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.
Full method →
Answered produced feed
D 5 · C 5 · P 5 · Cm 4 4.85
Q Yeah. What, uh, just there's people listening that are maybe thinking about opening their own hedge funds, right? What do you mean when you say you didn't raise enough money? How much do you have to raise to have a hedge fund?
A Uh, so we had somewhere between 50 and a hundred million, but, uh, that's not sufficient in a long, you know, The minimum you need five hundred million to a billion to make it a long-term business, sustainable business, and so that's hard, you know, for every, it's kind of like the music industry or tech startups, right? For every huge success you hear of, there are many that don't make it for various reasons, and, um, it was also easier 15 years ago, right? Anyone with a Bloomberg terminal could start a hedge fund 15 years ago, but things, uh, it's, it's a competitive place. It's a brutally competitive place and markets are tougher, right? They're also no longer going straight up right now. So, um, yeah, I say go for it.
AI assessment note: “The minimum you need five hundred million to a billion to make it”
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q All right. So enter Austin AI. So what's Austin AI do?
A Yeah, so, uh, Austin AI is a data science, uh, AI and ML services firm, and we have a real focus on practicality and getting real business results for our customers. So, uh, as you know, there's no plethora of, of, uh, AI frameworks and products, right, that purport to do X or Y, and I have nothing against those things, but oftentimes, right, these things are sold as panaceas where you just install this in your company, hit a button, and then it comes up with all these Wonderful predictions, right? Uh, that's rarely the case, right? Because at the end of the day, the devil's really in the details of this stuff. Um, there's no substitute for good experimental design. There's no substitute for a good data engineering and good pipelines and good coding and all this kind of stuff. Um, a lot of companies we've talked to have installed these spent, you know, seven figures or multiple seven figures on these frameworks and they do unify things and they add a layer of abstraction, you know, so the whole company is kind of centralized, but they don't solve your data science problems at the end of the day by themselves, right? You still need data scientists. You still need very smart people, uh, running your analyses. And this, this is where we step in. We fill that gap. Um, I like to say that we come in with a swap team, a very data science pod, if you will, right? Uh, of a mix of senior…
AI assessment note: “Austin AI is a data science, uh, AI and ML services firm”
Answered produced feed
D 5 · C 4 · P 4 · Cm 4 4.30
Q So, yeah. I mean, so, so is it, is this really like a service model where you've got these folks abroad and you have to sort of spin them up or down based off what your client volume looks like?
A We like to think of it as a package services model. So somewhere between pure services and, and products, right? So we don't like to just say services because that invokes ideas of, you know, here's a guy hourly, uh, or here's a fee to place you, this person here. We also do that by the way. So if anybody out there needs just staff augmentation, we can do that. But really our bread and butter is coming in and saying with that pod, Right. We have a packaged service that's been successful on a repeated basis. We have business processes in place. We actually have core code in a library that we will license to you as part of all this too. So we can just be really efficient when we come in with this pod. And it's more than just services because we're really trying to solve your problem, right? We're trying to get to know the business. We have industry experts. We don't have industry experts and speak to them. So it's very empathetic to the business side of things.
AI assessment note: “We like to think of it as a package services model.”
Partly produced feed
D 3 · C 4 · P 3 · Cm 3 3.30
Q What does that mean when you say efficient? So if I'm going to get a data science pod from Austin AI, that's maybe four or five people strong. What am I going to pay you per month for that on average?
A Um, so contact needs to be exact pricing, but let me put it this way. I always tell clients is think of your data science problem. Think of how many data scientists you're going to need to go higher to do that. Go look up the market rate. Okay. Now add taxes and now add benefits and you'll get, you'll come to a number, right? I can almost guarantee you that we will be significantly below that number, uh, significantly below that number and faster in the way in which we operate. Um, and that's due to our operational efficiency. And it's also due to Uh, the structure of the pod, right, may have one very senior person on it working only part-time, but they may be managing.
AI assessment note: “contact needs to be exact pricing, but let me put it this way”
