LLMs
7 statements across 6 episodes · 0 bullish · 0 bearish · 6 people on the record · first statement Apr 29, 2025 by Vinayak Hegde · across every show →
Everything said about LLMs, oldest first
Apr 29, 2025 neutral
Hegde: LLMs Introduce Probabilistic Behavior Unprecedented in Traditional Software
“What is so different about LLMs is, always, from a software engineering perspective, we never had elements which were probabilistic. We always had elements which were very deterministic. If you did A, B, and C, you would get D. If you do A, B, and C, again, yo…”
May 26, 2025 neutral
Jul 23, 2025 neutral
Malhotra: Visual AI Models Still Await Their Revenue-Driven ChatGPT Moment
“I believe that the, you know, the chat GPT moment for the image, video, and three models is still to happen. You could make a claim that an image that is kind of happened, but no one's making billions of dollars in revenue on image models. The way people are m…”
Feb 3, 2026 neutral
Toeman: AI video transformation expertise commoditized within four years
“Like when we started Augie, there were no L I mean, there were, but LMs were not commonly used or available for most developers. So one of the people on the team was an actual expert at AI video transformations. Amazingly over the course of the four years, tha…”
Feb 3, 2026 neutral
Toeman: AI elevates anyone to competence but only produces average work
“My opinion of what AI does for all of us is it brings us up to basically from incompetence to competent at anything it can do. So if you can't Photoshop. You know, you can now Photoshop, you can't do it as well as an actual designer, but now you can. If you co…”
Feb 24, 2026
Johnson: A company is AI-native only if removing LLMs breaks it
“The way to answer that question, whether you're an AI native or AI adjacent, is really simple. If the LLMs, all of the LLMs on the planet disappeared, would your company, would your department, would your role continue As, you know, as if nothing happened, or …”
Aug 13, 2026
Chen: LLM wrappers lack moats without proprietary IP or data
“We run into it a lot at the application layer where people build wrappers, so much, so to speak on top of these LLMs, like the antithesis of building a moat, right? You need some underlying IP and tech, your own model, your own data set, something else that is…”