Agree with your pov, especially the idea that you can only reject hypotheses, not confirm them.
What I keep seeing operationally is that teams are forced to act before hypotheses can be tested or falsified. For example, inspections completed, assets redeployed, and customers responded to. Only later are they asked to prove correctness.
When that gap shows up for you, what was the concrete trigger? An audit, a customer dispute, a safety review, or something else?
What I keep running into is that teams know the work is informal at the edge, but systems are designed as if formality can be enforced at capture time. In practice, that just pushes the work elsewhere.
In your experience, where does that mismatch hurt the most: audits, safety reviews, customer disputes, or something else?
I’m curious in the cases where that 1% mattered, what made the reconstruction painful even when the raw artifacts existed? Was it ordering events, understanding intent/decisions, or just finding the right things under pressure?
And were those moments tied to audits, disputes, safety issues, or something else?
@scott-iii That’s exactly the kind of situation I’m trying to understand better.
Out of curiosity, when you were piecing it together from the camera roll, what was hardest: ordering events, understanding why decisions were made, or just finding the right photos at all?
And was this tied to an audit, a customer issue, or something else?
I know some friends who were put on the RIF list for being critical of AI strategy in a division within a large company, whose CEO is talking about intelligent minds and diffusion.
Am better today, thank you :-). Your suggestions align with how we are thinking about addressing this problem. I like the suggestion around assistant vs admin. It strongly resonates with me and points us in a direction.
btw, multiple anecdotes are helpful, so thank you once again.
sorry a bug took me down, so could not reply earlier. Your comment hits the spot.
I agree with you about not just seeing raw numbers or some kind of trend line, but get more analytical insights into what is really happening with the data and the systems feeding that data. However, I am also skeptical that most humans are terrible at understanding stats so any information based on statistical analysis must be dumbed down to ELI5 level so most report consumers understand. How did you approach this problem?
sorry a bug took me down, so could not reply earlier. Thank you for your reply and a few follow-up questions and observations.
1. I do agree with you on the challenge between "feeling" vs "what i actually want". Is this about spending more time with the report consumer to understand what they want to see? Or is there something more?
2. How did you solve this problem? I would love to learn how you addressed this, despite all the struggles.
3. If you were to revisit this same problem today, what would you do differently?
Not just arsenic but also to remove the starch. Most folks don’t cool down the rice before eating so it’s preferable to remove the starch before eating.
Humans are the exception. Most animals return home as it gets dark. That is the natural pattern, something we are all primed via evolution. Your personal preference to get more light in the evening because you want to spend more time outdoors runs against evolutionary instincts.
They are taking a charge back of $1.2bn with the layoff. Are you suggesting canceling the private performance would have costed a huge amount on similar lines?
This still does not make it equal between Amazon and the sellers. Remember sellers have to pay a listing fee, referral fee etc which Amazon does not have to. So with everything else being equal, sellers are basically paying a validation fee for Amazon to learn about their products, then compete with them. No one should trust Amazon in whatever business they are in.
My point is every cloud vendor needs to break up revenue by IaaS, SaaS, and PaaS. Each one looks strong and bad across segments and hence such a breakup will never happen. But saying AWS or MSFT is #1 misses the dominant segment where they are leading, hides where they are struggling, and gives an incorrect impression.
So does AWS. They include revenue from AWS Workmail and other SaaS offerings. Too bad MSFT or GOOG make more money than AWS with their SaaS or "Productive" offerings.
Cannot up vote this enough. During my time both at Retail and AWS it was perfectly normal to trawl production customer data and come up with ideas to launch competing products. Prices were always set lower or free offering justified as data-driven and customer obsession. I hated the gas lighting their customers and left in disgust of the company and its leadership which encourages that behavior.
What I keep seeing operationally is that teams are forced to act before hypotheses can be tested or falsified. For example, inspections completed, assets redeployed, and customers responded to. Only later are they asked to prove correctness.
When that gap shows up for you, what was the concrete trigger? An audit, a customer dispute, a safety review, or something else?