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 5 · C 5 · P 5 · Cm 4 4.85
Q Yeah, no, that makes perfect sense. Um, so tell us about, uh, your investment is just, I guess, what, what gets you excited these days?
A Um, I, I guess I can, I can put a, um, I, I can put a data angle. I was, I was thinking about this from a data perspective. I mean, there's a lot of things we're investing in at Trinity. Um, there, there's really, um, you know, I would say two themes that we're interested in when you think about, uh, companies that are doing things with data. I think one of these has, um, been, already been stated, but it's solve, solve problems, right? Um, with whatever, with whatever data you have. Um, at, at Logly, which is one of my portfolio companies that does log management and analytics, uh, as a cloud-based service, so think Splunk in the cloud, we talk about revealing what matters. And so, you know, what, one of the things that we're looking for in, in data companies is not just handing the user a bunch of data and saying, hey, we've got tools for you to figure out what's interesting here, but actually companies that reveal the in, That, that, that pull the insights out of your, uh, out of your data and make that very easy for business users to understand what's going on, um, without having to understand complicated query languages and things like that. So Logly is an example in, uh, uh, the log management space. Instead of logging in and just seeing a blank search box and the user's like, what do I do now? We, when you log into Logly, you actually see Some charts and graphs that help…
AI assessment note: “there's really, um, you know, I would say two themes that we're interested in”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q of categories of emerging AI and ML companies, and maybe taking a step back, you can perhaps think of the landscape as a combination of horizontal AI, uh, that essentially tries to solve a wide cross section of problems, and vertical AI, uh, which is a much more either task specific or industry specific type of AI. From an investor perspective, are you interested in both, one over the other?
A Right, right now, um, we're more interested in vertical AI, because AI is still, um, and I think Crowdflower is a bit of an exception to this, but AI is really hard to use today. I mean, you need a, I don't know, I'm guessing a lot of people in the room are familiar with how, how this stuff works, um, given that this is a data event. I mean, this is, the AI is not the type of technology that your average Technologist at your average company in the world can just sit down and start using. It's just, it's just way too complex for that. Um, CrowdFlower is interesting as a horizontal platform because it, um, the way that it works, it makes AI far more accessible to the, um, average user. You can literally just write your, your AI task in kind of human readable format that a human can understand, and CrowdFlower can just Kind of train models for you and decide which one is the best. But that's, I, I think unless we see more crowd flowers, the, the vertical approach is, um, going to be a lot more, uh, successful in the near term because it won't rely on the customer to have expertise in how to do things like tune models and tune training sets and things like that.
AI assessment note: “Right, right now, um, we're more interested in vertical AI, because”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q in your portfolio. Um, and you're among, uh, the group of VCs that also invest heavily in, um, in, in developer Tools, developer products, ah, which, ah, a lot of people have historically said that that's, that's harder to find the value there because the business user with the budget is not the person that uses the product. How do you think about that spectrum and what gets you interested?
A Uh, yeah, I mean, I'm still surprised at people who don't think you can build real businesses selling to developers. I mean, we have so many success cases now, right? There's Atlassian, I mean, what a great company, GitHub, uh, New Relic and Docker, two of the companies that I'm, uh, uh, that I'm on the board of. You know, you would think that after having a whole bunch of data points, people would be, uh, more, investors would be more sold on the idea that, um, you can build real Big businesses selling to developers, but, um, there's still a lot of skepticism out there. The reason why I love these businesses is that, um, the, the power in, and the influence in, uh, technology organizations is, has moved, shifted dramatically away from the classic IT group, um, to the developer. And that's actually a wonderful thing. Because, um, in the old model, and this is what we had to deal with at Wiley, when you had an, an IT group intermediary, you weren't building product for the end consumer of, um, of your product. You were building it for this intermediary, and that's why enterprise software used to really suck. It's, it's because, you know, you had to just meet all these, like, checklist Boxes that the IT folks, um, would put out there without regard for what the, your, your end consumer, uh, wants. Now, in this, in this new world with the power moving away from the IT group and to…
AI assessment note: “The reason why I love these businesses is that, um, the, the power in”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q Great. So perhaps just one or two last questions from me, and then we'll open it up to people. Um, it's also interesting from an audience perspective to understand what you don't like, um, and perhaps specifically some examples of, um, Categories where you think there's just been, you know, too much action, too much investment, or the business case is not strong enough? Um, Sure.
A Um, well, I just said that I don't love traditional enterprise software businesses. Um, my partners might kill me for that, too, because some of them do invest in those types of things, but, um, I'm, I'm probably not the right person at Trinity to pitch on one of those. Um, I, you know, the big data platforms, Um, uh, and, and data analytics tools. There's just so many undifferentiated. If we want to keep this conversation in the data realm, there are so many undifferentiated, uh, products and, and services out there. Um, and I think the, the advice that was given by the earlier speakers of solving problems, specific problems for the customer, instead of just trying to be Horizontal in, you know, we have a better query engine, a faster query engine, or more efficient storage, or, um, and, you know, more visualizations than, than the next vendor. We see a lot of that stuff every day, and it's, it's hard to build a business on those things.
AI assessment note: “I don't love traditional enterprise software businesses... big data platforms, data analytics tools”
