Why opinion on AI is so divided

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In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. (It’s a marathon, not a sprint, after all.)

This year’s report, which dropped today, is full of striking stats. A lot of the value comes from having numbers to back up gut feelings you might already have, such as the sense that the US is gunning harder for AI than everyone else: It hosts 5,427 data centers (and counting). That’s more than 10 times as many as any other country.  

There’s also a reminder that the hardware supply chain the AI industry relies on has some major choke points. Here’s perhaps the most remarkable fact: “A single company, TSMC, fabricates almost every leading AI chip, making the global AI hardware supply chain dependent on one foundry in Taiwan.” One foundry! That’s just wild.

But the main takeaway I have from the 2026 AI Index is that the state of AI right now is shot through with inconsistencies. As my colleague Michelle Kim put it today in her piece about the report: “If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock.” (The Stanford report notes that Google DeepMind’s top reasoning model, Gemini Deep Think, scored a gold medal in the International Math Olympiad but is unable to read analog clocks half the time.)

Michelle does a great job covering the report’s highlights. But I wanted to dwell on a question that I can’t shake. Why is it so hard to know exactly what’s going on in AI right now?  

The widest gap seems to be between experts and non-experts. “AI experts and the general public view the technology’s trajectory very differently,” the authors of the AI Index write. “Assessing AI’s impact on jobs, 73% of U.S. experts are positive, compared with only 23% of the public, a 50 percentage point gap. Similar divides emerge with respect to the economy and medical care.”

That’s a huge gap. What’s going on? What do experts know that the public doesn’t? (“Experts” here means US-based researchers who took part in AI conferences in 2023 and 2024.)

I suspect part of what’s going on is that experts and non-experts base their views on very different experiences. “The degree to which you are awed by AI is perfectly correlated with how much you use AI to code,” a software developer posted on X the other day. Maybe that’s tongue-in-cheek, but there’s definitely something to it.

The latest models from the top labs are now better than ever at producing code. Because technical tasks like coding have right or wrong results, it is easier to train models to do them, compared with tasks that are more open-ended. What’s more, models that can code are proving to be profitable, so model makers are throwing resources at improving them.

This means that people who use those tools for coding or other technical work are experiencing this technology at its best. Outside of those use cases, you get more of a mixed bag. LLMs still make dumb mistakes. This phenomenon has become known as the “jagged frontier”: Models are very good at doing some things and less good at others.

The influential AI researcher Andrej Karpathy also had some thoughts. “Judging by my [timeline] there is a growing gap in understanding of AI capability,” he wrote in reply to that X post. He noted that power users (read: people who use LLMs for coding, math, or research) not only keep up to date with the latest models but will often pay $200 a month for the best versions. “The recent improvements in these domains as of this year have been nothing short of staggering,” he continued.

Because LLMs are still improving fast, someone who pays to use Claude Code will in effect be using a different technology from someone who tried using the free version of Claude to plan a wedding six months ago. Those two groups are speaking past each other.

Where does that leave us? I think there are two realities. Yes, AI is far better than a lot of people realize. And yes, it is still pretty bad at a lot of stuff that a lot of people care about (and it may stay that way). Anyone making bets about the future on either side should bear that in mind.

Neural Mechanism Underlying Sensory Behavior Revealed in C. elegans

Animal behavior reflects a complex interplay between an animal’s brain and its sensory surroundings. In a new study published in Nature Neuroscience titled, “Neural sequences underlying directed turning in Caenorhabditis elegans,” researchers from Massachusetts Institute of Technology (MIT) have shown how neuron circuits within C. elegans nematode worms respond to odors and generate movement as they pursue favorable versus unfavorable smells. The results inform understanding of the basic principles of the sensory nervous system for therapeutic applications. 

“Across the animal kingdom, there are just so many remarkable behaviors,” said Steven Flavell, PhD, associate professor at the Picower Institute at MIT, Howard Hughes Medical Institute (HHMI) investigator, and corresponding author of the study. “With modern neuroscience tools, we are finally gaining the ability to map their mechanistic underpinnings.” 

Whether moving toward a food source or away from a predator, animals must integrate sensory stimuli to navigate to favorable locations. The neural circuits for navigation are tasked with generating directed movement while simultaneously integrating sensory input to update behavior. Understanding how neural circuits select, execute and adapt sensory-guided navigation behaviors uncovers basic principles of how nervous systems are organized to integrate sensory information and control behavior. 

In C. elegans, the authors identified error-correcting turns during navigation and used whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. Defined neurons activated in a stereotyped order during each turn. Distinct neurons in this sequence respond to the spatial distribution of attractive and aversive olfactory cues, anticipate upcoming turn directions and drive movement, linking key features of this sensorimotor behavior across time. 

“One thing that really excited us about this study is that we were able to see what a sensorimotor arc looks like at the scale of a whole nervous system: all the bits and pieces, from responses to the sensory cue until the behavioral response is implemented,” Flavell said.  

The electrical activity of more than 100 neurons was tracked during sensory movement. Notably, C. elegans only have 302 neurons total. Instead of random movements, the worms executed turns with advantageous timing and at well-chosen angles.  

The activity of SAA neurons was crucial for integrating odor detection with planned movement and predicted the direction of upcoming turns. Several neurons showed different activity patterns depending on the location of odors were and whether the worm was moving forward or in reverse. 

