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Hello, diligent HCI readers! I hope everyone enjoyed their Memorial Day weekends. We’ve got a lot of numbers in today’s edition. Get out your abacus. And tell me if you want more or less math in here: bob.herman@statnews.com.
$775 billion
Centers for Medicare and Medicaid Services
Republicans’ recent tax law targets supplemental Medicaid funds that have increasingly propped up hospitals. The cuts are expected to be even bigger than originally forecast, which almost assuredly will provoke an opposition campaign from hospitals.
In Uganda’s fishing villages along lakes and rivers, wearable GPS devices are offering a new, detailed picture of how schistosomiasis spreads, helping refine control strategies for a disease affecting about 250 million people globally, mostly in rural sub-Saharan Africa.
A Nature Health study by researchers at the University of Oxford’s Big Data Institute shows that simple models using GPS-tracked movement can accurately predict which open-water sites people use, how often they visit them, and which sites are most likely to drive transmission of Schistosoma mansoni, the parasite that causes schistosomiasis.
“Snail fever”
Schistosomiasis, or “snail fever,” is caused by Schistosoma mansoni flatworms that parasitize freshwater snails. Infection occurs when people contact contaminated water where larval forms of the flatworm penetrate the skin. Repeated exposure leads to reinfection and chronic disease, including liver damage, portal hypertension, bladder fibrosis, kidney damage, and increased cancer risk.
While praziquantel can cure infection, mass drug administration (MDA) with the antiparasitic medication has failed to interrupt transmission of the parasite. The World Health Organization (WHO) notes that MDA alone is insufficient, since transmission persists in localized hotspots. Focal interventions are needed, but researchers have limited them due to poor data on where water contact actually occurs.
Conventional assumptions have limited modeling of human contact with open-water sites. Though open-water contact is heterogeneous within villages and households, it is often assumed that people use only the site closest to their household or village. However, how mobility affects site usage patterns and whether assignment rules beyond nearest distance can be more realistic are unclear.
Along with praziquantel MDA, the 2022 WHO schistosomiasis control guidelines recommend safe water, sanitation, and hygiene (WASH) as the main intervention, but evidence is mixed. Lack of reliable data has made it difficult to determine why the intervention did not improve biannual MDA. Most water contact and WASH studies use self-reported data or household distance to sites and taps, which lacks objective, spatially granular data to characterize fine-scale water usage patterns and quantify WASH’s impact on water contact and (re)infection.
Focal exposure
Lead author Fabian Reitzug, PhD, and colleagues from Goylette F. Chami’s lab tracked 452 people using GPS loggers in three Ugandan districts for 10 days. A total of 8,200 water contact events occurred at 69 open-water sites and 32 improved sources like taps and boreholes—deep drilling to groundwater. Of the participants, 63.9% used open water and 33.2% improved sources.
Reitzug and colleagues found, unsurprisingly, that distance strongly predicted behavior: usage dropped sharply with distance. Open-water contact was ~70% at 20 meters from home and 11% at 500 meters. Nearly all tap and borehole use occurred within 1 km of home, and over 99.5% of open-water contact occurred within 3 km, showing highly localized exposure.
Adding mobility metrics (such as “radius of gyration”) did not improve predictions. This simple finding challenges the assumption that mobile phone tracking can reliably estimate infection risk. Schistosomiasis exposure appears to be caused by local, routine movements, not long-distance travel.
The study found little evidence that safe water infrastructure reduces risky water contact. Taps and boreholes rarely replaced open-water use; fewer than 2% of people fully substituted safe water for natural sources. Daily activities like bathing, fishing, and washing still require lake or river contact. Behavior varied by location. In the Western Ugandan district of Buliisa, nearly 90% visited open water, compared with 44% in the Eastern Ugandan district of Mayuge.
When incorporated into transmission models, GPS-informed movement patterns closely reproduced observed reinfection rates. Simple “nearest-water-source” assumptions overestimated risk. The improved model also identified high-risk water sites by combining human use with ecological suitability for snail habitats.
Targeted interventions at key sites
These findings suggest control programs could shift toward targeted interventions at key transmission sites, such as focal snail control, environmental modification, or localized treatment. The study also indicates that a 1 km intervention radius may be more realistic than the current 500 m guideline. Importantly, reliable spatial patterns emerged from as few as 15 participants over 10 days, suggesting the approach is scalable. Key limitations include using proximity as a proxy for water contact and limited seasonal coverage.
