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Did you know the U.S. Mint requires gold coins to be made with American-made gold, but instead it gets illegally mined gold that can be traced back to a Colombian drug cartel? Truly mind-blowing stuff from this New York Times investigation. Let me know what the health care angle is on that one: bob.herman@statnews.com.
It’s tough to make predictions, especially about the future
Two years ago, my old pal Rachel Cohrs Zhang and I reported how Medicare’s actuaries predicted the new Alzheimer’s drug Leqembi would cost the program $3.5 billion in 2025. It turns out that prediction was way off.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising—an overhaul of all white-collar work—Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It’s okay at automating certain tasks, he wrote, but some jobs will be perfectly fine.
Two years later, Acemoglu’s measured take has not caught on. Chatter about an AI jobs apocalypse pops up everywhere from Senator Bernie Sanders’s rallies to conversations I overhear in line at the grocery store. Some previously skeptical economists have gotten more open to the idea that something seismic could be coming with AI. A California gubernatorial candidate said last week that he wants to tax corporate AI use and pay victims of “AI-driven layoffs.”
On the one hand, the data is still on Acemoglu’s side; studies repeatedly find that AI is not affecting employment rates or layoffs. But the technology has advanced quite a bit since his cautious predictions. I spoke with him to understand if any of the latest developments in AI have changed his thesis, and to find out what does worry him these days if not imminent AGI.
AI agents
One of the biggest technical leaps in AI since Acemoglu’s paper has been agentic AI, or tools that can go beyond chatbots and operate on their own to complete the goal you give them. Because they can work independently rather than just answering questions, companies are increasingly pitching agents as a one-to-many replacement for human workers.
“I think that’s just a losing proposition,” Acemoglu says. He thinks agents are better thought of as tools to augment particular pieces of someone’s work than something malleable enough to handle a person’s whole job.
One reason has to do with all the various tasks that go into a job, something Acemoglu has been researching in his work on AI since 2018. For example, an x-ray technician juggles 30 different tasks, from taking down patient histories to organizing archives of mammogram images. A worker can naturally switch between formats, databases, and working styles to do this, Acemoglu says, but how many individual tools or protocols would an AI require to do the same?
Whether or not agents will supercharge AI’s impact on jobs will come down to whether they can eventually handle the orchestration between tasks that humans do naturally. AI companies are in heated competition to prove that their AI agents can work independently for ever longer periods without making mistakes, sometimes exaggerating the results—but Acemoglu says many jobs will be spared from an AI takeover if agents can’t fluidly switch between tasks.
The new hiring spree
For years Big Tech has been offering staggering salaries to recruit AI researchers. But I asked Acemoglu about a different hiring spree I’ve noticed: AI companies are all building in-house economics teams.
OpenAI hired Ronnie Chatterji from Duke University in 2024 to be its chief economist and announced last year that Chatterji will work with Jason Furman—Harvard economist and former advisor to Barack Obama—to research AI and jobs. Anthropic has convened a group of 10 leading economists to do similar work. And just last week, Google DeepMind announced it had hired Alex Imas, an economist from the University of Chicago, to be its “director of AGI economics.”
Acemoglu has noticed colleagues getting snatched up for these roles too. “It makes sense,” he says: AI companies are well aware that public skepticism about AI, in large part due to job concerns, is growing. And they have strong incentives to shape the economic narrative around their technology (consider OpenAI’s latest proposal for a new era of industrial policy).
“What I hope we won’t get,” Acemoglu says, “is that they’re interested in economists just to further their viewpoints or further the hype.” That tension hangs over the emerging field of “AI economics”; it’s concerning that some of the most influential research about AI’s impact on work may increasingly come from the companies with the most to gain from favorable conclusions.
AI apps
I don’t think of AI as hard to use; most of us interact with it via chatbots that use plain language. But Acemoglu says we should consider how it compares with the sort of software that kicked off earlier tech transformations, like PowerPoint for slide decks and Word for documents.
“Anybody could install these on their computer and get them to do the things that they want them to do,” he says. They spread accordingly.
“We have not seen the development of apps based on AI that have the same usability,” he says. Even if anyone can chat with an AI model, it tends to take a while for the average worker to get practical and productive use out of it. That’s part of the reason why AI has not yet shown any seismic impact on the job market or the economy. One of the key signals Acemoglu is watching, then, is the creation of apps that make AI easier to use.
But he acknowledges that for a while, we’re going to see all sorts of conflicting evidence about AI: anecdotes that college grads are finding the job market worse and worse, but no noticeable effect of AI on productivity, for example. “There’s a huge amount of uncertainty,” he says. And that’s the most telling thing about the AI economy right now: the certainty of the rhetoric alongside the uncertainty of everything else.
