AI technology is facing challenges in monetization due to escalating costs. Companies like Microsoft, Google, and Adobe are experimenting with different approaches to create, promote, and price their AI offerings. These costs also affect enterprise users and can lead to high prices for AI workloads. Different strategies for AI monetization include enhancing productivity, hardware sales, subscriptions, APIs, and integrating AI into existing products. Open-source AI training is becoming cheaper and more accessible, but concerns about energy consumption and hardware shortages remain. The future of AI monetization is uncertain, with competing narratives about its development and potential obstacles.
Can the tech industry overcome the challenge of AI monetization?
AI is becoming increasingly popular, but monetizing the technology is proving difficult due to high costs. State-of-the-art AI tools require expensive servers and consume a lot of power. Companies like Microsoft, Google, and Adobe are experimenting with different approaches to create, promote, and price their AI offerings.
Challenges for commercial and enterprise users
Commercial models like ChatGPT and Bard already limit the number of prompts per hour or day. Adobe sets monthly use caps and determines pricing based on usage. Enterprise users also face high costs, which has led to dissatisfaction among customers.
Chris Young from Microsoft believes that it will take time for companies and consumers to understand how they want to use AI and what they are willing to pay for it. The challenge now is to translate the excitement and interest in AI into actual adoption.
Why is AI difficult to monetize?
AI doesn’t always offer the cost-saving benefits of standard software because it requires specific computations for each task. Each interaction with an AI model draws energy and incurs ongoing costs. As adoption grows, so do the costs, making it challenging for companies to offer AI services at flat rates.
For example, Microsoft’s GitHub Copilot, an AI assistant for programmers, has high operational costs and hasn’t been profitable. Microsoft charged a subscription of $10 a month for the service and incurred an average loss of more than $20 a month for each user.
AI monetization strategies
Tech companies are approaching the challenge of AI monetization in various ways:
- Enhancing productivity: AI can significantly boost net societal productivity by automating repetitive tasks and allowing professionals to focus on higher-level functions.
- Hardware sales: The demand for AI technologies has created a need for specialized hardware, such as GPUs.
- Subscriptions: Offering AI enhancements as part of subscription packages is a common way to generate revenue from public users.
- APIs: For enterprise users, businesses like OpenAI utilize token-based billing for their APIs, ensuring fair and transparent pricing based on computational load.
- AI as an inherent feature: Some companies integrate AI capabilities into their products without immediately charging extra, aiming to first boost the product’s inherent value.
- Open-source AI training: Open-source models are becoming cheaper and more accessible, allowing businesses to customize models and keep workloads private.
AI’s future challenges
The AI industry faces challenges related to energy consumption, hardware shortages, and monetization. The energy consumed by the AI industry is predicted to be equivalent to that of a small nation by 2027. Data centers already consume over 1% of global electricity, and water usage by tech giants like Microsoft and Google is increasing due to AI-related workloads.
The future direction of AI is uncertain. While there are challenges in monetizing AI, there are also opportunities for open-source AI solutions that are cheap and agile. Progress in fields like energy production and low-power AI architecture could help overcome current bottlenecks and continue the rapid development of AI.
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