Jay Baxter

They can also leave suggestions on style or tone, so they can say, like, hey, I think this source is biased, or I think you should use a primary source that's going to be more trustworthy.

And then the AI takes that, regenerates a note, and usually gets it right.

And what's cool about this is first you get a better note on this post people care about,

But two, all of those corrections, all those suggestions are training data that you can feed back into the AI.

So you can make it less likely to make that mistake again, you can make it better at researching in the first place, and also you can make it more neutral, less biased.

So all these human suggestions, both they make better notes and they make better AI.

Yeah, so definitely in the last six weeks or so with the Iran conflict, we've seen the biggest surge in synthetic media that I've seen, at least in the misleading info space.

And I will say, we're on the frontier here.

So this is the highest-scale, highest-speed solution that exists.

These are new problems.

So we don't know what's going to work, can't guarantee the problem will be solved, but I think there's a bunch of reasons to be optimistic.

For that problem, like synthetic media surge,

We can both scale up the corrections, and we can both change the fundamental incentives and dynamics of the system.

So in terms of scaling corrections, we talked about AI.

Just to put some numbers on that, in the last four months alone, we've doubled the number of notes that are showing on X. So that's not trivial for a scaled service, two X in four months.

I think there's clearly headroom on that.

Is it 10x, 100x?

I don't know.

But there's clearly headroom to grow.

The other thing on the incentive side, one of the reasons people post these things is they can make money off of it through creative revenue sharing programs.