Additionally, the neuromodulator, tyramine, was essential for turning and shifting gears. When the worms moved in reverse, tyramine from the neuron RIM enabled other neurons in the sequence to change their activity appropriately to execute the turns. In several experiments, the scientists knocked out RIM tyramine, which disrupted the navigation behaviors and the sequence of neural activity. 

The post Neural Mechanism Underlying Sensory Behavior Revealed in <i>C. elegans</i> appeared first on GEN – Genetic Engineering and Biotechnology News.

Co-Design of a Depression Self-Management Tool for Adolescent and Young Adult Cancer Survivors: Rapid Qualitative Analysis of Interview Feedback on a Prototype

<strong>Background:</strong> Over 2.1 million adolescent and young adult cancer survivors (AYACS) live in the United States. Recent estimates suggest that up to one-third of AYACS experience major depressive disorder. Although several efficacious evidence-based interventions are available to manage symptoms of depression, these interventions are often inaccessible to AYACS who have many competing commitments. Digital mental health tools hold promise for this population; however, only a few have been tailored to meet the unique needs of AYACS, and findings to date have yielded mixed results. <strong>Objective:</strong> This study aims to obtain feedback from AYACS on a mid-fidelity prototype of a depression self-management tool being tailored for AYACS. <strong>Methods:</strong> Individuals with a history of cancer diagnosed at age 12 or older who were between the ages of 15 and 39 and had completed primary treatment were identified through a review of medical records from a comprehensive cancer center in the Southeastern United States. Potentially eligible participants were contacted by study staff to conduct additional screening and obtain informed consent via REDCap (Research Electronic Data Capture; Vanderbilt University). Upon enrollment, participants provided demographic and clinical information, as well as their availability for an interview. The principal investigator (KMI) conducted semistructured individual interviews with consented AYACS. Most of the interview was dedicated to showing participants the mid-fidelity prototype of the tool, explaining how the prototype might work, and requesting targeted feedback. Demographic and clinical characteristics, as well as some aspects of feedback on the prototype, were summarized using descriptive statistics. Interviews were audio- and video-recorded and transcribed. The transcriptions underwent rapid qualitative analysis guided by the Rigorous and Accelerated Data Reduction technique. <strong>Results:</strong> A total of 14 AYACS (n=9, 64%, female; n=9, 64%, white; ages 15-38) completed an individual interview. Participant preferences for mood tracking, content presentation, user input, and duration of use were captured qualitatively but analyzed quantitatively. For example, most participants (n=10, 71%) indicated that they preferred a mood-tracking option that included emojis and would be willing to track their mood at least once per day (n=11, 79%). Participant preferences captured qualitatively fell into 4 themes: (1) features to promote user engagement (eg, the use of gamification); (2) tailored content presentation (eg, authenticity in the portrayal of the cancer experience); (3) perceived usability (eg, simplifying user input); and (4) interface design (eg, implementing a coherent design theme and color scheme). <strong>Conclusions:</strong> Findings indicated that AYACS highly value personalization, flexibility, and peer support in digital interventions. Based on insights obtained during individual interviews, a working prototype was developed by reprogramming an existing digital tool. Qualitative and quantitative findings informed modifications to the existing digital tool. The working prototype will next undergo evaluation as part of a pilot full-factorial trial.

Want to understand the current state of AI? Check out these charts.

If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts through some of that noise. 

Despite predictions that AI development may hit a wall, the report says that the top models just keep getting better. People are adopting AI faster than they picked up the personal computer or the internet. AI companies are generating revenue faster than companies in any previous technology boom, but they’re also spending hundreds of billions of dollars on data centers and chips. The benchmarks designed to measure AI, the policies meant to govern it, and the job market are struggling to keep up. AI is sprinting, and the rest of us are trying to find our shoes.

All that speed comes at a cost. AI data centers around the world can now draw 29.6 gigawatts of power, enough to run the entire state of New York at peak demand. Annual water use from running OpenAI’s GPT-4o alone may exceed the drinking water needs of 12 million people. At the same time, the supply chain for chips is alarmingly fragile. The US hosts most of the world’s AI data centers, and one company in Taiwan, TSMC, fabricates almost every leading AI chip. 

The data reveals a technology evolving faster than we can manage. Here’s a look at some of the key points from this year’s report. 

The US and China are nearly tied

In a long, heated race with immense geopolitical stakes, the US and China are almost neck and neck on AI model performance, according to Arena, a community-driven ranking platform that allows users to compare the outputs of large language models on identical prompts. In early 2023, OpenAI had a lead with ChatGPT, but this gap narrowed in 2024 as Google and Anthropic released their own models. In February 2025, R1, an AI model built by the Chinese lab DeepSeek, briefly matched the top US model, ChatGPT. As of March 2026, Anthropic leads, trailed closely by xAI, Google, and OpenAI. Chinese models like DeepSeek and Alibaba lag only modestly. With the best AI models separated in the rankings by razor-thin margins, they’re now competing on cost, reliability, and real-world usefulness. 

Chart of the performance of top models on the Arena by select providers, showing the Arena score from May 2023 to Jan 2026 with the models all trending upward.  The scores are tightly packed by US based Anthropic, xAI, Google and OpenAI lead Alibaba, DeepSeek and Mistral (in that order.) Meta trails the pack.

The index notes that the US and China have different AI advantages. While the US has more powerful AI models, more capital, and an estimated 5,427 data centers (more than 10 times as many as any other country), China leads in AI research publications, patents, and robotics. 

