warrenronsiek

Creator of tasktrace here, AMA!

I've created an AI children's storybook creator. I believe that I have the best product in the space, and by a wide margin. However, I'm concerned that product quality doesn't matter, and I'm looking for advice on what to do with the product.

There are many issues with this as a business: 1. Zero barriers to entry. 2. Low margins. 3. Repeat/subscription revenue is low or non-existent.

The margins on the business are too low to make ads make sense. New entrants constantly coming into the space and buying up keywords. You have to compete with them for SEO ranking even if their product is worse. By the time the customer has realized that the first ad/link they clicked on results in a sub-par book, they already made the purchase or gave up on the impulse. I've tried Facebook/Youtube/TikTok. My ads (according to FB) had above average performance, but even so CAC was WAY bigger than per-sale margin.

My current plan is an affiliate marketing system where I recruit influencers to link to books that they create and give them all of the profit in exchange for, effectively, 0 CAC traffic that I convert into an upsell. However, this has various difficulties as well. The influencers I've contacted all want upfront payment, often thousands of dollars, to create a single reel that will get a few thousand views.

Deeper issue is I don't understand how B2C products/marketing is supposed to break even. Somehow people are able to make money/businesses selling single-sale products via advertising, but all my experiments in doing this have negative ROI.

Thoughts? Am I missing something? What should I do?

So ML systems are guilty until they can prove themselves innocent?

Requiring the claimant have some evidence might not be a perfect legal regime, but the converse is a de-facto ban on innovation.

Can a human _prove_ that their decisions are made without bias? No. Are we going to hold them to the same standard proposed here for an AI? Also no. In practice, rules like the one proposed here just enshrine legacy decision systems which are already understood to biased and ineffective.

The most mysterious black box we have is the human brain.

Most companies aren't interesting enough to invest in without some kind of secret sauce. Claiming that they are going to use 'AI and ML' is a way of saying that 'yes, we are a company with tons of competition and little competitive advantage, but we will have a secret sauce eventually. We don't know what that is yet, so we are using jargon as a placeholder.'

Call me a cynic, but I think the talk of building a data moat is mostly nonsense. Of the companies that make these claims, how many of them are actually hiring expensive data engineers to build the moat? If don't have a team working on it, then its a ruse.

Unless the startup is founded by people with deep experience in ML and have been working on using it extensively from day 0, it is unlikely that they will be able to deliver on this vision. They won't be collecting the data correctly if they are doing it at all. If they are collecting it correctly (they aren't), they won't be able to get it to a useable form. If they get that far (they won't), they then need to build out the ML Ops to deliver their models. Now, finally, they can `from tensorflow import *`. Engaging this process post hoc takes YEARS.