What to expect from Google this week

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When Google opens its doors tomorrow for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race. A year ago, at Google I/O 2025, the situation looked very different: The company was still riding high from the launch of Gemini 2.5 Pro that March, and distinguishing among the top-tier large language models often felt like a subjective splitting of hairs. 

But a foundation model’s reputation these days rests largely on its coding capabilities, and for months Google’s coding tools have been outgunned by Anthropic’s Claude Code and OpenAI’s Codex. Those systems are so dramatically superior to Google’s own offerings that the company has reportedly had to allow some engineers at DeepMind, its AI division, to use Claude for their work—lest they fall farther behind.

So when I arrive at the conference in Mountain View, California tomorrow, I’ll certainly be on the lookout for any efforts Google is making to claw its way back into frontrunner position. But I’m also eager to see new developments in areas where Google shapes the cutting edge, such as AI for science. The company’s moves there might receive less attention, but they will be no less consequential. 

Here are three things I’ll be paying particular attention to over the next two days.

An attempted coding comeback

Google is taking its AI coding crisis seriously. According to reporting from The Information, there’s a new AI coding team at DeepMind. And the Los Angeles Times has reported that John Jumper, who shared a 2024 Nobel Prize in chemistry with DeepMind CEO Demis Hassabis for their work on the protein structure prediction software AlphaFold, is lending his talents to the efforts. I would be surprised if we don’t see a major new coding release at I/O, perhaps in the form of an update to the company’s Antigravity agentic coding platform.

That said, we shouldn’t expect anything transformative here. Googlers have access to models and products that are substantially ahead of those released to the public, yet they were still reportedly fighting over who got access to Claude Code last month. Unless the company has made astonishing progress since then, Google probably won’t make it back to the coding frontier in the next two days.

Science and health

Coding might be Google DeepMind’s weakness, but science is its conspicuous strength. It is the only frontier AI company to have earned a Nobel Prize. And as LLMs have come to dominate the AI-for-science landscape, Google has only solidified its lead. Last year, the company released multiple scientific AI tools, including the AI co-scientist, which formulates hypotheses and research plans in response to user questions and has been described as an “oracle” by one Stanford scientist, and AlphaEvolve, a system that iteratively discovers new solutions for mathematical and computational problems. If any new scientific tools are announced at I/O, they’ll be worth noting.

I’ll also be paying close attention to any moves Google makes in health and medicine. Google is doing some of the best research out there on LLM-based health tools, but OpenAI has defined the health AI conversation since the release of ChatGPT Health in January. Google has announced that it will be making its AI-powered Health Coach publicly available tomorrow, but promotional material suggests that the tool is geared more toward providing advice on topics such as fitness and diet than to addressing users’ medical concerns. Is this another area where Google has fallen behind, or is the company exercising appropriate caution in a high-stakes domain? 

The drama

While Google fans congregate down in Mountain View, roughly 30 miles north in Oakland the Elon Musk v. Sam Altman trial will be wrapping up. The past few months have seen more than their fair share of AI CEO drama—before the trial, the animosity between Altman and Anthropic CEO Dario Amodei took center stage as Anthropic and OpenAI worked to negotiate deals with the US Department of Defense. But DeepMind’s Hassabis has, for the most part, steered clear of such drama. He effectively presents himself as a Nobel Prize-winning nerd, and if he has written screeds about any of his peers, they haven’t been leaked to the press or appeared in legal discovery.

That’s not to say that Google is controversy free. Last month, a group of 600 employees, many of whom work for DeepMind, sent a letter to CEO Sundar Pichai protesting an impending DoD deal. Google signed that deal the next day. Hassabis, Pichai, and all the other big names will surely do their best to skirt these and other touchy subjects while on stage, but controversies will worm their way in regardless. It will be interesting to see whether Google can maintain its veneer of neutrality.

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Inside Anduril and Meta’s quest to make smart glasses for warfare

The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands.

Quay Barnett, who leads the efforts as a vice president at Anduril following a career in the Army’s Special Operations Command, says his fundamental goal is to optimize “the human as a weapons system.” The vision is undoubtedly cyborg-inspired: Barnett wants drones and soldiers to see together, share information seamlessly, and make decisions as one. 

Anduril actually has two such projects in the works. The first is the Army’s Soldier Born Mission Command, or SBMC, for which the company won a $159 million prototyping contract last year to work with Meta on augmented-reality glasses to attach to existing military helmets. But Anduril has also embarked on a self-funded side quest, announced in October, to design its own helmet and headset combo called EagleEye. This is something the military has not asked for, but Anduril insists it will prefer it and purchase it in the end.

So far, both systems are years away. The Army isn’t expected to move its top choice for the SBMC program into production until 2028, if it picks one at all (the previous lead for the effort, Microsoft, was set to receive a $22 billion production contract that was ultimately cancelled when the glasses didn’t prove viable). But Barnett told MIT Technology Review about where both Anduril’s prototypes are headed.