As competition intensifies, companies like OpenAI, Anthropic, and Google no longer disclose their training code, parameter counts, or data-set sizes. “We don’t know a lot of things about predicting model behaviors,” says Yolanda Gil, a computer scientist at the University of Southern California who coauthored the report. This lack of transparency makes it difficult for independent researchers to study how to make AI models safer, she says.

AI models are advancing super fast

Despite predictions that development will plateau, AI models keep getting better and better. By some measures, they now meet or exceed the performance of human experts on tests that aim to measure PhD-level science, math, and language understanding. SWE-bench Verified, a software engineering benchmark for AI models, saw top scores jump from around 60% in 2024 to almost 100% in 2025. In 2025, an AI system produced a weather forecast on its own.  

“I am stunned that this technology continues to improve, and it’s just not plateauing in any way,” says Gil.

line chart of Select AI Index technical performance benchmarks vs human performance, showing that skills such as image classification, English language understanding, multitask language understanding, visual reasoning, medium level reading comprehension, multimodal understanding and reasoning have surpassed the human baseline at or before 2025, with autonomous software engineering, mathmatical reasoning and agent multimodal computer use trending towards meeting the human baseline by 2026.

However, AI still struggles in plenty of other areas. Because the models learn by processing enormous amounts of text and images rather than by experiencing the physical world, AI exhibits “jagged intelligence.” Robots are still in their early days and succeed in only 12% of household tasks. Self-driving cars are farther along: Waymos are now roaming across five US cities, and Baidu’s Apollo Go vehicles are shuttling riders around in China. AI is also expanding into professional domains like law and finance, but no model dominates the field yet. 

But the way we test AI is broken

These reports of progress should be taken with a grain of salt. The benchmarks designed to track AI progress are struggling to keep up as models quickly blow past their ceilings, the Stanford report says. Some are poorly constructed—a popular benchmark that tests a model’s math abilities has a 42% error rate. Others can be gamed: when models are trained on benchmark test data, for example, they can learn to score well without getting smarter. 

Because AI is rarely used the same way it’s tested, strong benchmark performance doesn’t always translate to real-world performance. And for complex, interactive technologies such as AI agents and robots, benchmarks barely exist yet. 

AI companies are also sharing less about how their models are trained, and independent testing sometimes tells a different story from what they report. “A lot of companies are not releasing how their models do in certain benchmarks, particularly the responsible-AI benchmarks,” says Gil. “The absence of how your model is doing on a benchmark maybe says something.” 

AI is starting to affect jobs

Within three years of going mainstream, AI is now used by more than half of people around the world, a rate of adoption faster than the personal computer or the internet. An estimated 88% of organizations now use AI, and four in five university students use it. 

It’s early days for deployment, and AI’s impact on jobs is hard to measure. Still, some studies suggest AI is beginning to affect young workers in certain professions. According to a 2025 study by economists at Stanford, employment for software developers aged 22 to 25 has fallen nearly 20% since 2022. The decline might not be pinned on AI alone, as broader macroeconomic conditions could be to blame, but AI appears to be playing a part.

two line charts showing the normalized headcount trends by age group from 2021 through 2025. On the left for software developers the early career (age 22-25) cohort drops rapidly after a peak in September 2022, with other ages still rising albeit less steeply.  On the right, customer support agents see a similar trend, although the decline for the early career group is less steep than for software developers.

Employers say that hiring may continue to tighten. According to a 2025 survey conducted by McKinsey & Company, a third of organizations expect AI to shrink their workforce in the coming year, particularly in service and supply chain operations and software engineering. AI is boosting productivity by 14% in customer service and 26% in software development, according to research cited by the index, but such gains are not seen in tasks requiring more judgment. Overall, it’s still too early to understand the bigger economic impact of AI. 

People have complicated feelings about AI 

Around the world, people feel both optimistic and anxious about AI: 59% of people think that it will provide more benefits than drawbacks, while 52% say that it makes them nervous, according to an Ipsos survey cited in the index. 

Notably, experts and the public see the future of AI very differently, according to a Pew survey. The biggest gap is around the future of work: While 73% of experts think that AI will have a positive impact on how people do their jobs, only 23% of the American public thinks so. Experts are also more optimistic than the public about AI’s impact on education and medical care, but they agree that AI will hurt elections and personal relationships.

Bar chart of US perceptions of AI's societal impact contrasting US adults with AI experts, with the percentage of AI experts saying that AI will have a positive impact in the next 20 years is 2-3 times higher than the US adults.  The most optimistic AI experts are in the field of medical care with 84% predicting a positive outcome (versus 44% of US adults.) The greatest difference is for jobs with experts polling at 73% and US adults  polling at 23%.  Both groups have a similar (11% for experts and 9% of adults.) expectation for a positive outcome for AI in elections.

Among all countries surveyed, Americans trust their government least to regulate AI appropriately, according to another Ipsos survey. More Americans worry federal AI regulation won’t go far enough than worry it will go too far. 

Governments are struggling to regulate AI

Governments around the world are struggling to regulate AI, but there were some minor successes last year. The EU AI Act’s first prohibitions, which ban the use of AI in predictive policing and emotion recognition, took effect. Japan, South Korea, and Italy also passed national AI laws. Meanwhile, the US federal government moved toward deregulation, with President Trump issuing an executive order seeking to handcuff states from regulating AI. 

