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Silicon Valley has made a big bet on generative AI but it is not entirely clear whether this bet will be successful or not. A new report from the Wall Street Journal claims that, despite endless hype around big language models and the automated platforms they power, tech companies are struggling to turn a profit in terms of AI.
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For example, Microsoft, which has bet big on the generic AI boom by investing billions in its partner OpenAI, is losing money on one of its key AI platforms. GitHub Copilot, which launched in 2021, was designed to automate parts of coders’ workflows and, despite being extremely popular with its user base, has been a big “money loser”, Journal report. The problem is that users pay a $10 per month subscription fee for Copilot, but Microsoft lost an average of $20 per user during the first few months of this year, according to a source interviewed by the Journal. The source told the newspaper that the company loses an average of more than $80 per month due to some users.
Other platforms are also going through similar financial difficulties. For example, OpenAI’s ChatGPT is seeing a steady decline in user base, while its operating costs remain incredibly high. A Washington Post report in June claimed that chatbots like ChatGPT lose a lot of money every time a customer uses them.
There are diverse reasons why AI businesses are struggling, but one is well-known: AI platforms are extremely expensive to operate. Platforms like ChatGPT and DALL-E run through a Huge The amount of computing power and companies are struggling to figure out how to reduce that footprint. Additionally, the infrastructure to run AI systems—such as powerful, high-priced AI computer chips—can be quite expensive. Meanwhile, the cloud capacity needed to train algorithms and run AI systems is also growing at an alarming rate. All this energy consumption also means that AI is about as unfriendly to the environment as you can get.
To avoid the fact that they are wasting money, many technology platforms are experimenting with different strategies to cut costs and computing power while still providing the types of services they need. He has made a promise to the customers.
Still, it’s hard not to see this whole affair as a stumbling block for the tech industry. AI is not only a solution in search of a problem, but it is also increasingly becoming a problem in search of a solution. Companies need to figure out how these devices make sense both economically and strategically – something that probably should have been sorted out before launch.