Overall, the study reframes schistosomiasis transmission as a highly local, measurable process, enabling more precise, data-driven control strategies. Future research should examine whether similar models apply to other waterborne diseases. Identifying pathogen-specific exposure pathways and collecting GPS logger data from various locations could test this approach’s generalizability.
In a new study published in Nature Methods titled, “A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms,” researchers from University of California, Berkeley present high-powered microscopes that can track the development of live specimens, including cell movement through tissue, the evolution of internal cellular structures, and shuttling of proteins and other molecules within the cell. The system, namedMultimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), has been implemented in more than a dozen worldwide labs over the past six years.
“Life has to be studied in living tissue, holistically, and over fast timescales and for long periods of time,” said Eric Betzig, PhD, professor of molecular and cell biology at UC Berkeley, 2014 Nobel Prize in Chemistry, and co-corresponding author on the study. “You can’t study something as complex as a cell or organism just by looking at the parts individually—there are something like 40 million protein molecules alone of 20,000 different types.”
The microscope uses a large “vision” language model (LVLM), similar to ChatGPT, to measure petabytes of data, the equivalent of about 500 billion pages of text.
Betzig, who is also a Howard Hughes Medical Institute (HHMI) investigator, refers to the imaging data as five-dimensional (5D) composed of three spatial dimensions, plus time and color. The color comes from fluorescent labels that allow scientists to track multiple subcellular structures simultaneously, such as organelles, membranes, the cytoskeleton and more, as they migrate, change shape, divide and interact over time.
In one video, the authors capture a zebrafish regrowing its tail fin. The video revealed tiny events inside living tissue that are normally difficult to visualize, such as cells near the wound releasing small communication packets, microscopic fibers beneath the skin shifting as the tissue repaired itself, two repair cells fusing together and a red blood cell briefly getting trapped as new blood vessels were remodeled.
Ian Swinburne, PhD, assistant professor of molecular and cell biology at UC Berkeley and collaborator on the work, emphasizes that there’s a wealth of information in these large movies across scales, but it can be difficult for a very well-trained biologist to interrogate the data.
“AI can help us interface with the data and ask or answer questions more easily. Like, ‘How many macrophages are crawling into my tissue during an infection?’ or ‘Can I predict when a cell’s going to start leaving its organ?’ That happens in development but also in cancer during metastasis,” said Swinburne.
Building an LVLM or AI that can handle petabytes of imaging data is a main focus of Berkeley’s Advanced Bioimaging Center, which hopes to create a first-of-its-kind Cell Observatory.
“The impact of MOSAIC will be minimal until we build an AI model that can deal with the data that comes out of those systems. We basically have a gold mine, but we have no ability to get the gold out,” said Srigokul “Gokul” Upadhyayula, PhD, assistant professor in residence of cell biology, development and physiology at UC Berkeley. “The primary output of our Cell Observatory Initiative will be an AI mind that’s able to be our scientific partner in extracting these observations.”
Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution.
Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows.
The sticky tape problem
The challenge is that many organisations are often layering AI agents onto existing operations, rather than reimagine the operating model and how work will need to be rewired, explains Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting. “They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking.”
Doing so may be preventing organizations from unlocking the full value agentic AI offers, creating circumstances where disillusionment can quickly creep in. That full value lies in agents’ capacity to execute entire workflows with limited human input. They can coordinate complex tasks, make independent decisions, adjust to changing conditions, and iterate performance.
In early proving grounds that span customer service, HR, and sales, it’s already estimated that AI agents could accelerate business processes by as much as 30% to 50% and low-value work time by 25% to 40% when deployed at scale. But with this capability comes greater complexity and the need for an enterprise-wide change.
Growing the AI vocabulary
Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research, in an attempt to plug what it sees as a gap in the existing lexicon about AI agents, and to provide enterprises with a new framework with which to think about their own adoption of the technology.
“None of the existing vocabulary captures the full scope of the change,” explains Ema CEO and founder Surojit Chatterjee. “Digital transformation was about moving from paper to software. AI transformation was about adding artificial intelligence to existing processes. Co-pilot is about AI assisting in various human tasks. But ABT is something categorically different: It’s the integration of AI agents into the fabric of the organization.”
For Shah, the dedicated term (ABT) “helps drive the need to redesign an organization in its entirety: its operating model, its workflows, decision rights, and performance management systems.” He emphasizes that “everything that’s needed to ensure those agents are actually active participants in value creation, rather than just point tools or productivity aids.”