Despite this federal action, state legislatures in the US passed a record 150 AI-related bills. California enacted landmark legislation, including SB 53, which mandates safety disclosures and whistleblower protections for developers of AI models. New York passed the RAISE Act, requiring AI companies to publish safety protocols and report critical safety incidents.

line chart showing the number of AI-related bills passed into law by all US states from 2016-2025, which increases sharply in 2023 and peaks with 150 bills in 2025.

But for all the legislative activity, Gil says, regulation is running behind the technology because we don’t really understand how it works. “Governments are cautious to regulate AI because … we don’t understand many things very well,” she says. “We don’t have a good handle on those systems.”

Unequal voices: examining autism identification and diagnosis disparities for indigenous Mixtec families

Autism racial/ethnic disproportionality in special education is a significant concern in California and beyond, with White students often overidentified and Latinx and Indigenous (Zapotec/Mixtec) students under-identified. This mixed-methods study investigates the root causes of autism racial/ethnic disproportionality in a California high school district identified as significantly disproportionate for the overidentification of White students with autism. The study was conducted in two stages. First, a Likert-type scale survey (N = 147) was administered to caregivers to examine autism identification and service barriers. In the second stage, three open-response questions within the survey were used to gather qualitative insights from Latinx and Indigenous caregivers. Findings reveal systemic cultural and linguistic barriers contributing to the delayed diagnosis of autism in Latinx and Indigenous students. The qualitative responses further underscore the need for early screening, translation services, and culturally sensitive caregiver support particularly for Indigenous, Mixtec families.

You have no choice in reading this article—maybe

Uri Maoz loved doing his human research, back when he was getting his PhD. He was studying a very specific topic in computational neuroscience: how the brain instructs our arms to move and how our gray matter in turn perceives that motion. 

Then his professor asked him to deliver an undergrad lecture. Maoz assumed his boss was going to tell him exactly what to do, or at least throw some PowerPoint slides his way. But no. Maoz had free rein to teach anything, as long as it was relevant to the students. “I could have gone to human brain augmentation,” he says. “Cyborgs or whatever.”

Yet that admittedly fun and borderline sci-fi topic wasn’t what popped, unbidden, into his mind. His idea, he recalls with excitement: “What neuroscience has to say about the question of free will!” 

How—or whether—humans make decisions (like, say, about what to discuss in an undergrad lecture) had been on his mind since he’d read an article in his early twenties suggesting that … maybe they didn’t. This question might naturally beget others: Had he even had a choice about whether to read that article in the first place? How would he ever know if he was responsible for making decisions in his life or if he just had the illusion of control?

“After that, there was no turning back,” says Maoz, now a professor at Chapman University, in California. He finished his PhD work in human movement, but afterward he scooted further up the neural chain to find out how desires and beliefs turn into actions—from raising an arm to choosing someone to ask out to dinner on a Friday night.

Today, Maoz is a central figure in the attempt to (sort of, maybe) answer how that neural chain functions. His research has since overturned and reinter­preted canonical neuroscience studies and united the straight-scientific and philosophical sides of the free-will question. More than anything, though, he’s succeeded in uncovering new wrinkles in the debate.

Machines and magic tricks

The concept of free will seems straightforward, but it doesn’t have a universally accepted definition. One intuitive notion is that it’s the ability to make our own decisions and take our own actions on purpose—that we control our lives. But physicists might ask if the universe is deterministic, following a preordained path, and if human choices can still happen in such a universe. 

That’s a question for them, Maoz says. What neuroscientists can do is figure out what’s going on in the brain when people make decisions. “And that’s what we’re trying to do: to understand how our wishes, desires, beliefs, turn into actions,” he says.

By the time Maoz had finished his PhD, in 2008, neuroscientific research into the question had been going on for decades. One foundational study from the 1960s showed that a hand movement—something a person seemingly decides to do—was preceded by the appearance in the brain of an electrical signal called the “readiness potential.” 

Building on that result, in the 1980s a neuroscientist named Benjamin Libet did the experiment that had first piqued Maoz’s interest in the topic—one that many, until recently, interpreted as a death knell for the concept of free will.

An electrical impulse in our brains can shed only so much light on whether we truly are the architects of our own fates.

“He just had people sit there, and whenever they feel like it, they would go like this,” says Maoz, wiggling his wrist. Libet would then ask where a rotating dot was on a screen when they first had the urge to flick. He found that the readiness potential appeared not only before they moved their hand but before they reported having the urge to move—or, in Libet’s interpretation, before they knew they were going to move. 

Studies since have confirmed the observation and shown that the readiness potential appears a second or two—and maybe, fMRI implies, up to 10 seconds—before participants report making a conscious decision. “It suggests we are essentially passengers in a self-driving car,” says Maoz. “The unconscious biological machine does all the steering, but our conscious mind sits in the driver’s seat and takes the credit.” 

Maoz initially approached his own research with variations on Libet’s experiments. He worked with epilepsy patients who already had electrodes in their brains, for clinical purposes, and was able to predict which hand they would raise before they raised it. 

Still, some of the Libet-inspired studies people were doing nagged at him. “All these results were about completely arbitrary decisions. Raise your hand whenever you feel like it,” he says. “Why? No reason.” A decision like that is quite different from, say, choosing to break up with your partner. Try telling someone they weren’t in the driver’s seat for that

The field wasn’t looking at meaningful decisions, he says—the ones that actually set the course of lives. 

