This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

What it’s like to be banned from the US for fighting online hate  

Just before Christmas the Trump administration dramatically escalated its war on digital rights by banning five people from entering the US. One of them, Josephine Ballon, is a director of HateAid, a small German nonprofit founded to support the victims of online harassment and violence. The organization is a strong advocate of EU tech regulations, and so finds itself attacked in campaigns from right-wing politicians and provocateurs who claim that it engages in censorship. 

EU officials, freedom of speech experts, and the five people targeted all flatly reject these accusations. Ballon told us that their work is fundamentally about making people feel safer online. But their experiences over the past few weeks show just how politicized and besieged their work in online safety has become. Read the full story. 

—Eileen Guo

TR10: AI companions

Chatbots are skilled at crafting sophisticated dialogue and mimicking empathetic behavior. They never get tired of chatting. It’s no wonder, then, that so many people now use them for companionship—forging friendships or even romantic relationships. 

72% of US teenagers have used AI for companionship, according to a study from the nonprofit Common Sense Media. But while chatbots can provide much-needed emotional support and guidance for some people, they can exacerbate underlying problems in others—especially vulnerable people or those with mental health issues. 

Although some early attempts to regulate this space are underway, AI companionship is going nowhere. Read why we made it one of our 10 Breakthrough Technologies this year, and check out the rest of the list.

And, if you want to learn more about what we predict for AI this year, sign up to join me for our free LinkedIn Live event tomorrow at 12.30pm ET.

Why inventing new emotions feels so good  

Have you ever felt “velvetmist”?  

It’s a “complex and subtle emotion that elicits feelings of comfort, serenity, and a gentle sense of floating.” It’s peaceful, but more ephemeral and intangible than contentment. It might be evoked by the sight of a sunset or a moody, low-key album.  

If you haven’t ever felt this sensation—or even heard of it—that’s not surprising. A Reddit user generated it with ChatGPT, along with advice on how to evoke the feeling. Don’t scoff: Researchers say more and more terms for these “neo-­emotions” are showing up online, describing new dimensions and aspects of feeling. Read our story to learn more about why. 

—Anya Kamenetz

This story is from the latest print issue of MIT Technology Review. If you haven’t already, subscribe now to receive the next edition as soon as it lands (and benefit from some hefty seasonal discounts too!)

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Ads are coming to ChatGPT 
For American users initially, with plans to expand soon. (CNN)
+ Here’s how they’ll work. (Wired $)

2 What will we be able to salvage after the AI bubble bursts? 
It will be ugly, but there are plenty of good uses for AI that we’ll want to keep. (The Guardian) 
+ What even is the AI bubble? (MIT Technology Review)

3 It’s almost impossible to mine Greenland’s natural resources 
It has vast supplies of rare earth elements, but its harsh climate and environment make them very hard to access. (The Week)

4 Iran is now 10 days into its internet shutdown
It’s one of the longest and most extreme we’ve ever witnessed. (BBC)
+  Starlink isn’t proving as helpful as hoped as the regime finds ways to jam it. (Reuters $)
+ Battles are raging online about what’s really going on inside Iran. (NYT $)

5 America is heading for a polymarket disaster 
Prediction markets are getting out of control, and some people are losing a lot of money. (The Atlantic $)
+ They were first embraced by political junkies, but now they’re everywhere. (NYT $)

6 How to fireproof a city 
Californians are starting to fight fires before they can even start. (The Verge $)
+ How AI can help spot wildfires. (MIT Technology Review)

7 Stoking ‘deep state’ conspiracy theories can be dangerous 
Especially if you’re then given the task of helping run one of those state institutions, as Dan Bongino is now learning. (WP $)
+ Why everything is a conspiracy now. (MIT Technology Review)

8 Why we’re suddenly all having a ‘Very Chinese Time’ 
It’s a fun, flippant trend—but it also shows how China’s soft power is growing around the globe. (Wired $) 

9 Why there’s no one best way to store information
Each one involves trade-offs between space and time. (Quanta $)

10 Meat may play a surprising role in helping people reach 100
Perhaps because it can assist with building stronger muscles and bones. (New Scientist $)

Quote of the day

“That’s the level of anxiety now – people watching the skies and the seas themselves because they don’t know what else to do.”

—A Greenlander tells The Guardian just how seriously she and her fellow compatriots are taking Trump’s threat to invade their country. 

