AI Publications

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”

Many organisations are stuck in Proof-of-Concept mode: they keep producing impressive AI demos that look great in presentations, but never become real products embedded in daily work. The problem usually isn’t that the models “don’t work.” It’s that teams build in isolation—without clear ownership, without an integration plan, and without repeatable delivery habits. Over time, the volume of activity goes up… but the business value stays flat.

A big part of the issue is how AI is being approached. Many companies treat AI like a single, centralised transformation—something you “roll out” top-down—when in reality it behaves more like a capability that grows through smaller components, fast feedback loops, and iterative improvement. We’re repeating the early, pre-Agile software era… but with higher stakes, because AI now touches customer experience, operations, and trust.

That’s why so many AI initiatives derail. Not because the technology is broken, but because the delivery model is. The…

🔗https://www.gallup.com/workplace/701195/frequent-workplace-continued-rise.aspx

This Gallup research tracks how often U.S. employees use AI at work and how that’s changing over time. In Q4 2025, usage “intensity” rose modestly among people who already use AI: daily use edged up (from 10% to 12% since 2023) and “frequent” use (at least a few times a week) reached 26%.

But the overall share of employees who use AI at least occasionally (a few times a year) was flat in Q4, and 49% say they never use AI in their role—a reminder that workplace AI adoption is still very uneven.

The report also notes that organizational integration appears relatively steady: 38% of employees say their organization has integrated AI to improve productivity/efficiency/quality, while 41% say it has not, and 21% aren’t sure.

AI use varies by industry and role type

AI use clusters strongly where work is information-heavy and tool-friendly. Gallup finds the highest usage in technology, finance,…