The "spreadsheet" example video is kind of funny: guy talks about how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... I just needed to copy / paste a few things. If it can do 90 - 95% of the time consuming work, that will save you a ton of time"
It feels like either finding that 2% that's off (or dealing with 2% error) will be the time consuming part in a lot of cases. I mean, this is nothing new with LLMs, but as these use cases encourage users to input more complex tasks, that are more integrated with our personal data (and at times money, as hinted at by all the "do task X and buy me Y" examples), "almost right" seems like it has the potential to cause a lot of headaches. Especially when the 2% error is subtle and buried in step 3 of 46 of some complex agentic flow.
It feels like either finding that 2% that's off (or dealing with 2% error) will be the time consuming part in a lot of cases. I mean, this is nothing new with LLMs, but as these use cases encourage users to input more complex tasks, that are more integrated with our personal data (and at times money, as hinted at by all the "do task X and buy me Y" examples), "almost right" seems like it has the potential to cause a lot of headaches. Especially when the 2% error is subtle and buried in step 3 of 46 of some complex agentic flow.