Within no time, the project would be seen as a failure because no dirt was moved to the new location. Did the workers fail? Did the hammers malfunction? Or is management to blame because it forced them to use an absurdly wrong tool? Or what if the project involved moving dirt 9,000 miles away to an overseas location — and management only allowed the use of trucks?
The biggest data issue with genAI is the lack of reliability. That comes from a variety of factors, everything from hallucinations, to bad training data, bad fine-tuning, misinterpreted queries, badly phrased queries, a lack of proper data weighting (where low-quality sources are given the same credibility as high-quality ones) and other factors.
But someone who understands those realities can still get a ton of useful information from the technology. It simply has to be independently verified. I’ve used genAI tools for math problems, but I always verify the answer with a legacy calculator. I will also use it for research — but only as a pointer. Every detail must still be verified. Think about investor call transcripts. You can use genAI to find a statement, but you still have to find a copy of the original audio on a high-reliability site and listen to the passages to verify the transcript is correct.
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