According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success.
AI agents as connective tissue
The first pillar of ABT is the technology stack. “Your existing tech stack was designed for human-operated, application-centric workflows,” says Chatterjee. “It needs to be reconsidered when the actor is an AI agent operating at machine speed across multiple systems simultaneously.”
As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.”
To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a new business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.”
The workforce, redesigned
As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT.
Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. But with AI agents that can execute, coordinate, and optimize tasks—often without managerial coordination—the lines of that established hierarchy become blurred.
In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on new responsibilities associated with managing hybrid teams. Managers “will need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics” to navigate new tensions that could arise in a hybrid workforce, says Shah.
The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration.
From output to outcome
Success metrics are the third and final pillar of ABT.
As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output—such as calls handled or reports filed—no longer make sense.
“When you add AI employees into the workforce, activity metrics become meaningless or actively misleading,” says Chatterjee. “An AI employee can handle a thousand customer interactions in the time it takes a human to handle ten. If you measure success by interactions handled, you’ll conclude the AI is working brilliantly while missing whether any of those interactions actually drove customer satisfaction, retention, or revenue.” To correct this, enterprises must develop a new set of metrics that focus on outcome rather than output. That is, metrics on the broader benefits or changes achieved, rather than individual deliverables.
For example, when one of Ema’s large enterprise customers overhauled its own metrics, switching from tool metrics like cost per query and AI accuracy, to outcomes like the percentage of contracts reviewed without human escalation, the measured ROI from agentic AI tripled within two quarters. The changes meant “this customer stopped building point solutions in high-volume, low-complexity workflows and started deploying AI employees where the outcome value was highest,” says Chatterjee.
Integrating new metrics may also require a complete reconfiguration of reward and talent management processes, as well as accountability and ownership within organizations, points out Shah. In human-AI teams, for example, although ethical and fiduciary responsibilities will likely remain with human employees, operational accountability will become significantly more diffused to reflect the systemic role of AI agents.
This change will raise new questions that senior leadership teams will need to wrestle with, Shah adds. They’ll need to consider: Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? What guardrails should be erected to safeguard customers?
Laying the groundwork for systems-level change
Systems-level change is gradual. These are complex lines of inquiry that experts continue to grapple with. But in kickstarting internal dialogue about the core pillars of ABT—the workforce, the technology stack, and the metrics by which success can be gauged—leaders can lay the groundwork for an enterprise better poised to embrace AI agents at a systems level and start to close the gap between their ambition and execution.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
The 79-year-old president spent more than three hours at Walter Reed National Military Medical Center for what the White House described as preventive medical and dental checkups. It was Trump’s fourth publicly disclosed medical exam since he returned to office for a second term, and it comes as he tries to project strength ahead of midterm elections that will test his sway with voters.
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Hi! Hope you had a nice extended weekend.
Today: Eli Lilly’s gene-editing data seems promising for high cholesterol, an AI drug discovery CEO dispelled some AI drug discovery hype, and the new interim FDA chief is so far well received.
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.
A reality check on the AI jobs hysteria
Despite the growing hysteria over AI’s threat to white-collar jobs, there’s still scant evidence that the technology has had a large-scale impact on the labor market.
Analysis of US labor data shows that unemployment in occupations most exposed to AI is actually lower than in less-exposed jobs. There are also no signs that large numbers of workers are shifting from AI-threatened professions into supposedly safer manual-labor jobs.
Opinion: It’s time to address the looming crisis in entry-level work
—Georgios Petropoulos, an assistant professor at the USC Marshall School of Business
AI has not yet produced mass unemployment. But it may be quietly weakening the first rung of the career ladder.
