We have all been targeted by ads that you look at and wonder “how do they know I am in the market for that product?” The answer is that the AI/ML models that big tech companies have trained on our personal data are incredibly accurate and powerful.
There are two problems with this:
The first is that “our data” which they use to train their models actually belongs to us but for two decades now, we have been giving it to big tech companies.
The second is the models that are trained on our data belong to big tech even though they are trained on our data.
It doesn’t have to be this way and I don’t believe it will be this way for much longer.
Web3 will help.
Let me explain.
If you go to zora.co, you will find a social feed that feels like Tumblr, Instagram, Facebook, etc that you can scroll through and like the things you see. But there is one difference, liking is called minting on Zora. You don’t just tell the creator you like their work, you send them a tiny bit of money and you get to own a copy of the work.
It may not seem like much, but the difference here is that you own one of the things you liked and you paid a tiny bit for it. If the creator gets a thousand people to do what you did, which is not that uncommon at places like Zora, they make a nice bit of money on their work.
We have all been targeted by ads that you look at and wonder “how do they know I am in the market for that product?” The answer is that the AI/ML models that big tech companies have trained on our personal data are incredibly accurate and powerful.
There are two problems with this:
The first is that “our data” which they use to train their models actually belongs to us but for two decades now, we have been giving it to big tech companies.
The second is the models that are trained on our data belong to big tech even though they are trained on our data.
It doesn’t have to be this way and I don’t believe it will be this way for much longer.
Web3 will help.
Let me explain.
If you go to zora.co, you will find a social feed that feels like Tumblr, Instagram, Facebook, etc that you can scroll through and like the things you see. But there is one difference, liking is called minting on Zora. You don’t just tell the creator you like their work, you send them a tiny bit of money and you get to own a copy of the work.
It may not seem like much, but the difference here is that you own one of the things you liked and you paid a tiny bit for it. If the creator gets a thousand people to do what you did, which is not that uncommon at places like Zora, they make a nice bit of money on their work.
And the collector is building their own data set that they own. It is on the blockchain and it belongs to them.
The next obvious step is for companies like Zora to offer collectors the ability to train models on their collections. This turns their collections into training data sets. But these are training data sets the collectors own. Not training data sets that Zora owns.
It won’t be long until we have open-source AI/ML models that we can run on our phones. These will be our models and we can train them on our data sets.
Consider this blog post. You can collect it too. There is a green button on the upper right of this post that says Collect. When you click that the same thing happens here as what happens on Zora. I get a tiny bit of money and you get your own copy of this post.
Below is a screenshot of an Ethereum wallet I have connected to this blog. You can see some of the collecting transactions from this blog over the last week or two.
So what is going on here?
1/ Writers are getting paid for their work
2/ Readers are building a data set that they own, On Chain. Not on Facebook.
The next obvious step is for us to have our own open-source models that we train on these collections we are building.
These open-source models will help us write, find new things to read, and more. It can inspire us to start a new company, invest in a new company, listen to a new song, find artwork to hang over our fireplace and many other things we want to do.
Going back to Chris Dixon’s words in yesterday’s post:
in the long run, we are still going to need an economic covenant between AI systems and content providers. Al will always need new data to stay up to date. The world evolves: tastes change, new genres emerge, things get invented. There will be new subjects to describe and represent. The people who create content that feeds AI systems will need to be compensated.
There is a way forward that works for writers, readers, collectors, creators, and everyone.
It starts with us owning our work and allowing others to pay us to collect our work.
The thing that makes me so optimistic about this is that it is not some dream. It is happening right here on this blog. The tools we need to change the way the world works are already here. We just need to start using them.
My next post will be about how we get more people using these tools.
If I asked you what the native business model for content is, you'd probably say either advertising or subscriptions. But I am starting to think that AI is to content what search engines are to browsers. Money machines.
I was emailing with my friend Lock a few weeks ago and we were talking a bit about my 2024 predictions post. I made reference to the section in that post about AI and litigation and said:
maybe we will get a settlement that makes all the big AIs pay 2/3 of their revenue to content companies and writers and we will have the native revenue model for media!
I was only half joking.
When the Hollywood writers went on strike, I suggested to all of my writer friends that they should be happy to let AIs write films and TV shows as long as they get paid to sit home and do nothing under the premise that the AIs were trained on their work and so they are due royalties.
Most current AI systems have no economic model for creators... in the long run, we are still going to need an economic covenant between AI systems and content providers. Al will always need new data to stay up to date. The world evolves: tastes change, new genres emerge, things get invented. There will be new subjects to describe and represent. The people who create content that feeds AI systems will need to be compensated.
The Partnership for NYC, alongside its partners at the MTA, the Port Authority of New York and New Jersey, NJ TRANSIT, and NYC Department of Transportation, launched a call for applications for the 6th annual Transit Tech Lab this week.
