, and knowledgeable individuals are already well-versed in using AI technologies such as ChatGPT. They are open to testing new products and potentially paying for them. However, the barrier to entry for creating an AI-powered app is incredibly low, resulting in a flood of applications entering the market.

Consequently, it becomes increasingly difficult to have that “ahah” moment where users recognize the value of a particular AI solution and are willing to pay for it. People are eager to try out different options because the market itself is not yet mature. Additionally, there is uncertainty surrounding the capabilities of various AI models, such as the significant difference between chatGPT 3.5 and chatGPT 4. Even individuals who are well-informed and seeking an AI-powered solution, like a ghostwriter for their LinkedIn profile, will likely request free trials before making a commitment. They may test multiple AI-based options before settling on a final choice.

Considering the business model behind AI, it becomes apparent that offering free trials is essential. By allowing users to test and experience the benefits of a service, companies can attract and hook their audience. A prime example is chatGPT, which garnered millions of users within just a few days by offering free access to chat GPT 3.5.

Using freemium to kickstart your AI startup

For those building AI apps, regardless of their niche, adopting a freemium model is crucial for rapid user acquisition. Once a substantial user base is established, companies can then monetize their offerings through subscription-based models or other forms of payment. One good example is OpenAI offering the GPT3.5 but you have to paid to access their last model GPT4.

However in no way you should make it 100% free and expect revenue to comes later because of delayed learning and cost of infrastructure. However, in the midst of the AI hype, you can build a 100k users in few days but realize too late that they came not because you had a superior product but just for the free tier that was handy for testing. ChatGPT already paid the price with only few months after the release, the massive churn reduced their user base by 9.7%.

It is worth noting that even Midjourney don’t offer a free tier, you have only 25 images to decide wether you want to use their service. They success was however inspired by another advantage embedded into their business model : their community.

Community-driven business model for your AI product

Most of the early AI Startups like Midjourney and OpenAI resolved around communities for different (good) reasons :

  • Midjourney was one of the most early iteration of image generation and they discover that people did not knew what their AI for capable of. By enforcing public use of their AI on Discord, it acted like a tutorial for newbies and a showcase for curious and unconvincing users. Additionally they leveraged their free-users by offering “Earn Free Hours” option by making them label their data. A very smart move for B2C-based startups.
  • OpenAI with their parternship with Microsoft understangly focused on building a B2B community, i.e. through their dev forum. Behind this idea, OpenAI positionned itself as the by-default leader for back-end AI integration. However this strategy and business model was slowed down by the fact that OpenAI used users data for training their models which made some compagnies like Apple and Shopify banned completely these services.
  • HuggingFace, the github of ML models, use their opensource trademark to be the central hub of model repository. Scientist and corporate alike deploying their models into their platform for free. Meanwhile their business models seems emerge from partnerships, i.e. AWS, IBM or AMD, allowed by their central position in the AI field.

Not all AI platform deserve to build a community, and it is a expansive way to grow if you are not willing to go all-in in that strategy by embedding your community into your business model in the way shown before.

The alternative to AI-as-a-service ?

For those who are prepared to discart the recurring factor of subscription based services, It is worth mentioning that some SaaS applications have recently moved away from monthly payment structures and towards one-time fees, similar to the 90s software model. One such example is FeedHive and their application, LinkDrip. It can be hugely lucrative to build a small dedicated community of evangelists. However, caution must be exercised, especially due to the high cost of Language Model Licensing (LLM). It is advisable to ensure that the infrastructure costs are recovered before considering a transition away from the chatGPT API. Moreover, exploring open-source models available through platforms like HuggingFace endpoints might also be a viable option for cost optimization in the long run.

LinkDrip built their early community by offering a lifetime deal. It has the advantage of bringing a huge cash-flow while testing the potential of your idea.

Meanwhile, numerous mobile applications have found success in building mainstream business-to-consumer (B2C) services by adopting a business strategy that propelled several gaming companies to prosperity. This approach combines the use of micro-transactions and advertising, offering users access to premium features or content for a small fee while generating additional revenue from in-app ads. For instance, think of how a game might offer players the ability to purchase exclusive items or bypass waiting times with micro-transactions. Simultaneously, they might show advertisements to free users, driving revenue on both fronts. This dual-income stream model allows companies to cater to both paid and free users, thus expanding their user base while also ensuring monetization.

In summary, the discussion surrounding the business model for AI centers around the importance of offering free trials to entice users and gain their trust. Building a significant user base through a freemium model is crucial before monetizing the service through subscriptions or alternative payment options. While some companies have experimented with one-time fees, careful consideration is necessary to ensure infrastructure costs are covered and long-term sustainability is maintained. Additionally, exploring open-source alternatives can provide cost efficiencies. As the AI market continues to evolve, businesses must remain adaptable and find innovative ways to strike the balance between user acquisition and profitability.

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