Every argument clarity score on this site is built from rows on this page. Each
question and answer was assessed with names hidden, the host's own answers included, on
four things from 1 to 5:
directness (does it answer the question asked), coherence (do the ideas follow),
precision (concrete details and clear references), compression (says a lot per word). The weighted
mix (30/30/25/15) is the exchange score. A person's published score averages their exchange
scores on raw tape only, at least 8 of them, shrunk toward the cohort mean.
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Answered raw tape
D 4 · C 4 · P 4 · Cm 4 4.00
Q And the other thing is that, ah, the AI models that people are training, right? How do we ensure that they don't have any confirmation or harmful biases? Because we don't know, right? The input data or the data models that you talked about, where's the source of those data models?
A That's a risk. So especially when you go and look at any general purpose model that is out there and what exactly are you using? If you're using that for the entirety of the information to really get certain things done, Then it's probably going to be hard. So let's say we think about it a lot in the context of the companies. You have data. You have information. Can you really restrict all your answer or any kind of things that you want to answer only to that set of information that you're providing? So use that and then use the LLMs only to translate once you have the information In a language that the human can go and understand it, but don't use the intelligence or the, the biases that are set, uh, with those LLMs today. So most enterprises that you will see that they will just do it, uh, that way. Uh, in many cases that you'll also see that, uh, especially for the enterprises again, that they will go and, uh, train the LLMs with the data set for their domains, with their data. Uh, instead of just going and using the, the general purpose for all things that they want to do.
AI assessment note: “restrict all your answer or any kind of things... only to that set of information”
Answered raw tape
D 4 · C 4 · P 3 · Cm 3 3.60
Q And which are the jobs that you think will first go away because of AI? What kind of jobs are they?
A I think again, uh, that if you're not Producing something, um, from let's say, uh, yourself. If you're not producing something yourself, and most of the time that, okay, a question is being asked to you, and you have to go and look up for that information, search for that information, or you read about that information. That's what you are going on and answering. Those are the things that you will start to see that the bot can do. Because they can go and search for the information. They have plethora of information right there, and they can continuously learn about this information, like while human will have to go and sleep and so much time, but machine, they don't need to go and sleep. So those are the jobs that I definitely see as the first one, which can easily be displaced. But that just says that, okay, uh, because machine, uh, can do a better job.
AI assessment note: “Those are the jobs that I definitely see as the first one”
Answered raw tape
D 3 · C 3 · P 3 · Cm 3 3.00
Q Do you have any fears that AI is progressing at a speed that the best of humans cannot, not, are not able to keep a pace with it?
A I mean, it's a very interesting question. Um, first of all, like such technology, um, first basically they, they move slowly and then suddenly that's what happens. And it feels like that it is moving very, very fast right now. Now to say that whether human, there are a lot of good use cases, by the way, and there are a lot of bad use cases also will come, come along in, at least in my head right now, it is, If cognitive load that the human have, doing anything that they do, a lot of busy work that they do, and if machine learning or AI, at least in the context of, let's say, work that we do day to day at work, it starts to take away, then we have more time to think, more time to do a lot more innovative work. So in that sense that it should help, uh, in big way.
AI assessment note: “then we have more time to think... in that sense that it should help”
Not addressed raw tape
D 1 · C 3 · P 4 · Cm 3 2.65
Q Fantastic. And today, you and Dhiraj have built DevRev, right? And you are taking directly on Salesforce. You are?
A I mean, just the, I mean, learning, I would say, in building something, and then the pain points that you also see. So, I'll say that one of the things that we are super proud of at Nutanix was the very, very high net promoter score, NPS. And NPS, a lot of people that they confuse with the, CSAT, customer satisfaction score, because, uh, customer satisfaction score that when you say 90% or 95%, uh, just saying that, okay, people, they are saying that on a score of, let's say, one to 10, that they are giving you nine and above mark, right? That's the way that you can see that. Maybe when you see 80%, it's like 80% still considered very, very high on the CSAT side, so 80 score that you got. When on net promoter score, when people rate you at eight, It's considered zero. So seven and eight score when customers give you is considered zero. Below that is minus negative score. And only nine and 10 is considered plus one. So score goes from minus one hundred to plus 100. That's the way it is normalized. And, uh, despite at the speed that we are growing every year that we consistently stayed above plus 90.
AI assessment note: “one of the things that we are super proud of at Nutanix was the”
