Barak Turovsky (Exec In Residence, Scale Venture Partners): How to Evaluate Generative AI Use Cases
Barak Turovsky (Exec In Residence, Scale Venture Partners): How to Evaluate Generative AI Use CasesWhy search might be a red herring and who will capture the most value in the AI stackDear subscribers, Today, I want to share a great framework for evaluating generative AI use cases. Barak Turovsky is Executive in Residence at Scale Venture Partners and ex-head of product for Google Languages AI. I worked with Barak a decade ago, so naturally I had to chat with him about AI. In the interview below, we talk about:
How to evaluate generative AI use casesWelcome Barak! What’s your framework for evaluating generative AI use cases? I like to evaluate use cases across two axis:
Here’s the breakdown:
Makes sense. Just the other day I was using ChatGPT to clean up some data and it started making up numbers halfway through! Are there scenarios where generative AI can still help with high accuracy use cases? Yes, they can still help if you have a person manually checking the AI’s output. For example, ChatGPT can write the first draft of your business email for you to edit. But manual review breaks down in high volume use cases like search where it becomes impossible to have a trained person validate every result. Why search might be a red herring vs. other use cases for large language modelsBoth Microsoft and Google are racing to use generative AI and large language models (LLMs) to improve search. Can you describe the types of search results that might be a good fit for these AI models? Yes, let’s use the same framework above to breakdown search:
That matches my personal experience with Bing AI. Over time, I found myself using it for creator use cases (e.g., Make this content more clear and concise.”) Yes, a third axes of the framework above is how high the stakes are.
Imagine the LLM gave you a Disneyland itinerary that recommended a subpar hotel or a restaurant that’s actually closed. You would be pretty mad. Yeah, despite the hype I don’t think anyone will blindly trust LLMs to book travel or restaurants right now. How fast will LLMs fix the hallucination problem? I think it might be hard to improve accuracy. LLMs like GPT4 are already trained on trillions of parameters, so there’s a diminishing return to creating bigger models. The main value of LLMs is fluency with “good enough” accuracy. Even at 80-90% accuracy, LLMs could disrupt a wide variety of industries. Which markets do you think LLMs would disrupt first? So we discussed how LLMs are ideal for creator and productivity use cases. Here are three other markets that are ripe for disruption:
Which companies might capture the most value in the AI stackCan you describe the generative AI stack and which layer might capture value? At a high level, there are three layers:
In terms of which layer will capture the most value, my bet is on the infra layer:
Applications, on the other hand, are risky. There’s a joke that many AI apps are just wrappers around OpenAI’s APIs. How do you build a moat in the application layer? I think AI apps can build moats in a few ways:
Any closing thoughts on generative AI? We’re still in the early stages of the AI revolution but I think one thing is clear: Companies need to think about how they can use generative AI to enhance their product or they’ll risk getting left behind. As with every new tech, productizing generative AI is both exciting and scary. I’m excited to see more companies use the framework we discussed to cross the chasm. Thank you Barak! If you enjoyed this conversation, please follow Barak on LinkedIn. Creator Economy by Peter Yang is free today. But if you enjoyed this post, you can tell Creator Economy by Peter Yang that their writing is valuable by pledging a future subscription. You won't be charged unless they enable payments. |
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