2026 predictions, explaining AI paper, journalism jobs and more

Early bird tickets end soon for the 2026 AI x Journalism Summit

2026 predictions, explaining AI paper, journalism jobs and more

Prediction: Journalism will stop relying on exposure to hold the powerful accountable

In an essay published as part of NiemanLab's Predictions for 2026, Hacks/Hackers strategic advisor Paul Cheung suggests the actors that journalism aims to hold accountable now operate inside systems designed to neutralize exposure, rather than respond to it.

Journalism, Paul writes, must abandon the idea that illumination drives accountability and begin changing the environment in which its work operates.


Building future AI news experiences with The Atlantic and Infactory (East Palo Alto, January 31, 2026)

Join Hacks/Hackers, The Atlantic’s product and tech team and Infactory in East Palo Alto on January 31 for for a hands-on day of innovation exploring how AI can responsibly transform the way people experience news.

Using The Atlantic’s archives and Infactory’s AI technology, you can hack on new approaches to curation, personalization and audience engagement using AI. Space is limited to 100 participants, and there is a $5,000 prize for the top project.


Early bird registration ends on Wednesday, December 24!

The Hacks/Hackers AI x Journalism Summit returns to Baltimore on May 13-14, 2026. Early bird registration end on December 24!

We hope to see you in Baltimore, and we hope you'll send us your pitches – use the submission form below. We’re also calling for pitches for the program, more info at the event link:


This giving season, donate to Hacks/Hackers

Billions of dollars are being spent on AI infrastructure and platforms, and the media transformation is moving even faster.

Journalism needs a seat at the table, not just to use these tools, but to shape them – ethically and in service of public trust.

Hacks/Hackers want to increase our impact even more in 2026, and are asking for your help with a donation that will have immediate impact.


AI papers explained

We're launching a new feature! AI Papers Explained is our experiment in using AI to help translate the latest AI research from arXiv into plain language for journalists and technologists. NOTE: The summaries are AI-generated and lightly edited, and so may contain errors or omissions. Our first summary: The best large language models get facts wrong about one-third of the time, according to a new benchmark.


AI x journalism news


Upcoming events


Journalism jobs