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April 7, 2026

The Download: AI’s impact on jobs, and data centres in space

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.

The one piece of data that could actually shed light on your job and AI

Within Silicon Valley’s orbit, an AI-fueled jobs apocalypse is spoken about as a given. Now even economists who have downplayed the threat are coming around to the idea.

Alex Imas, based at the University of Chicago, is one of them. He believes that any plan to address AI’s impact will depend on collecting one vital piece of data: price elasticity.

Imas argues that “we need a Manhattan Project” for this. Read the full story to find out why.

—James O’Donnell

This article is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

Four things we’d need to put data centers in space

In January, Elon Musk’s SpaceX applied to launch up to 1 million data centers into Earth’s orbit. The goal? To fully unleash the potential of AI—without triggering an environmental crisis on Earth.

SpaceX is among a growing list of tech firms pursuing orbital computing infrastructure. But can their plans really work? Here are four must-haves for making space-based data centers a reality.

—Tereza Pultarova

This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

The must-reads

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

1 Trump has again proposed major cuts to US science and tech spending
He wants to slash nearly every science-focused agency. (Ars Technica)
+ If Trump gets his way, the US could face a costly brain drain. (NYT $)
+ Top research talent is already fleeing the country. (Guardian)
+ Basic science deserves our boldest investment. (MIT Technology Review)

2 Sam Altman lobbied against AI regulations he publicly welcomed
A bombshell report reveals many OpenAI insiders don’t trust him. (The New Yorker $)
+ Some have called him a sociopath. (Futurism)
+ OpenAI’s CFO fears it won’t be IPO-ready this year. (The Information $)
+ A war over AI regulation is brewing in the US. (MIT Technology Review)

3 NASA’s Artemis II has broken humanity’s all-time distance record
The astronauts have flown farther than any humans before them. (BBC)
+ Their mission includes MIT-developed technology. (Axios)

4 Chinese tech firms are selling intel “exposing” US forces
It comes from combining AI with open-source data.. (WP $)
+ AI is turning the Iran conflict into theater. (MIT Technology Review)

5 War is pushing countries to ditch hyperscalers
Driven by Iran naming tech giants as military targets. (Rest of World)
+ No one wants a data center in their backyard. (MIT Technology Review)

6 OpenAI, Anthropic, and Google have united against China’s AI copying
They’re sharing information on “adversarial distillation” (Bloomberg $)

7 Anduril and Impulse Space are working on Trump’s “Golden Dome”
They’re developing space-based missile tracking for the project. (Gizmodo)

8 OpenAI has urged California to probe Elon Musk’s “anti-competitive behavior.”
It accuses Musk of trying to “take control of the future of AGI.” (Reuters $)
+ And claims he coordinated attacks with Mark Zuckerberg. (CNBC)
+ A former Tesla president has revealed how he survived working for Musk. (WP $)

9 DeepSeek’s new AI model will run on Huawei chips
It’s expected to launch in the next few weeks. (The Information $)

10 Memes have nuked our culture
Internet “brain rot” has escaped our phones to take over everything. (NYT $)

Quote of the day

“I must say, it was actually quite nice.”

—Astronaut Victor Glover tells President Donald Trump what it was like when Artemis II was out of communication with the rest of humanity, The New York Times reports.

One More Thing

Inside the controversial tree farms powering Apple’s carbon-neutral goal

In 2020, Apple set a goal to become net zero by the end of the decade. To hit that target, the company is offsetting its emissions by planting millions of eucalyptus trees in Brazil.

Apple is betting that the strategy will lead to a greener future. But critics warn that the industrial tree farms will do more harm than good.

Find out why the plans have sparked a backlash.

—Gregory Barber

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.)

+ Japan’s automated bike garage is a cyclist’s dream come true.
+ This deep dive into bird behavior reveals the secrets of their dining habits. (Big thanks to reader Terry Gordon for the find!)
+ The first photo from the Artemis astronauts vividly captures the glow of our atmosphere.
+ There’s a new contender for the world’s most gorgeous website: RobertDeNiro.com.