The tobacco industry chalked up another win on Friday with a new policy announced by the Food and Drug Administration that gives what one expert called a “get-out-of-jail-free card” to some manufacturers illegally selling e-cigarettes and nicotine pouches.
The FDA has a significant backlog of applications from the makers of vapes and nicotine pouches seeking authorization to sell their products. Some have gone ahead and put their products on sale anyway while awaiting word from the agency. In the new guidance, first reported by the New York Times, the agency said it will not prioritize cracking down on illegal sales under two conditions.
Scientists at Columbia University have developed a brain-controlled hearing technology that allows users to amplify the conversation they are focusing on while reducing other voices. Published today in Nature Neuroscience, this study marks the first time this kind of technology has been tested in humans.
“We have developed a system that acts as a neural extension of the user, leveraging the brain’s natural ability to filter through all the sounds in a complex environment to dynamically isolate the specific conversation they wish to hear,” said Nima Mesgarani, PhD, principal investigator at Columbia’s Zuckerman Institute and associate professor of electrical engineering at Columbia’s Fu Foundation School of Engineering and Applied Science. “This science empowers us to think beyond traditional hearing aids, which simply amplify sound, toward a future where technology can restore the sophisticated, selective hearing of the human brain.”
While modern hearing aids can amplify human speech while suppressing background noise, they cannot separate and enhance specific voices when multiple people are speaking. This can make it difficult for users to concentrate on a specific conversation in everyday scenarios such as restaurants, classrooms, busy workplaces, and family gatherings.
The hearing device developed by Mesgarani’s team mimics the way the human brain can naturally identify and focus on a single speaker out of many within a crowd. Previously, the researchers had found a way of identifying which brain signals are linked to a specific conversation, by matching the timing of peaks and valleys of the brain waves to the sounds and silences of that conversation. They also identified distinct patterns of brain activity that indicate which conversation a person is focusing on and which one they are filtering out.
In the current study, the scientists developed a machine learning algorithm that could examine the user’s brainwaves and identify which conversation they are paying attention to in real time, making that voice louder and others quieter to make it easier to listen to. This system was tested on epilepsy patients who already had electrodes implanted in their brains. The electrodes were used to measure the user’s brain activity as they focused on two overlapping conversations played simultaneously, and the algorithm automatically detected which conversation they were trying to focus on.
“The results mark an important step toward a new generation of brain-controlled hearing technologies that align with the listener’s intent, potentially transforming how people navigate noisy, multi-talker environments,” said Vishal Choudhari, PhD, who led the development and evaluation of the system.
More research will be needed before minimally invasive wearable systems can integrate this kind of brain sensing technology with advanced audio processing capabilities, especially to ensure they can accurately decode conversations in real time and in real-world scenarios where multiple voices can be heard.
“The central unanswered question was whether brain-controlled hearing technology could move beyond incremental advances, towards a prototype that could help someone hear better in real time,” said Choudhari. “For the first time, we have shown that such a system that reads brain signals to selectively enhance conversations can provide a clear real-time benefit. This moves brain-controlled hearing from theory toward practical application.”
<![CDATA[Anosognosia drives untreated schizophrenia into homelessness and jail. Here’s why civil care fails and how structured treatment can decrease the number of arrests.]]>
In a new study published in Cell titled, “RegVelo: gene-regulatory-informed dynamics of single cells,” researchers from Stowers Institute of Medical Research have developed a new AI model that connects two areas of single-cell biology that have often remained separate: estimating how cells change over time and inferring the gene regulatory networks controlling those changes.
“You can imagine if you had a very early set of cells, having a particular set of instructions could allow you to reproduce, in vitro, some of these cell types in a very natural way. These cells could then be used in cell therapies in regenerative medicine,” said Tatjana Sauka-Spengler, PhD, Stowers Institute Investigator and co-senior author of the study.
While development is often described as a series of static snapshots of cell states, RegVelo models how these fate decisions are encoded in gene regulatory networks over time and space, and what drives cell state transitions. In zebrafish neural crest development, RegVelo identified an early driver of pigment cell formation (tfec) and revealed a previously unknown regulator of pigment cell fate (elf1). The neural crest is a developmental system that gives rise to many different cell types, including pigment cells, craniofacial tissues, and parts of the peripheral nervous system.
CRISPR/Cas9-mediated knockout and single-cell Perturb-seq supported predictions, showing that the model could do more than describe developmental changes and generate biologically meaningful hypotheses that held up in living systems.