One more thing

Inside a romance scam compound—and how people get tricked into being there

Gavesh’s journey started, seemingly innocently, with a job ad on Facebook promising work he desperately needed.

Instead, he found himself trafficked into a business commonly known as “pig butchering”—a form of fraud in which scammers form close relationships with targets online and extract money from them. The Chinese crime syndicates behind the scams have netted billions of dollars, and they have used violence and coercion to force their workers, many of them trafficked like Gavesh, to carry out the frauds from large compounds, several of which operate openly in the quasi-lawless borderlands of Myanmar.

Big Tech may hold the key to breaking up the scam syndicates—if these companies can be persuaded or compelled to act. Read the full story.

—Peter Guest & Emily Fishbein

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ Blue Monday isn’t real (but it is an absolute banger of a track.) 
+ Some great advice here about how to be productive during the working day.
+ Twelfth Night is one of Shakespeare’s most fun plays—as these top actors can attest. 
+ If the cold and dark gets to you, try making yourself a delicious bowl of soup. 

npj Digital Medicine, Published online: 19 January 2026; doi:10.1038/s41746-026-02362-6

LLM-driven collaborative framework for knowledge-enhanced cancer pain assessment and management

npj Digital Medicine, Published online: 19 January 2026; doi:10.1038/s41746-025-02295-6

UroFusion-X: a unified multimodal deep learning framework for robust diagnosis, subtyping, and prognosis of urological cancers

Nature Medicine, Published online: 19 January 2026; doi:10.1038/s41591-025-04168-7

A randomized trial provides direct human evidence that the placebo effect can shape humoral immunity, showing that reward-related brain activity correlates with vaccine-induced antibody production and opening new avenues for treating many medical conditions.

arXiv:2601.10718v1 Announce Type: new Abstract: Human papillomavirus (HPV) vaccine hesitancy poses significant public health challenges, particularly in Japan where proactive vaccination recommendations were suspended from 2013 to 2021. The resulting information gap is exacerbated by misinformation on social media, and traditional ways cannot simultaneously address individual queries while monitoring population-level discourse. This study aimed to develop a dual-purpose AI agent system that provides verified HPV vaccine information through a conversational interface while generating analytical reports for medical institutions based on user interactions and social media. We implemented a system comprising: a vector database integrating academic papers, government sources, news media, and social media; a Retrieval-Augmented Generation chatbot using ReAct agent architecture with multi-tool orchestration across five knowledge sources; and an automated report generation system with modules for news analysis, research synthesis, social media sentiment analysis, and user interaction pattern identification. Performance was assessed using a 0-5 scoring scale. For single-turn evaluation, the chatbot achieved mean scores of 4.83 for relevance, 4.89 for routing, 4.50 for reference quality, 4.90 for correctness, and 4.88 for professional identity (overall 4.80). Multi-turn evaluation yielded higher scores: context retention 4.94, topic coherence 5.00, and overall 4.98. The report generation system achieved completeness 4.00-5.00, correctness 4.00-5.00, and helpfulness 3.67-5.00, with reference validity 5.00 across all periods. This study demonstrates the feasibility of an integrated AI agent system for bidirectional HPV vaccine communication. The architecture enables verified information delivery with source attribution while providing systematic public discourse analysis, with a transferable framework for adaptation to other medical contexts.

arXiv:2601.10748v1 Announce Type: cross Abstract: Background: Artificial intelligence enabled electrocardiography (AI-ECG) has demonstrated the ability to detect diverse pathologies, but most existing models focus on single disease identification, neglecting comorbidities and future risk prediction. Although ECGFounder expanded cardiac disease coverage, a holistic health profiling model remains needed. Methods: We constructed a large multicenter dataset comprising 13.3 million ECGs from 2.98 million patients. Using transfer learning, ECGFounder was fine-tuned to develop AnyECG, a foundation model for holistic health profiling. Performance was evaluated using external validation cohorts and a 10-year longitudinal cohort for current diagnosis, future risk prediction, and comorbidity identification. Results: AnyECG demonstrated systemic predictive capability across 1172 conditions, achieving an AUROC greater than 0.7 for 306 diseases. The model revealed novel disease associations, robust comorbidity patterns, and future disease risks. Representative examples included high diagnostic performance for hyperparathyroidism (AUROC 0.941), type 2 diabetes (0.803), Crohn disease (0.817), lymphoid leukemia (0.856), and chronic obstructive pulmonary disease (0.773). Conclusion: The AnyECG foundation model provides substantial evidence that AI-ECG can serve as a systemic tool for concurrent disease detection and long-term risk prediction.

