OpenAI, the billion-dollar startup that initiated the generative AI revolution with ChatGPT, is projected to incur a $5 billion loss in 2024. Despite this negative figure, the company was recently reported to be involved in talks about raising more money, with its valuation said to soar to $100 billion after a $1 billion injection.
Remember that this is just one company training its AI models, with several more that are witnessing the same financial crunch. Artificial intelligence continues to remain the hottest trend in the technology industry, but it is thoroughly volatile and can burn through cash like there is no tomorrow. A panel of scientists and engineers have estimated that 80 percent of these projects fail and highlight the reasons why, along with providing some remedies.
One reason for the failure of AI projects is that company founders fail to understand which problem requires solving and are only focused on showcasing the technology to others
A U.S. non-profit global policy think tank, research institute, and public sector consulting firm called RAND Corporation has highlighted five reasons why 80 percent of AI projects fail. The first and foremost reason is that ‘industry stakeholders’ misunderstand what problem needs to be solved during AI. Another reason for failed projects is that companies lack adequate data to effectively train an AI model, leading to skewed results that discourage users from using the platform again.
More issues, such as an inadequate infrastructure can expedite the AI project’s failure rate, and assuming that resources are plentiful, company founders are more focused on displaying technological superiority against the competition rather than providing value to users. While you can check out the remaining reasons that accelerate a project’s downfall, the RAND Corporation has provided some solutions to mitigate the risks of failure.
One of them is investing in the infrastructure, as focusing on this area not only reduces the time it takes to complete training the AI model, but it can also present one major advantage; high-quality data available to effectively train other AI models. Founders should also understand that artificial intelligence is not a magic bullet and has its limitations.
While effectively training an AI model can result in a more potent product, we have ChatGPT as a worthy example since it is trained on terabytes of data and can still produce an incorrect result. A total of seven remedies are mentioned in the report, so you can go through all of them and let us know in the comments if you agree with these solutions.
News Source: RAND
About the author: Omar Sohail is a reporter and analyst for Wccftech's mobile section, specializing in the technology and business of the mobile industry. His expertise lies in the intricate hardware supply chain, covering developments in semiconductor manufacturing, chip lithography, and camera sensor technology.
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