Alejandro Sánchez Alvarado, PhD, Stowers President and chief scientific officer says RegVelo’s value “extends well beyond” neural crest cells and is applicable to any system in which cells change over time, from basic developmental biology to modeling tumor trajectories and the cellular outcomes that may inform treatment.
“Sauka-Spengler and her collaborators have developed a meaningfully different way to process this kind of data,” said Sánchez Alvarado. “It allows us to infer the most likely path of each component through space and time, and to use deep learning to predict those dynamics and test them experimentally.”
Single-cell biology research has made it possible to build increasingly detailed maps of development. RNA velocity methods can help researchers estimate how cells move through developmental landscapes, while gene regulatory network approaches can identify relationships among genes. However, these methods have typically been used in parallel rather than together.
“For a long time, cellular dynamics and gene regulation have largely been modeled separately,” said Fabian Theis, PhD, the study’s co-senior author and director of the institute of computational biology at Helmholtz Munich. “RegVelo brings those pieces together, allowing us to ask not only how cells are changing, but which regulatory interactions are helping drive those changes.”
The framework jointly models splicing kinetics and gene regulatory relationships, allowing researchers to map the hidden timeline of cell development, predict how cells shift from one state to another, and test what might happen when specific regulators are perturbed.
The framework can incorporate additional regulatory layers, including chromatin, protein activity, and other multimodal measurements. While the study’s limitations include simplifying assumptions around latent time, regulatory interactions, and computational cost, the results demonstrate a compelling proof of principle.
“When dynamic cell-state modeling is linked directly to gene regulation, it becomes possible to move closer to mechanism and then discovery,” Sauka-Spengler said.
Influencer Anna Sitar reflects on the importance of realness
In recognition of Mental Health Awareness Month, the Child Mind Institute has launched the Mental Health Fitness campaign — a national call to action highlighting the importance that caring for one’s mental health is just as important as physical health.
Known for embodying the color yellow and sharing sunshine, influencer Anna Sitar always keeps it honest when it comes to her mental health. Rather than curating only the good moments, Anna shares how she keeps her mental well-being in check through small, consistent habits like journaling, therapy, and being vulnerable with her followers. Her message is simple — actively look for the good, even on harder days.
“Being able to share the way that I’m feeling, whether it’s my highest highs or my lowest lows, has shown me that there’s other people out there who feel the same way I do. It’s allowed me to inspire them to look for the good in their every day and hopefully improve their lives.”
About Anna Sitar
Anna Sitar is a content creator and influencer who’s amassed over 1.6 million followers on Instagram. She’s known for her refreshing honesty in conversations around mental health and normalizing vulnerability in digital spaces. Through her content, Anna encourages others to embrace authenticity and prioritize self-reflection.
About Mental Health Fitness
For decades, we’ve understood that physical fitness doesn’t just happen — it takes skills, regular practice, and a supportive environment. The same is true for mental health. Developed by experts at the Child Mind Institute for three different age groups, our Mental Health Fitness guides have been used by more than 1.8 million students, caregivers, and educators to build emotion regulation skills and resilience. Whether your child is 5 or 15, struggling or thriving, they can learn these skills. And you can practice alongside them. Learn more at Mental Health Fitness.
<![CDATA[Large meta-analysis finds GLP-1 drugs don’t raise depression or suicidality and modestly boost quality of life and eating control in diabetes/obesity.]]>
Despite years of digitization, organizations capture less than one-third of the value expected from digital investments, according to McKinsey research. That’s because most big companies begin with technological capabilities and bolt applications onto them, rather than starting with customer needs and working backward to technology solutions. Not prioritizing the customer can create fragmented solutions; disjointed customer experiences; and ultimately, failed transformations.
Organizations that achieve outsized results from AI flip the script. They adopt a “customer-back engineering” mindset, putting customers at the heart of technology transformation.
It’s a strategy in which products and services are developed with the customer experience first in mind, including the customers’ challenges, needs, and expectations. Product development teams then work backward in a nimble and agile way to find the steps necessary to design and build solutions that achieve the desired experience.
“When you get your engineers closer to customers, you get a lot more sideways innovation,” says Ashish Agrawal, managing vice president of business cards and payments tech at Capital One. “That leads to a multiplier effect, because engineers can approach a problem from a different dimension that can be unique to the sales or product perspective.”
The case for customer-centricity in engineering
Engineers are problem-solvers by nature, says Agrawal. When they hear about challenges customers are experiencing, or how they are using products and services in the real world, they can devise ways to efficiently address customer needs, since they are naturally closer to systems and data than many other teams across the company.
