AI Publications

🔗https://www.anthropic.com/research/labor-market-impacts

Measuring AI’s labor-market impact requires caution because earlier attempts to predict disruption from new technologies have often been less accurate than expected. The paper notes that past forecasts around offshoring, robot adoption, and even official occupational projections have produced mixed or limited predictive value.

Anthropic presents this study as an attempt to build a more practical framework for tracking AI’s labor effects early, before the evidence becomes obvious in headline employment data. The authors say their goal is not to claim that major labor disruption has already happened, but to create a measurement system that can be updated over time and may detect vulnerability before displacement is visible.

AI’s labor effects are unlikely to look like a sudden shock such as COVID, where the signal was so large that causal inference was relatively straightforward. Instead, AI may resemble slower-moving structural changes like the spread of the internet or the…

🔗THE 2028 GLOBAL INTELLIGENCE CRISIS

Citrini Research’s “The 2028 Global Intelligence Crisis” is framed as a thought exercise written from the future (June 2028), not as a literal forecast. The authors are explicit about that. Their goal is to model a scenario that many AI bulls underweight: what if AI progress keeps exceeding expectations — and that very success becomes macroeconomically destabilizing? They position the essay as a left-tail risk map, not a doomer manifesto. 

The piece opens with a striking fictional macro snapshot: U.S. unemployment at 10.2%, the S&P down 38% from October 2026 highs, and markets already desensitized to bad labor prints. This future voice matters because the essay is written as a post-mortem, reconstructing how a “contained” AI disruption metastasized into a broad economic crisis in just two years. 

A central idea is the distinction between headline productivity and human economic participation. In the scenario,…

Fears of an AI-driven “jobpocalypse” are rising. Public figures warn that AI could hit labour markets like a tsunami, and the timing feels uncomfortable: unemployment is edging up in many advanced economies, entry-level roles are harder to find, and tech redundancies keep making headlines.

But the big claim — “AI is already destroying jobs at scale” — doesn’t hold up well under scrutiny. Labour markets did cool after ChatGPT’s release in November 2022, yet a slowdown that happens after a technology launch doesn’t prove the technology caused it.

Take the US, where AI investment has been most visible. Some observers argue that booming stock markets and falling job openings since around 2023 are proof that AI is boosting capital returns while squeezing workers. Zoom in, though, and the story shifts: job openings were already declining before ChatGPT arrived. A more straightforward explanation is macroeconomics. The Federal Reserve raised interest rates…

🔗https://foundationcapital.com/context-graphs-ais-trillion-dollar-opportunity/

The last era of enterprise software produced giants by becoming systems of record: the place where data and workflows “officially live.” Think CRM, ERP, HRIS—tools that define what happened inside a business.

AI agents won’t replace those systems. They’ll sit on top as the new interface: you ask, they act, they coordinate. But for agents to work reliably, they need more than clean APIs and better governance. They need the missing layer that actually runs most companies day to day: decision traces.

The missing layer: decision traces

In real operations, the important moments rarely look like a tidy workflow. They look like:

• an exception approved “just this once”