AI can facilitate business processes, thereby cutting costs. The irony is that AI per se is very costly.
If AI is scaled up, the costs rise — on account of capital investments in data centers and acquisition of costly GPUs. these costs cannot be passed on to the clients. AI tools are not economical.
Even the cost of deployment of AI into a company’s systems costs a lot — ranging from $5 million to $20 million.
The silver lining is that AI costs are declining apparently, and the gap between investments and returns is narrowing.
The crux of the issue is the benefits AI can offer. Ther is continuing investment for the growth of AI. The companies prefer overinvestment, rather than err by underinvestment.
Over a period of time, the costs of training AI models have risen. These too are coming down. Gemini from Google is a powerful model but is available at a lesser cost. Similarly, OpenAI’s GPT-$o is faster, but is available at half the cost of the previous model GPT-4 Turbo.
The cost of accessing the models has dropped substantially — this is measured by processing of tokens.
Researchers focus on techniques such as ‘sparsity’ and ‘quantization’ to do the cost cutting.
Even enterprises now focus on choosing the right model for their needs, not necessarily the costliest model. They can opt for open-source models or smaller models.
Silicon Valley in past has accepted a margin hit in order to grow the market share. The aim is to be competitive, and skim the market later.
However, the issue is how the benefits of generative AI outweigh its costs for the businesses. Some analysts suggest a 30 per cent project drop by the end of 2025. If generative AI remains confined to summerization and deployment of chatbots, it may not be worth even the lower price tags. AI compnies will have to ponder over this issue.
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