Advice and Learnings

/ Some New Musings (a bit different format this week)

Thinking about customer use cases for products. Theme: helping people help themselves

Hypothetical question: do products that help customers make money tend to do better?

Example: Youtube.

A creator makes videos, gets views, and puts in ads. The viewer enjoys the content enough to justify watching the ad. The ad company pays YouTube, and YouTube pays the creator the ad money after keeping a percentage.

Through building a following, the creator now sells merchandise, a separate revenue stream from ads because ad money only scales when the creator makes more videos. YouTube’s economics are generally maintained: the more videos, ad placements, and volume $ YouTube gets. Perhaps an easier/faster video upload interface or favorable payment rate encourages a positive feedback loop for the creator to stick with YouTube vs. other media services.

This seems understood in business segments where you need to pass most of your economics to the customer, and you can’t improve the fees you charge. There are plenty of business models where margins will be capped at some % (10% flat fee on all customer sales), so they can only rely on volume growth vs. increasing price.

What about segments that get worse, not better, with size/scale? Negative network effects?

Advice is very odd.

Say someone comes to me and asks me for help making a decision in a situation. After thinking about it, I suggest they do X over Y. I help a person directly make a decision.

They succeed. In this example, they eventually learn that if they picked Y, they would fail. Maybe they asked just you or 3 people, and you tipped their decision in favor of X over Y. You see them succeed because you spoke value into existence and analyzed the situation for them to win, by effort or luck.

Because you don’t know the alternative universe outcome, because (sometimes) you don’t know whether it was causative or lucky, advice is always “best educated guess”. By definition, it is not definite.

Because it is not 100% certain, the common belief is that “someone with certain accolades/track record (past wins)” should give advice with a higher degree of certainty. In other words, if you listen to them, you will more often win than if you ask a random person. I don’t know if you can assume the hit rate will be above 50% (coin flip), but it should at least be higher (40% vs. 30%). Ignore the law of large numbers and think of small n.

Does this really apply in practice?

Similarly, what is someone is viewed either by clout/false perception to give more certain advice. No one wins then! You get their advice, which may be higher, the same, or lower quality on the outcome, but you think it is higher.

My point here isn’t just the simple thing of “all advice, even from (insert most important or qualified person in the world), should be taken with grains of salt” but instead “be very delicate about believing advice is more certain than what it actually is”

Thinking about the disconnect in how people say things are/work vs. how they actually are/work.

You can learn in 1-2 days about how things work if people tell them to you.

You can learn in at least 12-18mo years how things actually work by seeing them first hand.

Is it the disconnect in not being able to explain it? Not being truthful? Along the lines of “experiencing things yourself is truly unique / some things can’t be taught” messaging?

Relating to framework, you need ground truth/first principles reasoning and conclusions to refine one’s framework best. If I ask someone how something works, I may get poor/low-quality data if a) I don’t understand boundary conditions or b) they poorly explain the actual nuances of it. Either this is on them or on me, or perhaps it is unexplainable, and the only way to know is to see it firsthand.

I tweeted something similar here:

This tweet is interesting:

Until next time -VS

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