Reading this, the comparison is unsettling precisely because it feels so logical. A machine can recognise three seconds of a song in an instant, yet a stolen face, voice, and reputation still depend on forms, reports, and patience. Very interesting piece and very well written.
Hi James. Good for you for doing what you feel you must do. I have wanted to do that for many years and finally got to it. I wrote a manuscript, a memoir called WALLFLOWER, that is awaiting publication. I can’t wait to share it with everyone. It tells the story of my traumatic childhood, living between the hearing and deaf worlds, how my animals and becoming a mother helped to heal me.
This is a genuinely sharp argument, particularly the contrast at its centre: three seconds of copyrighted music can wake the machine, while a stolen human identity still waits for someone to fill out a form. That comparison makes the technological capability almost secondary to the more uncomfortable question of what our systems have actually been designed to value.
I also appreciated the nuance around biometric privacy. It would have been easy to argue simply that platforms should scan every face automatically, but acknowledging the danger inherent in that solution makes the eventual point much stronger: once someone has voluntarily verified their identity and consented to detection, why should protection still depend upon repeated manual intervention?
The line that stayed with me was, “What a company doesn’t bother measuring is usually what it has decided doesn’t need managing.” There is something much larger contained in that observation. We tend to think algorithms reveal technological priorities, when in reality they often reveal very human ones—particularly where money, liability, and consequence are concerned.
I write occasionally about geopolitics and the stranger contradictions of our modern systems at The Reflective State, so this intersection of technology, institutional priorities, and human consequence was particularly fascinating to me. I’m happily subscribing. If curiosity ever leads you my way, I’d be delighted to have you visit.
Thank you. I'm really glad the point about voluntary verification landed, we wanted to acknowledge the trade-offs rather than pretend there was an easy fix. I'll definitely check out The Reflective State!
Thanks for this. I am surrounded by highly intelligent people and all of us (including myself have duped). It's impossible to believe anything anymore.
What a brilliant piece. Thank you; it's wonderful to wake up to your essay being the first thing I read. Well-researched, well-thought-out, and certainly well-written. Thank you
Really excellent read. “It’s not obscene, it’s just dishonest, and dishonesty, evidently, is not yet expensive enough to earn its own stopwatch.” Profound and very true.
That was a great read, quite frightening too, in a lot of ways. Congratulations both on putting this together. Will likely come back to this a few times.
I’m a software engineer, and have written image fingerprinting algorithms.
Yes, there’s a financial component to why copyright enforcement works so well, while deepfake enforcement doesn’t.
But the engineering problem for the latter is MANY orders of magnitude more difficult, and is simply not solvable to anywhere near the degree of accuracy music identification is.
You can answer the question “does this content contain these relative musical pitches sustained for these durations, with these relative gaps between them” using simple, relatively computationally inexpensive Fourier analysis, and not be wrong most of the time.
We have nothing quite like Fourier analysis for *people*. What comes closest is facial recognition algorithms, using ratios of facial feature distances. But they are VASTLY less accurate.
With any system like this, you can choose to err on the side of false positives or false negatives. Both are harmful. So the system needs to be highly accurate or it will do more harm than good.
So, yes, there’s a financial/political aspect to it, but in the case of music, you have the convergence of a mature music industry with money to throw at the problem, *combined with* a problem that is technically executable at scale, executable with high accuracy, at fairly low cost. That is simply not the case - in terms of accuracy, cost, or the existence of an organized lobby of … people with faces … with organizations like ASCAP and BMI already in the business of delivering royalties for use of those faces. So underlying it all is the collective action problem - a Google or an Apple has no use or capacity for millions of *individuals* approaching them with complaints - they spend a lot of money black-holing that sort of thing.
Then, there’s the legal issue: copyright is well-defined. There’s no such equivalent for a face. The closest I can think of is in the 90s when Billy Joel trademarked his face (yes, really, that was a thing) - but trademark law is nowhere near robust enough, since it contains a lot of caveats about how a trademark is used and the domain it applies to; whereas copyright straightforwardly is, if you use it, you are infringing, with a few minimal carve-outs for journalism and fair-use of small sections.
So, the legal regime that would support doing “content ID for faces” doesn’t really exist - not with the kind of bright-line, unambiguous tests that copyright offers.
If you want to solve this, start the organization that will act as the ASCAP or BMI of faces, mitigate tons of humans to register their faces through it (through a promise of monetization) so the Apples and Google’s of the world can costume that database and automate the process. And lobby your legislators for law that makes enforcement practically possible. And pray for engineering advancements in facial recognition, which isn’t as good as the people making money off it would like you to believe.
That's a really helpful explanation, thanks for sharing it. I totally agree that the engineering challenge is much harder. Our point wasn't that the two problems are equally difficult, but that platforms invest their efforts wherever the legal and economic pressure is strongest. That's why we focused on creators who already use Likeness Detection: even when the system finds a match, the victim still has to do most of the work. I think both the technical and institutional sides matter here and work together rather than against each other.
Wonderful Read
Very unique and fabulous work
Thanks for sharing and keep WRITING 💫
Thank you so much for reading!
Reading this, the comparison is unsettling precisely because it feels so logical. A machine can recognise three seconds of a song in an instant, yet a stolen face, voice, and reputation still depend on forms, reports, and patience. Very interesting piece and very well written.
Exactly, that contrast was really the starting point for the essay. Thank you so much for reading!
The wonderful, bright future that's already here. Thanks for making this louder 🖤
Thank you so much for reading!
I'm going to start billing you for the time I spend staring at your thumbnails
😂
Hi James. Good for you for doing what you feel you must do. I have wanted to do that for many years and finally got to it. I wrote a manuscript, a memoir called WALLFLOWER, that is awaiting publication. I can’t wait to share it with everyone. It tells the story of my traumatic childhood, living between the hearing and deaf worlds, how my animals and becoming a mother helped to heal me.
