So far, AI hasn’t been profitable for Big Tech

🌈 Abstract

The article discusses the challenges that Big Tech companies like Microsoft and Google are facing in turning AI products like ChatGPT into profitable enterprises. The key points are:

• Generative AI models used for creating text are not cheap to operate, as they require powerful servers with high-end, energy-consuming chips.
• The current cost challenge is tied to the nature of AI computations, which often require new calculations for each query, unlike standard software that enjoys economies of scale.
• Some companies are trying to dial back costs, while others continue to invest more deeply in the tech.
• Microsoft's GitHub Copilot, which assists app developers by generating code, has been operating at a loss despite attracting more than 1.5 million users.
• Using the most capable AI models like GPT-4 can be overkill for simple tasks, and companies are exploring less costly alternatives.
• Experts anticipate a more stringent financial approach in the near future, as the industry transitions from enthusiasm and experimental budgets to a phase where the focus will be on whether these AI models can actually contribute to company profitability.

🙋 Q&A

[01] The challenge of turning AI products into profitable enterprises

1. What are the key challenges that Big Tech companies are facing in turning AI products like ChatGPT into profitable enterprises?

• The cost of running advanced AI models is proving to be a significant hurdle, as generative AI models used for creating text are not cheap to operate.
• Large language models (LLM) like the ones that power ChatGPT require powerful servers with high-end, energy-consuming chips, making them expensive to run.
• The current cost challenge is tied to the nature of AI computations, which often require new calculations for each query, unlike standard software that enjoys economies of scale.
• This makes flat-fee models for AI services risky, as increasing customer usage can drive up operational costs and lead to potential losses for the company.

2. How are companies trying to address the cost challenges?

• Some companies are trying to dial back costs, while others continue to invest more deeply in the tech.
• Microsoft and Google have introduced more expensive AI-backed upgrades to their existing software services.
• Zoom reportedly tried to reduce costs by sometimes using a less complex in-house AI model for some tasks.
• Adobe is approaching the problem with activity caps and charging based on usage, while Microsoft and Google are typically sticking with flat fees.

3. What are the reasons for the high costs of AI services?

• Some companies have been reaching for the most powerful AI models available, like Microsoft using OpenAI's complex LLM, GPT-4, for many of its AI features.
• Using the most capable AI models can be overkill for simple tasks, and companies are exploring less costly alternatives.
• Over time, due to advances in AI acceleration hardware, the costs to operate these complex models will likely come down, but it's uncertain if the advancements can come soon enough to match this year's hype cycle over AI.

[02] The profitability of AI products

1. How has Microsoft's GitHub Copilot been performing financially?

• Microsoft's GitHub Copilot, which assists app developers by generating code, has been operating at a loss despite attracting more than 1.5 million users and integrating into nearly half of their coding projects.
• Users pay a flat fee of $10 a month for the service, but the cost to Microsoft exceeds $20 a month per user on average, and in some cases, individual power users have cost the company as much as $80 a month.

2. What is the outlook for the profitability of AI products in the near future?

• Experts anticipate a more stringent financial approach in the near future, as the industry transitions from enthusiasm and experimental budgets to a phase where the focus will be on whether these AI models can actually contribute to company profitability.
• According to May Habib, CEO of generative AI firm Writer, "Next year, I think, is the year that the slush fund for generative AI goes away," suggesting that the industry will move from a phase of enthusiasm and experimental budgets to a focus on the actual profitability of AI models.