[streetcat1]

They all spend with one purpose - replacing expensive humans, saying other wise does not make sense.

Any other app does not have moat - anyone can do the same app if it basically wrap the LLM.

If anything, LLM just destroy thier current moat, I.e. if everything is getting behind a chat interface, no one would would see ads.

[0xrelogic]

You're touching on the core tension in Meta's strategy. I think you're partially right, but there's more to it.

On "replacing expensive humans" agree that's part of it, but the bigger play is augmenting existing products. Meta's Q3 2025 guidance shows ad revenue still growing 21.6% YoY. They're using AI to make existing ads more effective (better targeting, higher conversion), not replacing the ad model entirely.

On the moat question this is where the infrastructure spending makes sense. You're right that wrapping an LLM has no moat, but owning the infrastructure to train and serve your own models does. Meta has three advantages: (1) 3B+ daily users generating training data competitors can't access, (2) owning 2GW of infrastructure means $0 marginal cost for inference vs paying OpenAI/Anthropic, and (3) AI embedded in Instagram/WhatsApp/Facebook is stickier than standalone chat.

On ads behind chat interface this is the real risk. But Meta's bet seems to be: short-term AI improves existing ad products (already working), mid-term AI creates new surfaces for ads (AI-generated content, business tools), and long-term if chat wins, Meta wants to own the chat interface (Meta AI), not lose to ChatGPT.

The $75B question is whether they're building a moat or just burning cash on commodity infrastructure. Time will tell, but the data advantage plus vertical integration gives them a shot.

What's your take do you think the data moat is real, or can competitors train equally good models on synthetic/public data?