[arbfay]

That is not true unfortunately.

ML has been around for decades, DL for more than a decade.

In 2019, I had to explain to executives that 95% of AI projects fail (based on some other survey), top 1 reason is bad or missing data and top 2 is misaligned internal processes. I probably still have the slides somewhere.

One project I worked on was impossible because the data was so bad that after cleaning, we went from 4M rows to 10k usable rows in 4 languages. We could have salvaged a lot more if we restricted the use case but then the benefits of the projects would be not so interesting anymore. The internal sponsor gave up and understood the problem. Instead, they decided to train everyone on how to improve data entry and quality! In just 6 months I could see the data was getting better indeed. But I had to leave this company, the IT dep was too toxic.

So I think the author is right. According to Scale, we'd have gone from 95% failures to 95% successes in just 4-5 years just thanks to LLMs? This is of course ridiculous, knowing the problem was never poor models.