Ethan LockshinSales at NVIDIA focused on AI adoption

🚀 Ty Dunn, Co-Founder & CPO of Continue, shares his take on Open Source vs Proprietary LLMs

Name: 🚀 Ty Dunn, Co-Founder & CPO of Continue, shares his take on Open Source vs Proprietary LLMs
Uploaded: 2025-08-19T14:09:54+00:00
Duration: 1 min 42 s
Channel: Ethan Lockshin
Description: In a conversation with Ethan Lockshin, Ty Dunn, Co‑Founder & CPO of Continue, discusses the role of open‑source and proprietary large language models…

19 Aug 2025 · 2 min

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In a conversation with Ethan Lockshin, Ty Dunn, Co‑Founder & CPO of Continue, discusses the role of open‑source and proprietary large language models (LLMs) in AI coding assistants. He explains that Continue can operate with both open‑weight and proprietary models simultaneously, noting that most AI code assistants actually orchestrate multiple models for different tasks. Dunn highlights the benefits of open‑weight models—greater portability across infrastructure (AWS GPUs, on‑prem, services like Together or Groq) and increased accountability due to transparent training data—while acknowledging that the current frontier proprietary models are often the easiest to adopt and deliver top performance. He concludes that the future of AI coding tools will be multi‑model, with developers selecting the best model for each context rather than relying on a single dominant model.

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I was reading the Q2 State of AI report by Artificial Analysis and there's a section about demand for AI coding tools. You had GitHub, Copilot, Cursor, Cloud Code and then a few others and then your company Continue. So you're building one of the fastest growing AI coding tools in the market. There's a growing debate about open source versus proprietary.
Models. Where do you stand? I think both of them have different value. Right. Depending on the use case. Right. The nice thing about Continue is you can use open models, you can use proprietary, you can use both of them at the same time. Something often people don't know is AI code assistants are generally actually multiple models depending on kind of the part of the AI code assistant. And so you can kind of mix and match. Right. And so for us, we're also open source and so we love kind of open models, open weights, open source models, but we also love the proprietary ones because it's like three clicks often and you can and just get set up and get going and have the best open weights. That said, have an advantage in many ways that the other models don't have. Right. Where maybe in some cases it's harder to actually go about setting them up. But the nice thing about the open weights models is you have greater portability. Right. You can run them anywhere, whether that's kind of renting some AWS GPUs or having your own data center or using something like Together or Groq. Right. You also have accountability with the open weights models. If you look at a lot of the Chinese labs, they're learning a ton from each other and distilling each other and using kind of data generated by the other models to make theirs better. But at the moment the best models are the frontier models. They're often the easiest to get started with. And so I think the future is a mix. Right. I don't believe in a single model to rule them all. I think it's definitely a multi model future. Many models that you will use in different contexts.