arXiv:2601.11505v1 Announce Type: cross Abstract: Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management datasets. Current datasets differ substantially in structure and are time-consuming to access and process, which impedes data integration and reduces the comparability and generalizability of algorithmic developments. This work aims to establish a unified and accessible data resource for T1D algorithm development. Multiple publicly available T1D datasets were consolidated into a unified resource, termed the MetaboNet dataset. Inclusion required the availability of both continuous glucose monitoring (CGM) data and corresponding insulin pump dosing records. Additionally, auxiliary information such as reported carbohydrate intake and physical activity was retained when present. The MetaboNet dataset comprises 3135 subjects and 1228 patient-years of overlapping CGM and insulin data, making it substantially larger than existing standalone benchmark datasets. The resource is distributed as a fully public subset available for immediate download at https://metabo-net.org/ , and with a Data Use Agreement (DUA)-restricted subset accessible through their respective application processes. For the datasets in the latter subset, processing pipelines are provided to automatically convert the data into the standardized MetaboNet format. A consolidated public dataset for T1D research is presented, and the access pathways for both its unrestricted and DUA-governed components are described. The resulting dataset covers a broad range of glycemic profiles and demographics and thus can yield more generalizable algorithmic performance than individual datasets.

npj Digital Medicine, Published online: 17 January 2026; doi:10.1038/s41746-026-02344-8

Wearable device derived electrocardiographic age and its association with atrial fibrillation

The startup has partnered with Eli Lilly and enjoys the backing of some of Silicon Valley's most influential VCs.

AI companies are clustering around healthcare and fast.  In just the past week, OpenAI bought health startup Torch, Anthropic launched Claude for healthcare, and Sam Altman-backed MergeLabs closed a $250 million seed round at an $850 million valuation. The money and products are pouring into health and voice AI, but so are concerns about hallucination risks, inaccurate medical information, and […]

Nature Cancer, Published online: 16 January 2026; doi:10.1038/s43018-025-01110-1

Yabut and Drucker discuss clinical and preclinical evidence about the potential roles of GLP-1 medicines on cancer incidence, development and therapy and speculate about their mechanism on cancer cells and the tumor microenvironment.

Nature Medicine, Published online: 16 January 2026; doi:10.1038/s41591-025-04073-z

Analyses of liver biopsies from a child with spinal muscular atrophy treated with adeno-associated virus gene therapy who developed hepatitis reveal contaminating manufacturing plasmids and disrupted vector genomes, possibly resulting from recombination events.

Background: Digital health services are increasingly used in hospital-based outpatient care, offering remote monitoring, patient-reported outcomes, information sharing, and asynchronous communication. While expected to improve self-management, timeliness, and efficiency, the success of digital health interventions relies on patients’ health literacy and digital health literacy. While some research has addressed potential associations between digital health interventions and patients’ health outcomes, research on patients’ experiences remains limited. Objective: The aim of this study was to explore and gain in-depth knowledge about the experiences of patients with chronic or long-term conditions enrolled in a 6-month digital outpatient care intervention for tailored care and health literacy. Methods: We conducted an exploratory qualitative interview study with 17 strategically recruited adult patients with cancer, interstitial lung disease, epilepsy, or complicated pain who used a digital outpatient service for 6 months. Individual telephone interviews were conducted using a semistructured guide, transcribed verbatim, and analyzed with thematic analysis to generate codes and themes. Participants had a median age of 62 years (minimum-maximum 36-83 years), with 8 females and 9 males. Results: The thematic analysis led to 1 main theme “Digital outpatient care as a flexible service supporting patients’ self-management,” informed by 3 subthemes “The ongoing nature of managing a chronic condition and how the digital service meet the patients’ desire for autonomy in their care,” “Digital tools flexibly address the patients’ unique needs, but reliability depends on patient interaction,” and “Digital services enhance the patients’ sense of safety through easy access to a relation with competent healthcare workers.” The themes highlight patients’ appreciation for greater flexibility in their care and their desire to self-manage with the support of easily accessible health care workers, ultimately supporting their health literacy. Patients recognized the importance of actively engaging with the digital solution to fully benefit from its opportunities and emphasized the critical role of health care workers in fostering their sense of security. Conclusions: Digital outpatient care was experienced as flexible and supportive for patients with long-term conditions. The increased possibility of interacting with health care workers was welcomed by the patients, and the combination of flexibility, self-monitoring, and addressing concerns regarding their self-management may increase the patients experience of autonomy. As health literacy likely plays a role in patients’ ability to effectively engage with digital tools and self-manage their conditions, future research should explore how varying levels of health literacy influence these outcomes. In addition, research should address whether such digital outpatient clinics are positive for a wider range of patients, associated health outcomes, and any positive effects on a health system level. Trial Registration: ClinicalTrials.gov NCT05068869; https://clinicaltrials.gov/ct2/show/NCT05068869