Maoz began pulling in philosophers to help guide his approach. They would challenge him to confront the semantic differences between things like intention, desire, and urge. Neuroscientists have tended to lump those concepts together, but philosophers tease them apart: Desire is a want that doesn’t necessarily progress toward an action; urge carries implications of immediacy and compulsion; and intention involves committing to a plan. (Maoz has come to focus specifically on intention—including, recently, the potential intentions of AI.)

In 2017, he organized his first in a series of free-will conferences, drawing many autonomy-interested philosophers. “Thank you so much for coming,” he recalls saying at the opening of the meeting. “As if you had a choice.” One day, the crew took an excursion out on a lake. As the group munched on shrimp, someone joked that they hoped the boat didn’t sink, because everybody in the field would die. 

The comment didn’t make Maoz feel existential dread. Instead, he figured that if the whole field was already there, why not lasso them all into writing a research grant? “He just thinks what should be the next step and just has a very good ability to just make it happen,” says Liad Mudrik, a neuroscientist at Tel Aviv University and a frequent collaborator.

That ability is special among scientists, says Chapman colleague Aaron Schurger, with whom Maoz co-directs the Laboratory for Understanding Consciousness, Intentions, and Decision-Making (LUCID, appropriately). “I really think that Uri is kind of at the nexus of this field right now because he’s really, really good at bringing people together around these big ideas,” he says.

Donations and interruptions

Maoz has recently been making progress on one of the big ideas that have consistently occupied his working hours: how trivial and significant decisions play out differently in the brain. In collaborations with Mudrik, he’s parsed the neural difference between picking and choosing—their terms for arbitrary decisions and those that change your life and tug on your emotions. 

Readiness potential? Their measurements didn’t clock it ahead of choices. In 2019, Maoz and a crew published a paper measuring the electrical activity in people’s brains as they pressed a key to choose one of two nonprofits to donate $1,000 to—for real, with actual dollars. Then the researchers compared that activity with what they saw when the same group pressed a key at random to donate $500 each to two nonprofits. The team saw the readiness potential in the arbitrary decision, but not for the $1,000 question. 

Libet’s result, they concluded, doesn’t apply to the important stuff, which means readiness potential might not actually be a sign that your brain is making a choice before you’re aware of it. “If Libet would have chosen to focus on deliberate decisions, then maybe the entire debate about neuroscience proving free will to be an illusion would have been spared from us,” Mudrik says. 

Maoz’s research has spurred others to reinterpret Libet’s work. It’s “enriched my thought process a great deal,” says Bianca Ivanof, a psychologist whose dissertation scrutinized Libet’s methods. They turn out to identify readiness potential at different times depending on how the rotating-dot setup is designed, complicating the ability to compare and interpret results.

Maoz has also continued to gather data on the subject. Last year, for example, he used an EEG to measure electrical signals in people’s brains as they got ready to press a keyboard space bar. At random moments, he interrupted their preparations with an audible tone and asked them about their intentions. He saw no connection between the readiness potential and whether or not they were planning to tap the key—evidence that the potential doesn’t represent the buildup of either conscious or unconscious plans. The team did see a signal, though, in a different part of the brain when people said they were preparing to move.

So … that’s free will? Sadly, Maoz would be compelled to say Well, not exactly. An electrical impulse in our brains can shed only so much light on whether we truly are the architects of our own fates. And maybe the confusing data from neurons is actually the point. “I don’t think it is a yes-or-no question,” Maoz says. Maybe our less meaningful choices aren’t mindfully made but big ones are; maybe we have the conscious power to change an intended action, but only if our brains are in a particular state. 

Neuroscientists likely can’t figure out, on their own, if free will exists. But they can, Maoz says, parse how semantically distinct decision-making forces—desires, urges, intentions, wishes, beliefs—manifest in our brains and become actions. “That is something that we are making progress on,” he says, “and I think that that’s going to help us understand what we do control.” And perhaps also help us make peace with what we do not. 

Sarah Scoles is a freelance science journalist and author based in southern Colorado.

STAT+: Hospitals roll out chatbots, looking to reclaim their role in patients’ health conversations

Every day, more than 40 million people ask ChatGPT about health care, according to OpenAI. They’re asking questions about diet, exercise, insurance — and in some cases, serious symptoms that would typically get discussed on a 911 call or in a doctor’s office.

For some health systems, that’s creating an imperative. A small number of hospitals are trying to recapture some of those clinical conversations from commercial large language models like ChatGPT, Claude, and Gemini. They’re implementing their own patient-facing chatbots, ones that draw directly from their existing medical records and can funnel patients toward care in their own system. 

Hartford HealthCare this week will launch PatientGPT, a chatbot engineered by clinical AI company K Health, to its patients in Connecticut. Two health systems — California-based Sutter Health and Reid Health, serving Indiana and Ohio — have announced pilot versions of Emmie, the chatbot built by medical record mammoth Epic. The list is likely to grow rapidly.

Continue to STAT+ to read the full story…

StockWatch: IPO Market Shows Sign of Life with Avalyn Filing

The initial public offering (IPO) market showed signs of life for the first time in more than a month as Boston-based Avalyn Pharma filed a registration statement on Wednesday seeking to raise capital to develop its pipeline of respiratory disease treatments.

