A review exploring how the current artificial intelligence revolution is driven more by capital interests and hype than technological necessity. The piece examines Cory Doctorow’s argument that technology development is often steered toward maximizing investor returns rather than human empowerment, leading to a phenomenon known as "reverse centaurs" where workers lose autonomy and skill to machines. It critiques the industry's perceived inevitabilism and its similarities to the process of enshittification seen in other tech sectors.
* The economic motivations behind artificial intelligence hype
* Concept of reverse centaurs versus automation theory
* Critique of Big Tech business models
* Impact of capitalism on technological progress
* The economic motivations behind artificial intelligence hype
* Concept of reverse centaurs versus automation theory
* Critique of Big Tech business models
* Impact of capitalism on technological progress
Nobel laureate Daron Acemoglu critiques current optimism regarding AI productivity and economic narratives. He argues that much of the prevailing debate is speculative and fails to address critical issues like concentrated corporate power and extractive data models. Rather than focusing on whether capitalism is mutating, he suggests evaluating technology based on whether it fosters inclusive or extractive institutions.
>*Seen through that lens, AI is not troublesome in its own right, but rather whether it is positioned as inclusive or extractive. Today’s AI hyperscalers, he argues, fit the extractive mold almost perfectly: concentrated ownership, regulatory capture, and a business model that extracts data and attention at scale."
- Skepticism toward massive near-term AI productivity gains due to current model limitations
- The distinction between simple automation and true human-complementary tasks
- Potential social instability if significant job displacement occurs among younger generations
- A call for global governance and a focus on socially desirable technological outcomes
>*Seen through that lens, AI is not troublesome in its own right, but rather whether it is positioned as inclusive or extractive. Today’s AI hyperscalers, he argues, fit the extractive mold almost perfectly: concentrated ownership, regulatory capture, and a business model that extracts data and attention at scale."
- Skepticism toward massive near-term AI productivity gains due to current model limitations
- The distinction between simple automation and true human-complementary tasks
- Potential social instability if significant job displacement occurs among younger generations
- A call for global governance and a focus on socially desirable technological outcomes
This essay argues that the economics of context engineering expose a gap in the Brynjolfsson-Hitzig framework that changes its practical implications: for how enterprises build with AI, which firms centralize successfully, and whether the AI economy will be as centralized as their framework suggests. It explores how the cost and effort required to make knowledge usable by AI—context engineering—creates a bottleneck that prevents complete centralization, preserving the importance of local knowledge and human judgment. The article discusses the implications for SaaS companies, knowledge workers, and the future of work in an AI-driven economy, predicting that those who invest in context engineering capabilities will see the highest ROI.
Researchers have found that even seemingly random events, like the roll of a die, are governed by fundamental laws of physics. Their work provides further evidence for a long-held belief that the universe is fundamentally deterministic, even if it appears chaotic.
In essence, the study reinforces that the Boltzmann distribution isn't just *a* way to model randomness, it's *the* way to model truly independent random systems.
In essence, the study reinforces that the Boltzmann distribution isn't just *a* way to model randomness, it's *the* way to model truly independent random systems.
Details:
IS-LM 3B is StableLM 3B 4E1T(Licensed under CC BY-SA 4.0.) instruction tuned on DataForge Economics for 3 epochs with QLoRA(2305.14314).
IS-LM 3B is StableLM 3B 4E1T(Licensed under CC BY-SA 4.0.) instruction tuned on DataForge Economics for 3 epochs with QLoRA(2305.14314).
Behind one of the most iconic computer games of all time is a theory of how cities die—one that has proven dangerously influential.