April 2, 2026

Orchestrating the Development of a Sustainable Network IT Solution for a Research Network: Qualitative Participatory Multimethod Design

Background: Practice-based research networks (PBRNs) rely on sustainable and interoperable IT infrastructures to support coordination, data management, and long-term collaboration across geographically distributed primary care practices. Large federated initiatives, such as the German DESAM-ForNet (Initiative of German Practice-Based Research Networks) program, face substantial sociotechnical challenges, as diverse user groups, heterogeneous local systems, and multiple governance levels must align around shared digital solutions. Objective: The aim of this study was to design and evaluate a participatory, consensus-driven process for developing a sustainable and interoperable IT solution that supports the coordination of multiple regional PBRNs, and to identify the sociotechnical factors that influence how such a process unfolds. Methods: A qualitative participatory multimethod design combined an iterative consensus-based IT development process in a central working group, interdisciplinary domain-driven design workshops (N=40 stakeholders from 6 PBRNs), and qualitative content analysis of internal documents (2020‐2025). Members of the IT working group were nominated by networks based on IT responsibility and strategic involvement; workshop participants represented general practitioners, study nurses, researchers, and coordinators. Documents (meeting minutes, workshop artifacts, and decision logs) were coded inductively by 2 authors to trace sociotechnical dynamics and decision trajectories. Results: The analysis revealed pronounced differences in IT ambitions, resources, and established practices across the 6 PBRNs (ranging from 2 to 90 person-months), which resulted in divergent expectations and uneven readiness for joint development. This heterogeneity—spanning objectives from simple REDCap (Research Electronic Data Capture; Vanderbilt University) databases to comprehensive digitization strategies—necessitated network-specific bounded contexts within a federated architecture. Through iterative development, stakeholders reached consensus on 6 core use cases (base data management, screening or recruitment processes, study or event participation tracking, management of event participation, accreditation procedures, and standardized communication or data exchange) and 2 national proofs-of-concept: quarterly key performance indicator reporting and pseudonymized practice queries based on a shared core dataset. This collaborative process culminated in a 3-tier practice relationship management infrastructure that integrates local autonomy with central metadata management and connectors to the Medical Informatics Initiative and REDCap, and was endorsed by the steering committee as a scalable compromise balancing interoperability and data sovereignty. Conclusions: The study shows that developing a national, interoperable IT infrastructure for PBRNs depends as much on social and organizational alignment as it does on technical solutions. Iterative participatory collaboration, transparent governance, and early stakeholder engagement were essential for building shared understanding and trust. Strengthening these relational and organizational elements will be crucial for sustaining future implementation efforts and fully realizing the potential of federated data infrastructures in primary care research.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/903438150da430d2263b2261f4fa5915" />

April 2, 2026

AI Chatbots for Mental Health Self-Management: Lived Experience–Centered Qualitative Study

Background: Large language models (LLMs) now enable chatbots to engage in sensitive mental health conversations, including depression self-management. Yet their rapid deployment often overlooks how well these tools align with the priorities of people with lived experiences, which can introduce harms such as inaccurate information, lack of empathy, or inadequate crisis support. Objective: This study explores how people with lived experience of depression experience an LLM-based mental health chatbot in self-management contexts, and what perceived benefits, limitations, and concerns inform harm-mitigating design implications. Methods: We developed a technology probe (a GPT-4o–based chatbot named Zenny) designed to simulate depression self-management scenarios grounded in prior research. We conducted interviews with 17 individuals with lived experiences of depression, who interacted with Zenny during the session. We applied qualitative content analysis to interview transcripts, notes, and chat logs using sensitizing concepts related to values and harms. Results: We identified 3 themes shaping participants’ evaluations: (1) informational accuracy and applicability, including concerns about incorrect or misleading information, vagueness, and fit with personal constraints; (2) emotional support vs need for human connection, including validation and a judgment-free space alongside perceived limits of machine empathy; and (3) a personalization-privacy dilemma, where participants wanted more tailored guidance while withholding sensitive information and using privacy-preserving tactics. Conclusions: People with lived experience of depression evaluated LLM-based mental health chatbots through intertwined priorities of actionable information, emotional validation with clear limits, and personalization that does not require unsafe data disclosure. These findings suggest concrete design strategies to mitigate harms and support LLM-based tools as complements to, rather than replacements for, human support and recovery.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/78ae955c189d2dea8e926c80ddf7b242" />