It’s too early to know how much money Avalyn plans to raise—the registration statement includes a placeholder “$100 million” figure that will inevitably be revised, and doesn’t say how many shares will be sold. It’s also too soon to know how much of the proceeds will go toward each of the three pipeline candidates cited in the filing to the U.S. Securities and Exchange Commission:

  • AP01—An inhaled version of pirfenidone, a small molecule modulator of cytokines and growth factors whose development the IPO would advance through Phase IIb topline data and into Phase III. AP01 is under study in the Phase IIb MIST trial (NCT06329401) as a potential treatment for progressive pulmonary fibrosis.
  • AP02—An inhaled version of nintedanib, a small molecule inhibitor of multiple tyrosine kinases, being developed to treat idiopathic pulmonary fibrosis (IPF). Avalyn plans to advance AP02 into the Phase II AURA-IPF trial (NCT07194382) after completing single-ascending dose (SAD) and multiple-ascending dose (MAD) Phase I trials in healthy adult volunteers and IPF patients.
  • AP03—A preclinical inhaled fixed-dose combination of AP01 and AP02 designed to combine what Avalyn says is their ability to substantially reduce or eliminate the adverse effects of oral pirfenidone and oral nintedanib.

Pirfenidone is an IPF drug marketed as Esbriet® by Genentech, a member of the Roche Group, with several other companies selling generic versions. Nintedanib is a kinase inhibitor with indications in treating IPF and chronic fibrosing interstitial lung diseases (ILDs) and slowing the rate of decline in pulmonary function in adults, marketed as Ofev® by Boehringer Ingelheim, with generic versions approved this month.

“The change we aim to make in the treatment paradigm of pulmonary fibrosis and other ILDs parallels the decades-long evolution seen in the treatment of asthma and COPD,” Avalyn stated in its S-1 statement.

In those diseases, the company explained, treatments advanced from broad, systemic oral therapies to targeted inhaled treatments, and ultimately to combination inhalers.

Pulmonary fibrosis “opportunity”

“We see a similar opportunity in pulmonary fibrosis, where the field still relies on oral antifibrotics today. Our programs are designed to drive a similar evolution, first by shifting treatment toward inhaled, lung-targeted formulations of existing antifibrotics that aim to improve safety and efficacy,” Avalyn explained. “We aspire to deliver inhaled therapies that combine complementary mechanisms into a single device for even greater therapeutic impact.”

In discussing the use of its proceeds, Avalyn said it envisioned advancing AP01 and AP02 through Phase IIb and Phase II topline data, respectively, into Phase III trials. AP03 would be advanced into the clinic and Phase I topline data using capital from the IPO.

Whatever isn’t spent on the pipeline candidates will be set aside for R&D activities for additional programs, working capital, and general corporate purposes, Avalyn added.

Avalyn is the first biotech IPO filing since Generate: Biomedicines completed the year’s largest to date, raising $400 million in gross proceeds toward clinical trials, as well as platform and pipeline R&D efforts. To date, seven companies have completed biotech IPOs, raising just over $1.7 billion in combined proceeds, Jefferies analyst Andrew Tsai wrote in a research note.

“The IPO market has been more of a laggard but showed signs of strength this quarter, with Q1 offerings the largest in the past four years,” Tsai wrote. As a result, he added, the IPO market is on pace to exceed historical levels except for the 2020–2021 IPO boom due to the COVID-19 pandemic.

Mixed on IPO improvement

Heading into 2026, analysts were mixed on whether this year would see improvement in the IPO market compared to 2025, when 11 U.S. companies raised a total of $3 billion on Wall Street. “We think it will be slightly better, but we have not seen enough to suggest that it’s truly rebounding,” Subin Baral, EY global life sciences deals leader, told GEN.

However, Michael Allwin, head of biopharma investment banking, Truist Securities, told GEN that IPOs are typically “the last shoe to drop” after other non-IPO financings show signs of recovery, giving him hope and optimism that 2026 would see a much more active IPO market than 2025: “While we’re not anticipating a resurgence in activity to the tune of what we saw at all-time highs in 2020 and 2021, we are anticipating a more normalized level of activity, maybe on parity with 2019.”

As for Avalyn, should its planned IPO raise the placeholder $100 million amount, it would nearly double the $138.359 million in cash, cash equivalents, and marketable securities with which Avalyn finished 2025.

Avalyn ended last year with no revenue and a net loss of $85.204 million, a 71% increase over the $49.744 million net loss the company reported for 2024. As a result, Avalyn’s accumulated deficit rose from $180.2 million at the end of 2024 to $265.4 million on December 31, 2025.

The IPO comes nine months after Avalyn completed its last financing, an oversubscribed $100 million Series D round completed in July and led by investment firms Suvretta Capital Management and SR One.

Survetta and SR One are two of 18 firms that have invested in Avalyn. The 18 include Novo Holdings, the asset manager of the foundation that controls Novo Nordisk.

Novavax rises on shareholders’ opposition

Novavax (NASDAQ: NVAX) enjoyed a small but noticeable surge in its stock price this past week after its second-largest shareholder ramped up its opposition to the vaccine developer’s leadership on several fronts.

Shah Capital Opportunity Fund, which holds an approximately 9% stake in Novavax, said it will oppose the company’s nominees for re-election to the board of directors when Novavax holds its annual shareholder meeting, scheduled for June (no date had been announced at deadline).

In an open letter to Novavax’s board, Raleigh, NC-based Shah Capital also requested that Novavax:

  • Shrink the board from eight to five members and elect new members “with emphasis on pragmatic entrepreneurial experience to turn Novavax into an equity success story.”
  • Buy back 10 to 20 million shares.
  • Retire its outstanding $225 million convertible bond with cash on hand “at the earliest.” Novavax reported $244.213 million in convertible notes payable and $240.634 million in cash and cash equivalents as of December 31, 2025.
  • Persuade a strategic long-term investor to take a 10–20% ownership stake “to reshape Novavax entirely.”