To kick off this year’s program, the Transit Tech Lab is seeking early and growth-stage tech companies with compelling solutions to one of three local transit system challenges:
Customer Experience Challenge: How can we improve customer experience by better communicating service changes, reducing delays, and augment safety and cleanliness initiatives?
Resilience Challenge: How can we build a more resilient and adaptive transit system?
NYCDOT’s Curb Activity Challenge: How can we maximize the city’s curb space to serve the multiple and varied needs of New Yorkers?
Representatives from each participating agency will evaluate applications based on the technology’s impact and the applicant’s product, team, and overall value proposition. Finalists will advance to conduct a proof-of-concept over an eight-week period; the companies demonstrating the most compelling technologies that align with the agencies' objectives have the opportunity to secure a yearlong pilot.
Applications are due Wednesday, February 28. Interested applicants are invited toattend an information session on February 1 at 1pm ET.
If you know of a company or emerging innovator that would be a good fit for this year’s Transit Tech Lab, please let us know about them via email or encourage them to apply here: https://transitinnovation.org.
And the collector is building their own data set that they own. It is on the blockchain and it belongs to them.
The next obvious step is for companies like Zora to offer collectors the ability to train models on their collections. This turns their collections into training data sets. But these are training data sets the collectors own. Not training data sets that Zora owns.
It won’t be long until we have open-source AI/ML models that we can run on our phones. These will be our models and we can train them on our data sets.
Consider this blog post. You can collect it too. There is a green button on the upper right of this post that says Collect. When you click that the same thing happens here as what happens on Zora. I get a tiny bit of money and you get your own copy of this post.
Below is a screenshot of an Ethereum wallet I have connected to this blog. You can see some of the collecting transactions from this blog over the last week or two.
So what is going on here?
1/ Writers are getting paid for their work
2/ Readers are building a data set that they own, On Chain. Not on Facebook.
The next obvious step is for us to have our own open-source models that we train on these collections we are building.
These open-source models will help us write, find new things to read, and more. It can inspire us to start a new company, invest in a new company, listen to a new song, find artwork to hang over our fireplace and many other things we want to do.
Going back to Chris Dixon’s words in yesterday’s post:
in the long run, we are still going to need an economic covenant between AI systems and content providers. Al will always need new data to stay up to date. The world evolves: tastes change, new genres emerge, things get invented. There will be new subjects to describe and represent. The people who create content that feeds AI systems will need to be compensated.
There is a way forward that works for writers, readers, collectors, creators, and everyone.
It starts with us owning our work and allowing others to pay us to collect our work.
The thing that makes me so optimistic about this is that it is not some dream. It is happening right here on this blog. The tools we need to change the way the world works are already here. We just need to start using them.
My next post will be about how we get more people using these tools.
If I asked you what the native business model for content is, you'd probably say either advertising or subscriptions. But I am starting to think that AI is to content what search engines are to browsers. Money machines.
I was emailing with my friend Lock a few weeks ago and we were talking a bit about my 2024 predictions post. I made reference to the section in that post about AI and litigation and said:
maybe we will get a settlement that makes all the big AIs pay 2/3 of their revenue to content companies and writers and we will have the native revenue model for media!
I was only half joking.
When the Hollywood writers went on strike, I suggested to all of my writer friends that they should be happy to let AIs write films and TV shows as long as they get paid to sit home and do nothing under the premise that the AIs were trained on their work and so they are due royalties.
Most current AI systems have no economic model for creators... in the long run, we are still going to need an economic covenant between AI systems and content providers. Al will always need new data to stay up to date. The world evolves: tastes change, new genres emerge, things get invented. There will be new subjects to describe and represent. The people who create content that feeds AI systems will need to be compensated.
The Partnership for NYC, alongside its partners at the MTA, the Port Authority of New York and New Jersey, NJ TRANSIT, and NYC Department of Transportation, launched a call for applications for the 6th annual Transit Tech Lab this week.
To kick off this year’s program, the Transit Tech Lab is seeking early and growth-stage tech companies with compelling solutions to one of three local transit system challenges:
Customer Experience Challenge: How can we improve customer experience by better communicating service changes, reducing delays, and augment safety and cleanliness initiatives?
Resilience Challenge: How can we build a more resilient and adaptive transit system?
NYCDOT’s Curb Activity Challenge: How can we maximize the city’s curb space to serve the multiple and varied needs of New Yorkers?
Representatives from each participating agency will evaluate applications based on the technology’s impact and the applicant’s product, team, and overall value proposition. Finalists will advance to conduct a proof-of-concept over an eight-week period; the companies demonstrating the most compelling technologies that align with the agencies' objectives have the opportunity to secure a yearlong pilot.
Applications are due Wednesday, February 28. Interested applicants are invited toattend an information session on February 1 at 1pm ET.
If you know of a company or emerging innovator that would be a good fit for this year’s Transit Tech Lab, please let us know about them via email or encourage them to apply here: https://transitinnovation.org.