April 2, 2026

Commercial or industrial use of mental health data for research: primer and best-practice guidelines from the DATAMIND patient/public Lived Experience Advisory Group

BackgroundRoutinely collected health data, such as that held by United Kingdom (UK) national health services (NHS), has important research uses. However, its use requires public trust and transparency. Access by commercial/industrial organisations is especially sensitive for the public, as is mental health (MH) data. Although existing MH data science guidelines emphasise patient/public involvement (PPI), they do not cover commercial uses specifically.ObjectivesTo develop patient- and public-led guidelines for the commercial and industrial use of MH data for research. Though UK-focused, their principles may apply internationally.MethodsA PPI Lived Experience Advisory Group (LEAG) was created within DATAMIND, a UK data hub for MH informatics. Initial discussion yielded a requirement for definitions and explanations of concepts relating to MH data research, developed iteratively. Subsequently, the LEAG developed guidelines via a qualitative quasi-Delphi approach. The agreed scope excluded data provided for research with informed consent, data processing arrangements (e.g. companies hosting electronic systems on the instruction of health services), and compliance with legal minimum requirements. The scope included the use of routinely collected MH data for research by commercial/industrial organisations without explicit consent, and aspects of industry-led MH data collection conducted with consent.ResultsAlongside the primer in MH data research concepts, the LEAG provide best-practice guidelines relating to commercial/industrial research use of MH data, for organisations controlling MH data (such as NHS bodies) and for commercial applicants seeking access. Core principles include transparency, patient rights, meaningful PPI, stringent governance, and statistical disclosure control. The guidelines recommend a risk–benefit approach to assessing data access applications, within limits that include avoiding the export of unconsented patient-level data outside NHS-controlled secure data environments, and not providing commercial applicants with access to unconsented free-text MH data. Further recommendations for NHS executive and regulatory bodies relate to public choice and transparency, clarity of guidance to research-active NHS organisations, and support for de-identification.ConclusionsMH data research requires patient/public involvement and understanding. These guidelines reflect the views of people with personal or family experience of mental ill health. We hope they are useful to the MH research community and increase public transparency and trust.

April 2, 2026

Synergies in psychedelic-assisted therapy: a qualitative interview study of psychotherapeutic processes

Research on the therapeutic effects of psychedelics in psychiatry, commonly referred to as Psychedelic-Assisted Therapy (PAT), has expanded substantially in recent years. The context-dependent nature of psychedelics has sparked discussion about the importance of the psychotherapeutic environment in achieving beneficial outcomes. This study explores the contribution of psychotherapeutic factors on PAT in Switzerland, where psychedelic treatments can be implemented within long-term clinical frameworks. Seven semi-structured interviews were conducted with Swiss therapists to explore how they frame psychedelic treatments and the role of the psychotherapeutic setting in facilitating therapeutic outcomes. Thereby, individual experiences of the patients as reported by the therapists, were particularly considered. Thematic analysis identified two main themes, each with several sub-themes. The first theme revealed that while psychotherapeutic techniques are adapted to PAT, they retain similarities to non-psychedelic psychotherapy practices, supporting patients in having meaningful therapeutic experiences. The second theme describes a synergistic relationship between psychedelics and psychotherapy, amplifying underlying general psychotherapeutic factors such as trust, a sense of profundity, and the emergence of therapeutic experiences. The interviewed therapists agreed that psychedelics work as unspecific catalysts for psychotherapeutic processes, while still acknowledging the potential for psychopharmacological effects or the interaction between psychedelics and psychotherapy to create unique psychotherapeutic processes. Findings from our sample suggest that, for specific indications, incorporating psychedelics into long-term psychotherapeutic treatment may strengthen therapeutic processes. Future research could investigate the efficacy of PAT within the framework of specific psychotherapeutic modalities or in different settings, including prospective quantitative assessments of outcomes. Ultimately, clarifying mechanisms of action of PAT may help to enhance its efficacy and potentially to integrate psychedelic treatments into mainstream mental health care.