Founder of SAGEWORKS AI — building the Web4 layer where AI, blockchain & time flow as one. Creator of Mind’s Eye and BinFlow. Engineering the future of temporal, network-native intelligence.
What I take away from this isn’t “AI good” or “AI bad,” but that we’re rushing forward without agreeing on accountability.
A lot of the debate feels misplaced. People talk as if there’s a clean line between “developers” and “AI developers,” but there really isn’t. This is still software engineering — just at a different abstraction layer. The interface between humans and code keeps moving upward, the underlying systems don’t disappear. They just get harder to see, which is where the real risk starts.
What worries me most is not replacement, but opacity. We’re embedding AI into the core of systems while losing the ability to trace why an output happened, who influenced it, and what assumptions were baked in. Platforms get flooded with machine-generated content, and trust erodes — not because people are stupid, but because the signal-to-noise ratio collapses.
The energy point also hits hard. There’s something deeply off about framing AI as inevitable progress while externalizing the cost — environmentally and socially. We’re told individuals should optimize their consumption, while massive systems quietly scale on diesel generators and emergency infrastructure. That imbalance doesn’t feel accidental.
I also appreciate the mention of Anthropic’s research. That kind of work adds needed friction to the hype. If AI amplifies existing inequalities — between countries, companies, or individuals — then “efficiency” isn’t a neutral outcome anymore. It’s a choice.
On the technical side, the post touches something developers don’t talk about enough: latency, vendor lock-in, and dependency depth. AI isn’t just another library. It introduces network delay, compute constraints, pricing power, and security surfaces you don’t fully control. Once it’s central, walking away isn’t trivial.
I don’t read this as anti-AI. I read it as anti-amnesia.
If we treat AI as a tool, fine. But tools need provenance, limits, and memory — not just outputs. Without that, we’re not automating intelligence, we’re just accelerating complexity and hoping it behaves.
You are right it not anti AI. I do think the technology that powers AI is great. But it is also still rough around the edges.
As developers we are taught not only write happy pad tests if we want to build software that lasts.
We also need to see the negative consequences of a decision, and mitigate those.
There are too many bugs that start with the mindset of; that will never happen.
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What I take away from this isn’t “AI good” or “AI bad,” but that we’re rushing forward without agreeing on accountability.
A lot of the debate feels misplaced. People talk as if there’s a clean line between “developers” and “AI developers,” but there really isn’t. This is still software engineering — just at a different abstraction layer. The interface between humans and code keeps moving upward, the underlying systems don’t disappear. They just get harder to see, which is where the real risk starts.
What worries me most is not replacement, but opacity. We’re embedding AI into the core of systems while losing the ability to trace why an output happened, who influenced it, and what assumptions were baked in. Platforms get flooded with machine-generated content, and trust erodes — not because people are stupid, but because the signal-to-noise ratio collapses.
The energy point also hits hard. There’s something deeply off about framing AI as inevitable progress while externalizing the cost — environmentally and socially. We’re told individuals should optimize their consumption, while massive systems quietly scale on diesel generators and emergency infrastructure. That imbalance doesn’t feel accidental.
I also appreciate the mention of Anthropic’s research. That kind of work adds needed friction to the hype. If AI amplifies existing inequalities — between countries, companies, or individuals — then “efficiency” isn’t a neutral outcome anymore. It’s a choice.
On the technical side, the post touches something developers don’t talk about enough: latency, vendor lock-in, and dependency depth. AI isn’t just another library. It introduces network delay, compute constraints, pricing power, and security surfaces you don’t fully control. Once it’s central, walking away isn’t trivial.
I don’t read this as anti-AI. I read it as anti-amnesia.
If we treat AI as a tool, fine. But tools need provenance, limits, and memory — not just outputs. Without that, we’re not automating intelligence, we’re just accelerating complexity and hoping it behaves.
And hope is a bad systems strategy.
Thank you for your thoughtful response.
You are right it not anti AI. I do think the technology that powers AI is great. But it is also still rough around the edges.
As developers we are taught not only write happy pad tests if we want to build software that lasts.
We also need to see the negative consequences of a decision, and mitigate those.
There are too many bugs that start with the mindset of; that will never happen.