The policy menu. There’s no single answer. There’s a menu — and choosing is a values choice in disguise.

An analysis of the diverse policy options addressing AI-driven economic changes, emphasizing values over technical solutions and highlighting ongoing uncertainties.

The stake. Why the answer to automation is broad-based ownership, not a bigger transfer.

Thorsten Meyer argues that expanding capital ownership, not redistribution, is the market-friendly solution to AI-driven value shifts from labor to capital.

The referral. How AI search severs the content-for-traffic contract that funded the open web.

AI search now answers queries directly, ending the traditional referral traffic model that funded publishers, with significant implications for digital media.

The clause. How a contractual definition of AGI met the capital built on top of it.

An analysis of how a contractual definition of AGI in the Microsoft–OpenAI agreement was ultimately replaced by a verification process amid capital pressures.

The citation. Why generative engine optimization rewards the same brand on the least stable ground.

Generative Engine Optimization (GEO) is shaping how brands are cited in AI answers, favoring established entities and deepening content concentration.

The Ghost Story Became a Forecast.

Thorsten Meyer reports on Jack Clark’s recent essay revealing a bivalent forecast for AI development, with 60% probability by 2028 and 40% indicating fundamental paradigm limits.

Engineering Is Automated. Research Is the Residual.

Recent developments show AI has automated much of engineering work, leaving research as the remaining challenge. This shift could reshape AI development timelines.

The Machine Economy — Capital-Heavy, Human-Light, Trading With Itself

Analysis of the emerging machine economy where AI-driven firms operate with minimal human labor, reshaping markets and economic structures.

The Compounding Error Problem — Why 99.9% Alignment Decays to 60% in 500 Generations

Analysis of how 99.9% alignment accuracy degrades exponentially over multiple AI generations, raising concerns for recursive self-improvement.