Oh sounds great. Thank you for telling me. When is it being published?
Hopefully soon!
This is a genuinely sharp argument, particularly the contrast at its centre: three seconds of copyrighted music can wake the machine, while a stolen human identity still waits for someone to fill out a form. That comparison makes the technological capability almost secondary to the more uncomfortable question of what our systems have actually been designed to value.
I also appreciated the nuance around biometric privacy. It would have been easy to argue simply that platforms should scan every face automatically, but acknowledging the danger inherent in that solution makes the eventual point much stronger: once someone has voluntarily verified their identity and consented to detection, why should protection still depend upon repeated manual intervention?
The line that stayed with me was, “What a company doesn’t bother measuring is usually what it has decided doesn’t need managing.” There is something much larger contained in that observation. We tend to think algorithms reveal technological priorities, when in reality they often reveal very human ones—particularly where money, liability, and consequence are concerned.
I write occasionally about geopolitics and the stranger contradictions of our modern systems at The Reflective State, so this intersection of technology, institutional priorities, and human consequence was particularly fascinating to me. I’m happily subscribing. If curiosity ever leads you my way, I’d be delighted to have you visit.
Thank you for such a close read and thoughtful comment.
Thank you. I'm really glad the point about voluntary verification landed, we wanted to acknowledge the trade-offs rather than pretend there was an easy fix. I'll definitely check out The Reflective State!
Absolutely incredible read! Thank you so much for sharing this ✨💫
Thank you 😊
Thank you for reading.
Terrifying
Yup.
Thank you for reading!
It was a beautifully written essay quite frightening but excellent ❤️
Thank you so much.
This was a fascinating and frightening read. I feel better informed now. And very clearly written. Thanks for sharing
Thank you so much.
Thanks so much! It was a tricky topic, so I'm glad it came out clear.
Thanks for this. I am surrounded by highly intelligent people and all of us (including myself have duped). It's impossible to believe anything anymore.
That's absolutely right. It's happening to everyone. Thank you for reading.
Thank you for reading! I don't think the takeaway is that we can't trust anything, but that we have to be a more careful about what earns our trust.
Absolutely.
What a brilliant piece. Thank you; it's wonderful to wake up to your essay being the first thing I read. Well-researched, well-thought-out, and certainly well-written. Thank you
Oh thank you so much!
Really excellent read. “It’s not obscene, it’s just dishonest, and dishonesty, evidently, is not yet expensive enough to earn its own stopwatch.” Profound and very true.
Thanks so much!
Thank you so much!
That was a great read, quite frightening too, in a lot of ways. Congratulations both on putting this together. Will likely come back to this a few times.
Thank you so much!
Thank you! We definitely came away with more questions than answers 😅 It turned out to be a much bigger topic than we expected.
I’m a software engineer, and have written image fingerprinting algorithms.
Yes, there’s a financial component to why copyright enforcement works so well, while deepfake enforcement doesn’t.
But the engineering problem for the latter is MANY orders of magnitude more difficult, and is simply not solvable to anywhere near the degree of accuracy music identification is.
You can answer the question “does this content contain these relative musical pitches sustained for these durations, with these relative gaps between them” using simple, relatively computationally inexpensive Fourier analysis, and not be wrong most of the time.
We have nothing quite like Fourier analysis for *people*. What comes closest is facial recognition algorithms, using ratios of facial feature distances. But they are VASTLY less accurate.
With any system like this, you can choose to err on the side of false positives or false negatives. Both are harmful. So the system needs to be highly accurate or it will do more harm than good.
So, yes, there’s a financial/political aspect to it, but in the case of music, you have the convergence of a mature music industry with money to throw at the problem, *combined with* a problem that is technically executable at scale, executable with high accuracy, at fairly low cost. That is simply not the case - in terms of accuracy, cost, or the existence of an organized lobby of … people with faces … with organizations like ASCAP and BMI already in the business of delivering royalties for use of those faces. So underlying it all is the collective action problem - a Google or an Apple has no use or capacity for millions of *individuals* approaching them with complaints - they spend a lot of money black-holing that sort of thing.
Then, there’s the legal issue: copyright is well-defined. There’s no such equivalent for a face. The closest I can think of is in the 90s when Billy Joel trademarked his face (yes, really, that was a thing) - but trademark law is nowhere near robust enough, since it contains a lot of caveats about how a trademark is used and the domain it applies to; whereas copyright straightforwardly is, if you use it, you are infringing, with a few minimal carve-outs for journalism and fair-use of small sections.
So, the legal regime that would support doing “content ID for faces” doesn’t really exist - not with the kind of bright-line, unambiguous tests that copyright offers.
If you want to solve this, start the organization that will act as the ASCAP or BMI of faces, mitigate tons of humans to register their faces through it (through a promise of monetization) so the Apples and Google’s of the world can costume that database and automate the process. And lobby your legislators for law that makes enforcement practically possible. And pray for engineering advancements in facial recognition, which isn’t as good as the people making money off it would like you to believe.
Thanks for your expertise. I appreciate it!
That's a really helpful explanation, thanks for sharing it. I totally agree that the engineering challenge is much harder. Our point wasn't that the two problems are equally difficult, but that platforms invest their efforts wherever the legal and economic pressure is strongest. That's why we focused on creators who already use Likeness Detection: even when the system finds a match, the victim still has to do most of the work. I think both the technical and institutional sides matter here and work together rather than against each other.
Sounds about right.
Thanks for making me research and write an essay, @Marble & Ember.
Grazie a mille.
Mission accomplished 😊 It was worth it though.