Shah Capital cited a 27% drop in Novavax’s share price from $11 on January 1, 2023, when John C. Jacobs took over as president and CEO, to $8 on March 31, 2026. The fund also expressed frustration that the COVID-19/influenza combination vaccine Novavax is developing with Sanofi (Euronext Paris: SAN)—a potential $5+ billion category, according to Shah Capital—hasn’t yet begun Phase III trials. Sanofi shared positive Phase I/II data in December and told Novavax it is working with regulators on next steps.

“Management has failed to implement aggressive cost-cutting measures necessary to achieve consistent profitability,” Himanshu H. Shah, the fund’s managing partner and chief investment officer, advocated in an open letter to Novavax’s board. “The current senior management team should be reduced by 30% to reflect Novavax’s new royalty and partnership business model.”

“The board size should also be reduced to five from eight, including electing new members with emphasis on pragmatic entrepreneurial experience to turn Novavax into an equity success story,” Shah added.

At odds for months

Shah has been at odds with Jacobs and Novavax leadership for months, having called for a sale of the company last October. Shah has held off pursuing a proxy campaign since the board’s majority has favored current management.

Novavax is based in Gaithersburg, MD, and reported approximately 749 employees as of December 31, 2025, down 21% from 952 a year earlier, according to Form 10-K annual reports.

Novavax investors responded to the Shah Capital letter with a buying spurt that sent shares climbing 5.5% Wednesday, from $7.98 to $8.42 after rising to $8.60 during intraday trading. The momentum continued somewhat on Thursday as shares rose another 1.4%, to $8.54, though Novavax slumped 5% Friday to finish the week at $8.12.

Shah Capital’s letter also sparked a statement to GEN and other news outlets from Novavax, which asserted that its board and management team “are committed to progressing our growth strategy, which is designed to leverage partnerships and R&D innovation to maximize the value of our technology.”

The statement cited recent Novavax efforts that include its up-to-$530 million (plus royalties) partnership with Pfizer (NYSE: PFE), which entered into a non-exclusive license agreement with Pfizer for use of Novavax’s Matrix-M® adjuvant; additional and expanded material transfer agreements with pharmaceuticals; and what the company called “continued progress” on its partnership with Sanofi, from which Novavax generated $225 million in milestone payments last year.

“In addition, we continue to make targeted investments in R&D with the intention of driving further value from our technology, while continuing to significantly reduce costs in our lean and efficient operating model,” Novavax continued. “We maintain constructive dialogue with our shareholders, and we welcome collaborative input that is in the best interest of Novavax and all of its shareholders.”

Shah essentially controls 14,845,097 shares of Novavax stock, including 125,359 shares he owns personally, and 14,719,738 shares owned by Shah Capital and its investment adviser.

Leaders and laggards

  • Invivyd (NASDAQ: IVVD) shares jumped 32% from $1.35 to $1.78 Thursday after the company announced positive progress in its REVOLUTION clinical program for VYD2311, a monoclonal antibody candidate designed to prevent symptomatic COVID-19. As of April 6, when the first 1,500 of 1,818 subjects reached Day 45, clinical events supported statistical powering for the high end of anticipated VYD2311 efficacy levels in the Phase III DECLARATION trial (NCT07298434), with about half of the base study still to be carried out, Invivyd said. DECLARATION will enroll ~500 additional subjects, which, according to the company, will likely, depending on recruitment rates, push back the timing for data release by approximately two months, from mid-year to Q3 2026. Invivyd also announced the discovery and advancement of a “highly potent,” half-life-extended, high-resistance-barrier measles monoclonal antibody candidate, VMS063.
  • Replimune Group (NASDAQ: REPL) shares tumbled 19.5% from $5.91 to $4.76 Friday after the developer of oncolytic immunotherapies disclosed that the FDA for a second time had rejected the company’s biologics license application (BLA) for its lead product candidate RP1 (vusolimogene oderparepvec) in combination with nivolumab to treat advanced melanoma, instead issuing a complete response letter (CRL). Replimune criticized the FDA for an inconsistent review process, saying the agency contradicted earlier guidance to the company and assessed the resubmitted BLA through a different review team that replaced the team that previously interacted with the company. Replimune also defended the combination therapy’s data in the Phase II IGNYTE trial (NCT03767348)—a 34% response rate with a median duration of 24.8 months and a favorable safety profile, the basis of the combo’s breakthrough therapy designation. “We have no choice but to eliminate jobs, including substantially scaling back our U.S.-based manufacturing operations,” stated Replimune CEO Sushil Patel, PhD. Nivolumab is the cancer immunotherapy marketed as Opdivo® by Bristol Myers Squibb (NYSE: BMY).

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Cryo-EM Structural Biology Facility Opened in San Diego by FairJourney Bio

FairJourney Bio (FJBio), a CRO, opened its advanced cryo-electron microscopy (cryo-EM) structural biology facility in San Diego. The new site significantly expands the company’s presence in the U.S. market and incorporates atomic-resolution structural biology directly into its antibody discovery platform, according to Christopher Arthur, PhD, CSO, structural biology, FairJourney Bio.

The facility houses a 300 kV cryo-EM infrastructure, including two ThermoFisher Titan Krios 5 systems, which enables native-state structure determination of antibody-target complexes. The technology is designed to provide detailed insights across the R&D value chain—from epitope mapping and hit generation to structure-guided lead optimization and candidate selection.