A recent Stanford study found that young workers in AI-exposed occupations suffered a sharp decline in employment after the spread of generative AI. The same pattern didn’t appear in low-exposure jobs, suggesting AI is replacing junior tasks that once gave young workers their first foothold.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The Pope has called for governments to regulate AI In his first major teaching document, Pope Leo said AI must be “disarmed.” (BBC) + He warned that AI fuels war and misinformation. (CNN) + But could also “open up a horizon extending in all directions.” (Engadget) + Anthropic cofounder Chris Olah also spoke at the event. (Reuters $)
2 SpaceX has launched its biggest and most powerful rocket The Starship V3 made its test flight debut two days after Elon Musk announced SpaceX’s IPO.(Guardian)+ SpaceX pulled off the launch, but not the landing. (Ars Technica) + The rocket could be key to SpaceX’s valuation. (Fortune $) + But rivals to the company are rising. (MIT Technology Review)
3 Huawei says it can make industry-leading chips within five years The Chinese tech giant announced a breakthrough in chip design. (Reuters $) + Its progress underscores Beijing’s push to neutralize US sanctions. (NBC) + Chinese chip stocks rallied after the announcement. (Bloomberg $)
4 A new vaccine may protect against the Ebola strain behind the current crisis Tests have shown promising results for the mRNA vaccine. (New Scientist) + Another Ebola vaccine that could be ready for trials in months. (BBC) + But vaccines face a new problem: their name. (MIT Technology Review)
5 A swimmer broke a world record at the ‘Steroid Olympics’ Athletes at the Enhance Games were encouraged to take dope. (Wired $) + Silicon Valley elites have backed the competition. (WP $) + Which fits right into 2026’s longevity vibes. (MIT Technology Review)
6 The EU plans to fine Google a massive antitrust penalty For allegedly favoring its own services in search results. (CNBC) + It would be the largest penalty for breaching the Digital Markets Act. (Reuters $)
7 US quantum computing subsidies may not be legal Congressional critics say the funding has been misused. (Ars Technica)
8 AI is minting new billionaires—and workers want their share The Samsung labor showdown reflects global concerns. (Rest of World)
9 China has launched artificial human embryos into orbit To find out whether we can reproduce beyond Earth. (Gizmodo)
10 Jony Ives has designed Ferrari’s first fully-electric car The legendary Apple designer has created a polarizing aesthetic. (FT $)
Quote of the day
“Technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate, and use it.”
—Pope Leo issues a warning about AI in his first encyclical letter, entitled ‘Magnifica humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence.”
One More Thing
ALYSSA SCHUKAR
How climate vulnerability and the digital divide are linked
In Anacostia, a historic African-American section of Washington, DC, Monica Sanders is measuring Wi-Fi speeds. It’s below the FCC’s minimum to qualify as a broadband service. She then checks the temperature: 46.9 °F.
Sanders, an adjunct professor of law at Georgetown University, frequently records this combination of weak internet access and environmental conditions. Her work shows how underinvestment in infrastructure can leave underserved communities more exposed to climate risks like extreme heat and flooding.
BackgroundPattern visual evoked potentials (pattern VEP) are widely used for functional assessment of the visual pathways. The P100 component represents the principal clinical parameter owing to its relative interindividual stability and diagnostic value. However, both latency and amplitude are modulated by multiple physiological and environmental factors, which complicates interpretation and the establishment of reliable reference standards. This scoping review aimed to systematically map determinants of P100 parameters in healthy individuals.Main textThe review was conducted in accordance with PRISMA-ScR and Joanna Briggs Institute methodology. Databases were searched for studies published between 2015 and 2025 that examined biological, refractive, anthropometric, metabolic, or environmental influences on pattern VEP parameters in healthy populations. Owing to methodological heterogeneity, findings were synthesized descriptively. Thirty-nine studies met the inclusion criteria. Age emerged as the most consistent determinant of P100 parameters. Latency followed a non-linear trajectory across the lifespan, with shortening during maturation, stabilization in early adulthood, and progressive prolongation after approximately 40 years of age, whereas amplitude generally declined with aging. Sex differences predominantly affected amplitude, with women typically demonstrating higher P100 or N75–P100 amplitudes in adult populations; latency differences were less consistent and often minimal in paediatric cohorts. Retinal image quality exerted a strong dose-dependent effect on P100 parameters: increasing refractive blur and higher-order aberrations were associated with progressive latency prolongation and amplitude reduction, particularly for small check sizes. Ocular dominance showed no clinically meaningful interocular asymmetry. Metabolic disturbances were associated with prolonged latency in selected populations, whereas anthropometric variables such as head size and height demonstrated weak or inconsistent associations. Among environmental factors, acute alcohol intake prolonged P100 latency, while moderate caffeine consumption had no significant effect.ConclusionAge and retinal image quality represent the primary physiological determinants of P100 latency and amplitude in healthy individuals. Most other modifiers exert modest or context-dependent effects. Consideration of these variables is essential for accurate interpretation of pattern VEP recordings and for establishing reliable local reference standards consistent with ISCEV recommendations.
