The Exchanges

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 →

Andrew Feldman no published score: only 6 usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.5/5 from 6 raw tape exchanges record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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Answered raw tape D 5 · C 5 · P 5 · Cm 4 4.85

Q Co-design has become an important part of how do you create that kind of platform for the next three years because you're, you're building for the next three years. So how do you think about that and how do you go about chip design?

A Well, I, I think historically you, you made chips and you ran software on them and, uh, There wasn't surprisingly a close interaction because there was a layer called an operating system that lived between the chip and the software. And so, uh, you know, Intel and AMD made chips and people wrote software for the operating system or for the chip. AI has gotten so large and, uh, speed is so important that, that what they're doing is they're thinking about sort of the design together. That what changes could we make in software that would advantage the hardware or as we're designing the hardware, what changes could we make that would make the software easier to run? And so they're being designed sort of at the same time. And like anything, when you sort of design things together, um, the, the advantages are enormous. And, and so this is something that's really taken shape right now. Um, you know, one of the advantages of our relationship with open AI is we, we get to see exactly where the frontier is going and we get a chance to, to roll that into our designs. One of the advantages Google has is that their TPU can be designed in collaboration with the team building Gemini or the team of DeepMind, and so they can, they can inform their choices back and forth, and that's an enormously powerful thing that is surprisingly relatively new in our space.

AI assessment note: “they can inform their choices back and forth, and that's an enormously powerful thing”

Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q Okay. Wow. And so what lesson do you learn from that as a CEO? Like what is in your mind?

A I think a couple of things. I think to, to do the job that I love to build. I think, uh, making money is really great and making money for people you care about is really, really great. And when you get a chance to deliver for people who bet on you, who, who bet chunks of their career, right? Your investors, it's great to deliver for them. They bet on you, but they're diversified. They bet on you and 20 other companies. When someone bets five or seven years of their career and a career is 30 years, Right. They're making a sixth of their professional career. And when you get to deliver for them and, and, uh, they get to, to achieve the financial goals that they wanted. That's a great feeling and I'm proud of every day.

AI assessment note: “making money for people you care about is really, really great”

Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q runs on Brex, so I can spend time on building and not busy work. It's time to get Brex AF. Learn more at brex.com slash sorcery. That's B-R-E-X dot com slash S-O-U-R-C-E-R-Y. Bye. So looking forward, I'm sure this is also just a mark in your journey because you're a builder. So what are you most looking forward to over the next couple months as we said across this year?

A Look, getting to an IPO is, is not the, the, the end of a journey. It's sort of a plateau. It's sort of the arrival at corporate adulthood. It is the achieving what one plateau so that you can climb others. And our opportunity has gotten bigger. We have more resources. We have, uh, we're better recognized. Uh, we can reach more people. Um, and we can sort of prosecute our vision and our, our ambitions, uh, with more fuel. And so that, that, that's what we're excited about every day. You know, building more chips, building more data centers, inventing technology that, that, that moves the industry forward. Um, that's what, what drives us and gets us out of bed every day.

AI assessment note: “building more chips, building more data centers, inventing technology that moves the industry forward”

Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q What do you think that the biggest misconception with that process is and the challenges are?

A Well, I, I think that the misconception is that it's easy and all you need to do is get in a room and, um, it's a, uh, it's a very hard problem. Um, you know, the, the, the software guys think one way. The hardware guys think a slightly different way. Um, you know, anything you do to make it easier to, to, To, to write the software makes it harder to do the hardware, right? And these are really hard trade-offs, and so bringing them together, um, and means these sort of compromises where it will be harder here to make it easier here. And that means somebody's schedule is going to be impacted. Somebody's got to add resources. Um, those discussions are enormously difficult.

AI assessment note: “the misconception is that it's easy and all you need to do is get in a room”

Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q As we look forward, I guess more on the macro lens, the proliferation of AI and everything that we're be able, we're able to now create and build and do. What are you excited about on the externalities that come with all of this?

A Look, I, I think, um, that we have a chance for our children or the next generation, not only to not die from cancer, but to not know anybody who died from cancer. I think that is a, a real, uh, achievable goal in 25 years. Wouldn't that be something? Um, you know, when we think about what AI can do, uh, writing better code is cool. And there's a huge market for that. But I, I think what, what it can do to better humanity is rid us of the number one killer of, of adults. And, um, I think, you know, that, that's when, when I think of what we're all doing this for, it's an outcome like that. You know, pancreatic cancer had a huge breakthrough recently. I, I think they're, uh, the opportunity for, for breakthroughs right now is, It has never been better, and AI is a, an extraordinary tool in, in pursuit of, of, of knocking down, um, major, major human killers, right? I mean, if you think, if you, if you took out cancer and you took out automobile accidents, you're taking out huge numbers of deaths a year. Um, and you say to yourself, well, that's a lot of good. That we did. And I, I think, you know, self-driving, humans are terrible drivers.

AI assessment note: “we have a chance for our children or the next generation, not only to not die from cancer”

Answered raw tape D 5 · C 5 · P 4 · Cm 3 4.45

Q semiconductors or they're in, I don't know, models or, you know, coding agents, there's a lot of companies that are rising up really fast and hitting that billion dollar mark. Some are having liquidity events like OpenAI and Anthropik to come. Um, Um, but others are, are getting that paper markup and they're, they're seeing that kind of wealth creation event. How do you maintain a healthy mindset around that?

A I think you, you, you bring it. You, you, you, it's not a change, right? You, um, For the type of people I love working with, they, they like building stuff, and they like building hard stuff when it paid a little, when it was out of fashion, you know, when, when, when hardware was uncool, they were still building hardware, because that's what they like to build, and now that it's in fashion, they like to build hardware, and they're sort of even keeled about that, that their passion is the building, and I, I think in, in Silicon Valley, which sort of is what I know, that Um, chasing money is not the path to money. The, the, the path to happiness is working on projects you like with colleagues that are interesting for, for people with integrity, and if you do that, the money will come, but even more importantly, you'll work on things you like, and you'll work with people you learn a little something from, and you can teach a little something from, and if, if that's the way you, you set about pursuing your career, I think When things are really bad you're on an even keel and when things are really good you're on an even keel because you're enjoying what you're doing.

AI assessment note: “chasing money is not the path to money. The, the, the path to happiness is”

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