I work in a domain of applying AI to specific enterprise domain. It's not like you can crawl our data in the open web. Getting any data from clients is years of lawyer struggles and chicken and egg problems to solve. Fine-tuning models to client expectations - they are not going to go through the process again with someone else.
And moat in B2C AI is owning tons of your personal data and habits that Google and Facebook do. It's just not trully utilized with GPT models yet.
I heard from a large AI founder recently on this topic. Data is an okay moat, but in this craze we'll see the power of data shrink. Companies are getting enough VC funding ($10m-$100m+) to buy any data they need. A better model could also make up for a lack of better data.
Instead, the best moat is to know that your product isn't a thin replicable wrapper for ChatGPT but instead has a large surface area, with lots of well-built features. Continue building those features at a fast pace, and you can win.
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Data is moat in AI.
I work in a domain of applying AI to specific enterprise domain. It's not like you can crawl our data in the open web. Getting any data from clients is years of lawyer struggles and chicken and egg problems to solve. Fine-tuning models to client expectations - they are not going to go through the process again with someone else.
And moat in B2C AI is owning tons of your personal data and habits that Google and Facebook do. It's just not trully utilized with GPT models yet.
I heard from a large AI founder recently on this topic. Data is an okay moat, but in this craze we'll see the power of data shrink. Companies are getting enough VC funding ($10m-$100m+) to buy any data they need. A better model could also make up for a lack of better data.
Instead, the best moat is to know that your product isn't a thin replicable wrapper for ChatGPT but instead has a large surface area, with lots of well-built features. Continue building those features at a fast pace, and you can win.