Louis Hunt's Profile

Thank you for sharing, Saad. I agree with you that Dr. Ming’s intentions here feel very admirable. It could be that Dr. Ming is simply before her time – I do feel that as the costs of computation continue to decline, more ML applications that don’t necessarily have to generate lots of profit could become viable… Until then though, I could see why Dr. Ming would have difficulty sustaining these applications. As long as the computation required for training and maintaining ML models remains costly, I feel that commercial applications will need to be the primary focus for innovation in the field…

So interesting, thanks very much for sharing Sultana. Question for you: what aspect of the business do you think creates the largest moat for FINESSE? Do you think it’s the sentiment analysis, or the voting and media campaign generation process? In regards to the former, I would think that other companies could duplicate these models or try to specialize in providing sentiment analysis to try and out compete FINESSE on this dimension… but I wonder if FINESSE feels it has some distinctive advantage here.

Fascinating post, Karthik! Thank you for sharing.

I remember C3.ai’s IPO in 2020 and watching the stock price soar. I saw that, despite C3.ai’s continued growth, today the stock is down more than 90% since its high… macro factors aside, I would imagine that this is partially due to the market’s skepticism around the sustainability (or availability) of C3.ai’s future cashflows.

In addition to companies’ data readiness, do you think there are other factors that could limit C3.ai’s profitability in the future? If companies are able to overcome their data readiness challenges, would that put them in a position to no be as reliant on C3.ai? Similar to Gigi, I’d be curious to hear your thoughts on C3.ai’s moat going forward.

That is super interesting. On the one hand I wonder if a longer input affords Craiyon more data to be able to incorporate into its generative model. So more words and precision might lead to more output that resembles the user’s general idea for what that user is looking for…

Also, I think that advertising content is the immediate use case for generative AI. I think it’s not long before we see tons of advertising images directly related to each person’s life (ie we would see people in ads with “HBS” sweaters, perhaps even see people in images that look very similar to you, etc…).

I think this is quite interesting. I wonder if asking for predictions from Craiyon sort of plays off a human flaw in human cognition. When we type in a prompt asking a question and images get generated (kind of like in our own minds), we think about the images generated as responding predictively versus simply scraping previous data histories related to the words in the prompt…

I love it, Isa! I was thinking about this given some of my dream vacation destinations (Egypt, Argentina). I was wondering if the prevalence and use of generative AI will lead to less diversity of thought. I.e. people start moving towards the same ideas for vacations. I think we’re all more susceptible to suggestions than we might believe…

Thanks for the fascinating post, Carlos!

Similar to Nthato, I think about OpenSea’s valuation, and it reminds me of how linked demand for platforms are with the “intrinsic” demand for transactions that platforms facilitate. I think it is unquestionable that OpenSea significantly enhanced the market for NFTs by facilitating an open marketplace for transactions, and that the many features they offered significantly enhanced both the purchasing and selling experience for NFTs. And yet, it feels like the rapid decline in transactions on OpenSea points to how, as powerful a complement that OpenSea has proved to be in the marketplace for NFTs, at the end of the day the value that a marketplace platform can create is ultimately most contingent on the amount of demand for the assets being transacted upon…

Thank you for posting, Michelle! As someone who loves to read I’ve heard about and always been curious about Goodreads but have not yet signed up…

In terms of the sustainability and durability of the platform in general, I also wonder if one of major constraining factor is customers’ demand or “willingness to pay” for books in general. In many ways, I view Goodreads and some other reading platforms (my favorite is Readwise) as offering complementary value to the reading experience – i.e. they help users gain more value from any reading that they do. At the same time, with such sharp decline in reading and rates of purchase for books in the United States, I wonder if Goodreads will find itself trying to capture value from a continuously declining pool of engaged readers…

Thanks for the wonderful post, Kate! This is fascinating. On the one hand, it seems like Hinge benefits greatly from same-side network effects to the extent that the more users they have on the app the stronger the “dating pool” of candidates available. At the same time, I can’t recall too many apps that aim to enhance the value of their platform by “constraining” their network effects… Really interesting, thank you for posting!

Thanks very much for this fascinating post, Patric!

I would be curious to see a breakdown of Flo’s revenue. Hearing about their advertising business and their challenges with the FTC made me think about the adage heard frequently these days in tech circles: “On a long enough timeline, everyone sells ads.”

It feels like the allure of ads is particularly powerful in the context of consumer software that collects first-hand user data and venture backed businesses. Despite the power of personalization, it strikes me as an interesting phenomenon that many business still fail to capture value directly from the personalized user experience and have to depend instead on monetizing the database on the back end…

Thanks for this fascinating post, Karthik! Super interesting to learn more about how Kroger is leveraging big data to optimize both the personalized shopping experience and the supply chain.

Regarding the former — I was actually working quite closely with the 84.51 team over the summer on innovation. In addition to believing that they can optimize demand for goods available in the store currently, they also believe their predictive analytics give them an edge in predicting what new products consumers will want in the near future… I think only time will tell just how good their predictive models are 🙂

Thanks very much for this fascinating post, Anand!

I’m most curious about the data labeling process for farm-specific data. It strikes me that being able to use special imaging technology to identify granular crop growth patterns and point out crop stress in terms of diseases, drought, or pests is in and of itself a remarkable use of ML. I wonder how long it took to train that component of the model and how many drone flights it required…