“Fostering a customer-centric culture has a motivational effect on engineers when they actually start seeing how the core changes they’re making, or the features they’re adding, are having a direct impact on the lives of customers,” says Agrawal.
It also takes discipline. Agrawal explains that Capital One has set a goal for every engineer in his organization to establish several touchpoints with customers throughout the year in different forms, including:
Digital empathy sessions to observe user journeys and identify where users hit friction
Embedded customer support for periods of time to deepen understanding of servicing needs
Engineering ride-alongs, in which engineers join customer success, sales, and support staff on calls or on-site visits
Hackathon competitions to build solutions around real customer problems
The AI opportunities with customer-centricity
“The biggest challenge engineers within large companies face is a lack of direct access to customers,” says Agrawal. “This can make it harder for technologists to work with customers to identify problems and innovate solutions.”
AI has accelerated the challenges as well as the opportunities. The lifecycle of launching products has become significantly faster. But the good news is that engineers are closer to the data that feeds into AI, so they can more rapidly apply AI-informed data techniques to solve customer problems.
Agrawal outlines a recent scenario: In the customer servicing space, conversations can instantly be summarized and give a customer agent context on the member’s original request and remaining action points. Agentic AI can also be enabled to ask pointed follow-up questions about the interaction that would otherwise take human agents time to read through the entire thread.
“A solution would have been a lot harder in an ecosystem without a lot of high-quality data,” says Agrawal. “But when you combine a rich data ecosystem with agentic tools, you move from incremental fixes to high-velocity transformation.”
By investing in AI data and tools and focusing on rapid experimentation, Agrawal says the cycle of deploying solutions can be accelerated. Teams learn that if they meet customer needs and iterate on a wider range of solutions much faster, then the entire innovation cycle speeds up.
For example, Capital One used customer insights to build a state-of-the-art, multi-agent AI framework called Chat Concierge to enhance the customer experience for car buyers and dealers. In a single conversation, Chat Concierge can perform tasks like comparing vehicles to help car buyers decide on the best choice and scheduling test drives or appointments with salespeople.
Agrawal explains that car buyers can engage with Chat Concierge directly through participating dealer websites. Dealers can access and can take over the chat through Navigator Platform. The AI assistant consists of multiple logical agents that work together to mimic human reasoning, allowing it to provide information and take action based on the customer’s requests.
The elements of an AI-first mindset
According to a recent MIT Technology Review Insights survey, 70% of leaders say their firm uses agentic AI to some degree. Roughly half of executives say agentic AI systems are highly capable of improving fraud detection (56%) and security (51%), reducing cost and increasing efficiency (41%), and improving the customer experience (41%).
Looking into the future, achieving these outcomes looks even more likely. More than half of the banking executives surveyed say they expect to continue to improve fraud detection (75%), security (64%), and the customer experience (51%). Agentic AI use cases that show strong potential to transform the customer experience in financial services include responding to customer services requests, adjusting bill payments to align with regular paychecks, or extracting key terms and conditions from financial agreements.
Placing the customer at the center of a transformation requires an AI-first mindset. Companies must shift from simply augmenting an existing product to fundamentally reimagining the problem and the user’s needs through the lens of AI’s capabilities.
A few best practices that Agrawal recommends include:
Reimagine the core function of AI to solve a user’s problem: “The true value isn’t in chasing the AI hype; it’s in solving meaningful customer problems. By focusing on impact, we ensure that our innovation isn’t just fast; it’s transformative,” says Agrawal.
Start with high-quality, well-governed data as the foundation: “Data readiness and unified information across systems are the non-negotiable foundations of AI. A clean data layer is what orchestrates the agentic loop— enabling the perception, reasoning, and execution required to solve a customer’s problem before they even have to ask,” explains Agrawal.
Rebuild workflows with AI embedded from the start: “People treat models as black boxes, but agentic systems require tremendous rigor and oversight. Having a data ecosystem that is well-governed and responsible AI standards are essential pillars for building trust in these systems,” says Agrawal.
Build a cross-functional team involving data science, engineering, product, design, and other partners: Agrawal advises, “It’s important to be open and nimble to transforming how we work and create impact as AI becomes more integrated into workflows. It’s also important to take a ‘crawl, walk, run approach’ if you are new to AI, as opposed to simply jumping into it.”
In the end, achieving end-to-end transformation depends on empowering engineers and partner teams to start with customer needs and work backward to technology solutions, rather than starting with technological capabilities first and finding applications for them. When organizations make a customer-back approach second nature, they are able to not only reimagine the customer experience from the inside out, but to also place the customer front and center from the very start.
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.
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<![CDATA[Review news from the last week in psychiatry. ]]>
