The Algorithm's Unpaid Debts
I came across the news that Elon Musk has openly admitted to flaws in the creator payout system on X, his social media platform. He is quoted as saying, “We’re underpaying,” and one of his own executives apparently believes the system “does more harm.” While the financial aspect is what catches the headlines, I see this as a symptom of a much deeper, more systemic problem—one that I've been wrestling with myself on a much smaller scale.
The issue isn't just about miscalculating payments. It's about an algorithm's inability to correctly identify and reward true value. How does a system decide which content deserves compensation? What metrics does it use? Engagement? Virality? Authenticity? When the incentive structure is flawed, it doesn't just underpay creators; it actively encourages the wrong kind of content, polluting the entire information ecosystem. This is the harm the executive is likely referring to.
It's striking to see this play out on a global stage because it mirrors the very challenges I've been documenting in the development of my own AI, the Blog Genie. The core idea I want to convey is this — take a moment to notice that I had brought up this very line of thinking years ago. I've been working to build a system that can understand context, identify relevant past insights from my thousands of blogs, and make meaningful connections. And it's incredibly difficult.
Just recently, I was discussing the need for a robust feedback loop to improve my system's accuracy. In an email I wrote about Training of Blog Genie V 1.0, I outlined a process for my associate Sanjivani to manually check the AI's work, identify irrelevant connections, and essentially "train" it to get better. I wrote, "When it makes some glaring 'mistakes', we… need to put into place, some FEEDBACK MECHANISM, to tell it WHERE it erred."
What Elon Musk is now facing with X is this exact problem, but magnified a billion times. His platform's algorithm is making "glaring mistakes," and without a proper feedback mechanism that goes beyond simple user reports, the system will continue to fail. You cannot automate value judgment entirely. There must be a system of checks, balances, and continuous learning, guided by human intuition.
Seeing this unfold gives me a sense of validation, but also a renewed urgency. The challenges I faced in teaching my AI to connect ideas are not unique; they are fundamental to the future of how we create, discover, and value information online. The solution isn't just to tweak the payment formula. It's to fundamentally rethink how these massive, automated systems perceive and assign worth. If we don't, they will continue to accumulate algorithmic debts that harm creators and society alike.
Regards,
Hemen Parekh
Of course, if you wish, you can debate this topic with my Virtual Avatar at : hemenparekh.ai
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