Retraction Watch reported on 16 October 2026 that the website of Springer Journals Online, an agency offering authorship slots in articles in exchange for payment, went offline four days after Retraction Watch published its investigation.
What it means for your work
Selling authorship corrupts the literature that AI systems and reviewers rely on, so citations tied to agencies like this need extra scrutiny.
Our read on the items that move something. The reporting is everyone's; this part is ours.
1
The adoption gap has closed but the gaps in frequency and trust have not, which matters for anyone designing AI products or training.Pew: men and women now use AI chatbots at similar rates, but men use them more oftenThe Data & Decision Science beat · Pew Research Center
2
Workspace archives contain other people's messages and documents. Anyone whose data sits in a startup's tools now has a stake in what happens to it at shutdown.AI labs are buying the code and workspace data of shut-down startups to train modelsThe AI Product & Business Strategy beat · MIT Technology Review
3
Decision models are being added to gateways and local runtimes fast; this is early independent evidence on where they are reliable and where they are not, especially for routing.Paired study finds hosted decision model Jev beats open-weight Laya on most agent decision pointsThe Data & Decision Science beat · arXiv
Following opens with early access — sign up for early access to register your interest; it does not switch following on today. Everything you follow will land on one page, in your account menu.
All coverage
Everything Intel has read, newest first. Each title opens at its original publisher.
Workspace archives contain other people's messages and documents. Anyone whose data sits in a startup's tools now has a stake in what happens to it at shutdown.
If agent loops rather than chat are driving a three-and-a-half-fold jump in six months, capacity planning is an agent-architecture question before it is a model one.
Enterprise model spend is compounding faster than the seat-based software it displaces — the number to hold up next to any internal business case that still treats model cost as an experiment budget.
A $100M run-rate on Lakebase after roughly a year is the number to watch: it is the clearest public read on how fast an AI-native database attaches to an existing data-platform install base.
A $54B valuation jump in five months tells you where enterprise AI money is landing: governance gateways, AI coworkers over company data, and agent-native databases — the implementation layer, not the models.