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arXiv:2601.08988v1 Announce Type: new Abstract: Reliable clinical decision support requires medical AI agents capable of safe, multi-step reasoning over structured electronic health records (EHRs). While large language models (LLMs) show promise in healthcare, existing benchmarks inadequately assess performance on action-based tasks involving threshold evaluation, temporal aggregation, and conditional logic. We introduce ART, an Action-based Reasoning clinical Task benchmark for medical AI agents, which mines real-world EHR data to create challenging tasks targeting known reasoning weaknesses. Through analysis of existing benchmarks, we identify three dominant error categories: retrieval failures, aggregation errors, and conditional logic misjudgments. Our four-stage pipeline -- scenario identification, task generation, quality audit, and evaluation -- produces diverse, clinically validated tasks grounded in real patient data. Evaluating GPT-4o-mini and Claude 3.5 Sonnet on 600 tasks shows near-perfect retrieval after prompt refinement, but substantial gaps in aggregation (28--64%) and threshold reasoning (32--38%). By exposing failure modes in action-oriented EHR reasoning, ART advances toward more reliable clinical agents, an essential step for AI systems that reduce cognitive load and administrative burden, supporting workforce capacity in high-demand care settings

arXiv:2601.09072v1 Announce Type: new Abstract: Developing safe, effective, and practically useful clinical prediction models (CPMs) traditionally requires iterative collaboration between clinical experts, data scientists, and informaticists. This process refines the often small but critical details of the model building process, such as which features/patients to include and how clinical categories should be defined. However, this traditional collaboration process is extremely time- and resource-intensive, resulting in only a small fraction of CPMs reaching clinical practice. This challenge intensifies when teams attempt to incorporate unstructured clinical notes, which can contain an enormous number of concepts. To address this challenge, we introduce HACHI, an iterative human-in-the-loop framework that uses AI agents to accelerate the development of fully interpretable CPMs by enabling the exploration of concepts in clinical notes. HACHI alternates between (i) an AI agent rapidly exploring and evaluating candidate concepts in clinical notes and (ii) clinical and domain experts providing feedback to improve the CPM learning process. HACHI defines concepts as simple yes-no questions that are used in linear models, allowing the clinical AI team to transparently review, refine, and validate the CPM learned in each round. In two real-world prediction tasks (acute kidney injury and traumatic brain injury), HACHI outperforms existing approaches, surfaces new clinically relevant concepts not included in commonly-used CPMs, and improves model generalizability across clinical sites and time periods. Furthermore, HACHI reveals the critical role of the clinical AI team, such as directing the AI agent to explore concepts that it had not previously considered, adjusting the granularity of concepts it considers, changing the objective function to better align with the clinical objectives, and identifying issues of data bias and leakage.

arXiv:2601.09152v1 Announce Type: new Abstract: This paper introduces PRA, an AI-agent design for simulating how individual users form privacy concerns in response to real-world news. Moving beyond population-level sentiment analysis, PRA integrates privacy and cognitive theories to simulate user-specific privacy reasoning grounded in personal comment histories and contextual cues. The agent reconstructs each user's "privacy mind", dynamically activates relevant privacy memory through a contextual filter that emulates bounded rationality, and generates synthetic comments reflecting how that user would likely respond to new privacy scenarios. A complementary LLM-as-a-Judge evaluator, calibrated against an established privacy concern taxonomy, quantifies the faithfulness of generated reasoning. Experiments on real-world Hacker News discussions show that \PRA outperforms baseline agents in privacy concern prediction and captures transferable reasoning patterns across domains including AI, e-commerce, and healthcare.