FairJourney Bio’s San Diego facility opening event. [FairJourney Bio]
FairJourney Bio’s San Diego facility opening event. [FairJourney Bio]

The cryo-EM services, launched in January 2026, complement FJBio’s antibody discovery and biologic development portfolio. The services enable scientists to visualize protein structures at atomic resolution, including protein-protein and protein-ligand complexes, providing high-quality and interpretable results to inform confident decision-making across programs.

The facility is strategically positioned within a leading global biotech hub, complementing FJBio’s existing U.S. presence in San Francisco and its operations across Europe.

“Structural biology has historically been a late-stage tool, used to confirm decisions already made,” notes Arthur. “We are redefining that paradigm. In San Diego, we are building a premier, full-service cryo-EM CRO that brings together decades of deep expertise in sample preparation, data collection, and computational analysis, embedding structural insight at the very start of discovery, where it shapes epitope selection and determines which leads are worth advancing.”

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Metabolic Driver of Radiation Resistance in Lung Cancer Identified

Radiation therapy remains a cornerstone of lung cancer treatment, yet its long-term effectiveness is often undermined by a persistent challenge: tumors adapt and become resistant. Understanding, and overcoming, this resistance is a major priority in oncology.

A new study from researchers at The University of Texas MD Anderson Cancer Center, published in Cancer Research, identifies a metabolic mechanism that allows lung cancer cells to evade radiation-induced death and proposes a clinically actionable strategy to counter it.

A hidden driver of resistance

Radiotherapy works by damaging cancer cells in multiple ways, including triggering ferroptosis—an iron-dependent form of cell death driven by oxidative stress. However, many tumors develop the ability to suppress this process, allowing them to survive treatment.

The new study pinpoints a key player in this resistance: the mitochondrial enzyme dihydroorotate dehydrogenase (DHODH). Researchers found that radiation exposure increases DHODH activity in lung cancer cells, enabling them to withstand ferroptosis and continue growing.

“This is an important finding because of the immediate translational opportunity,” said Boyi Gan, PhD, senior author of the study. “By understanding how DHODH is preventing cell death in radioresistant cancer cells, we were able to develop a strategy to overcome radiation therapy resistance in tumor models.”

A metabolic shield against cell death

DHODH is best known for its role in nucleotide synthesis, helping cells produce the building blocks needed for DNA repair and replication. But the study highlights an additional function that is particularly relevant in cancer.

The enzyme also supports the production of ubiquinol, a molecule that protects cells from oxidative damage. In the context of radiation therapy, this acts as a shield, preventing the lipid damage required to trigger ferroptosis.

By simultaneously promoting DNA repair and suppressing ferroptosis, DHODH enables cancer cells to survive what would otherwise be lethal radiation-induced stress.

Repurposing an existing drug

Rather than developing a new inhibitor from scratch, the researchers turned to leflunomide—an FDA-approved drug currently used to treat rheumatoid arthritis, which is known to inhibit DHODH.

In preclinical models, blocking DHODH alone modestly increased sensitivity to radiation. However, the most striking results emerged when the team combined three treatment modalities: radiation therapy, immune checkpoint blockade, and DHODH inhibition.

Radiation plus immunotherapy alone was insufficient to control tumor growth. But when leflunomide was added, the combination restored ferroptosis and led to a marked reduction in tumor progression.

“DHODH inhibition alone had some effect on sensitization to radiation therapy, but it was really this triple combination that had a marked effect,” Gan said.

Leveraging the immune response

A key aspect of the strategy lies in its interaction with the immune system. Immunotherapy, specifically anti–PD-1 checkpoint blockade, stimulates the production of interferon-gamma (IFN-γ), a signaling molecule that can enhance ferroptosis.

However, in resistant tumors, this signal alone is not enough to overcome the protective effects of DHODH. By inhibiting the enzyme, the researchers effectively remove this metabolic barrier, allowing IFN-γ–driven ferroptosis to proceed.

The result is a coordinated therapeutic effect in which radiation induces stress, immunotherapy amplifies cell death signals, and DHODH inhibition prevents tumor cells from escaping.

Toward clinical translation

One of the most compelling aspects of the study is its translational potential. Leflunomide is already widely used in clinical practice, with a well-characterized safety profile, potentially accelerating its evaluation in oncology settings.

“These findings provide a good rationale for testing this combination in clinical studies,” Gan said in a press release.

If validated in patients, this approach could offer a new strategy for overcoming resistance not only in lung cancer but potentially in other solid tumors treated with radiotherapy.

A broader shift in cancer therapy

The findings also reflect a broader trend in cancer research: targeting metabolic pathways that enable tumor survival under stress. While traditional therapies focus on directly damaging cancer cells, emerging approaches aim to disrupt the adaptive mechanisms that allow tumors to recover.

By linking metabolism, immune signaling, and cell death pathways, the study provides a more integrated view of how resistance develops—and how it can be reversed.

Although the results are based on preclinical models, they offer a clear path forward. Future clinical trials will be needed to determine whether the triple combination strategy can improve outcomes in patients with radioresistant lung cancer.

More broadly, the work highlights the importance of identifying “druggable” vulnerabilities within resistance pathways, especially those that can be targeted with existing therapies.

In this case, a drug originally developed for autoimmune disease may help solve one of the most persistent challenges in cancer treatment: restoring the effectiveness of radiation therapy when it begins to fail.

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