Will building leverage get easier thanks to better technology?
Published on my website
While reading some of the work of Naval Ravikant. On the topic of building wealth, he stresses the need for someone to build leverage. Meaning your time is not correlated to your output. Meaning you can work on a product for 10 hours and the returns can be 10x or 100x the value. This is why Naval urges people to stop swapping time for money. Even doctors and lawyers get paid a lot by the hour. Won’t get as rich. If they did. They started something separately like a private practice or selling a medical product.
Naval talked about the most recent form of leverage. Which are products with little marginal cost of replication. Which means media, books and code. With the internet, the cost of replication is close to zero. So if you write an eBook and sell it on amazon. You don’t need to pay the printing cost. It simply goes straight to the user. Naval explains this new form of leverage is great because it’s permission-less. You can simply start producing without another person’s approval. Things like social media, blogs and podcast also count in this bucket. All you need is a microphone or a camera and you can start. He mentioned that code can come with extra leverage because it can work 24/7. In this context he is talking about you can rent servers from the tech companies where you can place your code in. And can run the service for you 24/7.
Which brings me to the topic at hand. Code is a high leverage skill because of the possibilities you can make with it. With little replication cost. I’m thinking that while code is already high leverage skill. Machine learning may increase that leverage even more. As you are teaching a robot. Learn a process. Once that process is learnt it can be replicated to many places. Compared to normal programming where you are making the end product from scratch. Before you may have to rely on human leverage aka human labour¹. But now you can use AI to complete a task. Which may be done faster and more accurately. Like what Naval said you have datacentres packed with robots. So you have other robots helping the robots you made. As the machine learning model improves from your use of the product. Due to users adding new data to the model. As time increases the leverage also increases as well.
I think the best examples of this are the major tech companies. With Google, each search is making the service better. As they collecting data on how the service is being used. As the service gets better more people are more likely to use the search engine as it gives them what they want. Expanding there reach even more. Same with Facebook. For the data, they collect. This is to make the service better. (which means more money). As you click on certain posts on your timeline you are training the algorithm. Which means it will show more posts that you’re more likely to click on. Helping you stay on the platform. As it knows more about you it can sell that information to advertisers. Were targeted ads can be displayed on your feed.
So compared to other products. Machine learning products. Can have a strong flywheel effect. Where the cost of replication is not just low but its better after each replication. This is where some of these algorithms get powerful. This is why regulation is likely going to step in. As the flywheels of the companies are just too strong. And Competitors can’t compete with them. The competitor will never have enough data coming to go head to head with them. Don’t get me wrong they are some exceptions like Tick Tock. Where they got a great data flywheel going. Helping to grow the product even more.
Let’s go back to the ebook example. As soon as the author publishes the book. The author is not getting paid by the hour. Is getting paid by items sold. Disconnecting him from the input and the output. As the author can make tons of money by selling lots of items. While hours put into the book is the same. Which is where the leverage comes from. But imagine each time a person gets a book. After they have finished reading it. The book improves ever so slightly. So when the next person comes then he is seeing an improved book. This is what is happening for the tech companies that I mentioned above. So it will be hard for a beginner to catch up.
While these systems are democratic meaning everyone can use them. After a while, they become almost an oligopoly. Due to entrenched powers. This can be applied to the highest levels like the tech companies. To the lower levels like the content creators. A content creator nowadays will find it harder to build an audience than a couple of years ago. Due to massive content creators on the platform taking attention from most users. As they pull lots of the clicks and views. The algorithms tend to have a bias to favour them. Due to the history of generating attention for their platform.
Therefore making an entrenched class of content creators on a platform. Those content creators can use the money they earned from making content. To make better content that people are more likely to see. (good on them though). And leveraging their audience to help get further reach. Some of these issues are only a result of content creators leveraging their audience for greater heights in their career. Which is great for them don’t get me wrong. But because of the increased leverage. The compounding interest makes it harder for everyone to catch up.
This is not a sob story. With the internet now all of us have the chance. To make our flywheel. And what naval said you can “Escape Competition through authenticity”. Meaning you can make your monopoly just by being yourself.
[1]: A lot of human labour is still used for labelling data. So human labour is still important for making AI.