Depending on the situation, the glasses for either prototype will overlay certain information onto a soldier’s field of view. This might be as simple as a compass or as complex as an entire map of the area, information about where nearby drones are flying, or AI-driven recognition of a target like a truck. 

The soldier would then speak to the interface in plain language—for example, to order an evacuation for someone who’s been injured or to plan a route taking into account which areas are off limits. A large language model—Anduril is in tests with Google’s Gemini, Meta’s Llama, and even Anthropic’s Claude, despite the company’s conflict with the Pentagon—will be used to help translate a soldier’s speech into commands the software can follow. And the engine for it all will be Anduril’s software Lattice, which incorporates data from lots of different military hardware into one picture. The Army announced in March that it would spend $20 billion to integrate Lattice with essentially its entire infrastructure.

Barnett’s team is designing the headset to carry out multi-step tasks. A soldier might send a drone to surveil an area and instruct it to come back once it’s found something that looks like an artillery unit; then the system would recommend courses of action, like sending a nearby drone to strike, that would have to be approved by the normal chain of command. Leading the system through this, if all goes to plan, might not even require speech; the soldier could instead communicate through tracked eye movements and subtle taps.

That’s the idea, anyway. It’s worked on early prototypes, Barnett says, but there aren’t yet versions ready for the Army to test at scale. The component parts began arriving in March. Because of federal military contracting rules, these parts—unlike Meta’s commercial smart glasses—required new supply chains that don’t rely on Chinese companies.

It’s a lot for soldiers already bogged down in information overload, says Jonathan Wong, a former US Marine who works as a senior policy researcher at RAND on Army efforts to buy new tech. Both smart glasses projects aim to create a clean interface that presents only the right information at the right time. But it’s a product that soldiers will reject if it costs more of their attention than it saves. “How much mental bandwidth do you have to be both aware of your surroundings and to operate this technology in a way that makes you and your whole unit better?” he says.

Wong recalls that as a platoon commander, for example, he had a radio that operated on three different channels at once. “The moment that two people were on different channels talking at the same time, I immediately couldn’t comprehend anything that either one of them was trying to tell me, and I was probably not aware of my own surroundings,” he says. “I think there are limits to what you can take in.”

Ideally, Barnett says, smart glasses can ease that information overload. Anduril’s approach is to get creative with ways the user can access necessary information quickly. Voice commands and eye tracking are a piece of that strategy. But even if it’s all technically feasible, it might take years of field testing to know if the system is actually useful for soldiers, Wong says. 

Such a system would mark a major escalation in how closely soldiers rely on imperfect AI systems. While computer vision models used to identify objects have long been employed by militaries, and chatbots have recently entered decision-making during the war in Iran, these technologies have not yet made their way to most frontline soldiers. A smart glasses system tasked with identifying threats and recommending strikes would introduce massive new risks of errors. 

Anduril is not the only one competing to develop smart goggles for combat. Rivet, which specializes in wearable sensors for the military, received a $195 million prototyping contract the same time, and in March the Israeli defense-tech company Elbit received its own $120 million contract. This all comes after Microsoft lost its role leading the Army’s smart glasses effort, following a Pentagon audit that found the Army wasn’t properly testing the glasses, a mistake that could have wasted $22 billion.

For both Anduril’s prototypes, the company is testing a new system for digital night vision, which uses electronic sensors and algorithms to boost low levels of light. It’s been a promised technology for decades but has tended to work too slowly for practical use and produce grainy images. Anduril says it has found improvements over previous prototypes through techniques rooted in both new generative AI and older machine learning. 

Much of the other hardware for both projects is being built by Meta, including the displays and the waveguides that send visuals to the user’s eye without blocking the view. That might be a surprise to anyone who knows the backstory: In 2017, Facebook (now Meta) ousted Anduril founder Palmer Luckey following an internal conflict involving his support for Donald Trump. The two are now back in the augmented-reality business together, while Mark Zuckerberg has also adopted a friendlier posture toward the second Trump administration.

For the Army initiative, this suite of smart glasses, night vision, and sensors will be attached to the helmets and other gear soldiers already wear, with a separate battery pack. The EagleEye version will instead incorporate the tech into the helmet itself. Even if the Army doesn’t prefer EagleEye in the end, Barnett says, Anduril will attempt to sell the system to foreign militaries.

Multiple challenges must still be overcome. Unlike Meta’s Ray-Ban glasses, the prototypes have to operate in an environment full of dust, explosions, and smoke. Adding the computing power and battery life they need also means more weight for soldiers already carrying upwards of 100 pounds. Then the technology has to work in environments without ubiquitous 5G cell connections; powerful computer vision and AI models will need to run locally on the device.

For the Army to want to buy it at scale, “it’s got to work, and it’s got to be pretty seamless,” Wong says. “It’s a high bar.”

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Genetic and clinical investigation of insulin-degrading enzyme in Parkinson’s disease within the Chinese Han population

IntroductionGrowing evidence suggests a mechanistic link between type 2 diabetes mellitus and Parkinson’s disease (PD), with insulin-degrading enzyme (IDE) implicated in both insulin and amyloid-β metabolism, as well as α-synuclein degradation. However, the role of IDE in PD pathogenesis remains insufficiently defined. This study aimed to investigate the association of IDE gene polymorphisms and serum IDE levels with sporadic PD in a Chinese Han population.MethodsFourteen single nucleotide polymorphisms (SNPs) within the IDE gene were genotyped in 463 patients with sporadic PD and 576 age- and sex-matched healthy controls (HCs). An independent cohort of 100 PD patients and 100 HCs was used to quantify serum IDE concentrations. Correlations between IDE levels and clinical features were assessed. Logistic regression was employed to identify independent factors associated with PD.ResultsAmong the examined SNPs, rs11187007 showed a nominal allelic association with PD (P = 0.046), which did not survive the Bonferroni correction. Serum IDE concentrations were significantly higher in PD patients than in HCs (P = 0.015). Elevated IDE levels were negatively correlated with Mini-Mental State Examination scores (R = –0.230, P = 0.027) and positively associated with more severe symptoms. Logistic regression indicated that elevated serum IDE levels were associated with PD.ConclusionOur findings highlight that elevated serum IDE correlates with PD, suggesting a role for IDE in neurodegeneration, warranting further mechanistic and longitudinal studies to evaluate its potential as a therapeutic target in PD.

From autophagy–lysosomal deficits to neurodegeneration in Niemann-Pick type C1 disease: implications for age-related neurodegenerative disorders

Niemann-Pick type C1 (NPC1) disease is a neurodegenerative lysosomal storage disorder caused by loss-of-function mutations in the NPC1 gene. NPC1 deficit primarily disrupts lipid homeostasis and subsequently drives cellular degeneration through mechanisms involving impaired autophagy and mitophagy, mitochondrial dysfunction, and, recently demonstrated NAD depletion that links autophagy impairment to neuronal death. Emerging evidence also highlights the activation of innate immune signaling leading to neuroinflammation. In this review, we synthesize current mechanistic insights and describe how these molecular deficits are interconnected to drive neuronal death in NPC1 disease. We also discuss how these pathological processes parallel those observed in major age-related neurodegenerative pathologies such as Alzheimer’s and Parkinson’s disease. Finally, we highlight emerging therapeutic strategies that can potentially ameliorate these cellular deficits, offering avenues for mitigating neurodegeneration in NPC1 disease and other related neurodegenerative disorders.

Effects of motor imagery brain-computer interface task on quantitative EEG features in patients with prolonged disorders of consciousness

ObjectiveTo analyze quantitative electroencephalographic (EEG) characteristics during Motor Imagery Brain-Computer Interface (MI-BCI) task in patients with prolonged disorders of consciousness (pDoC).MethodsForty-three patients with pDoC due to various brain injuries were enrolled. Based on modified Coma Recovery Scale-Revised (CRS-R) assessments, the patients were divided into 19 in the unresponsive wakefulness syndrome (UWS) group and 24 in the minimally conscious state (MCS) group. All patients underwent 5 min of resting-state (RS) EEG followed by 5 min of MI-BCI task. Relative power, DTABR, and average brain engagement (BE) during MI-BCI were analyzed across resting and MI-BCI states using Fast Fourier Transform (FFT) spectra.ResultsMixed-design ANOVA showed significant main effects of condition and group across all EEG frequency bands, indicating clear differences between the RS and MI-BCI conditions and between UWS and MCS patients. Significant group × condition interactions were found in the delta, beta, and gamma bands, as well as in DTABR. Simple effects analysis showed that delta power was higher in RS than in MI-BCI in both groups, with UWS consistently exhibiting higher delta power than MCS under both conditions. In contrast, beta and gamma power were higher in MI-BCI than in RS in both groups. For beta power, UWS was higher than MCS under RS, whereas MCS was higher than UWS under MI-BCI, showing a reversal of the interaction pattern. For gamma power, MCS showed higher values than UWS under both conditions, with a larger between-group difference during MI-BCI. DTABR was significantly higher in RS than in MI-BCI in both groups; however, MCS exhibited higher DTABR than UWS under RS, whereas the opposite pattern was observed under MI-BCI. In addition, during MI-BCI tasks, the MCS group showed greater average BE than the UWS group.ConclusionMI-BCI shows potential as a diagnostic or assessment tool for evaluating the level of consciousness in patients with pDoC.