Lasers lifting tiny metajets & Self-organizing laser pencil beam - News (Apr 29, 2026)

Let’s start in the lab, where light is doing jobs we normally associate with machines. At Texas A&M, researchers have shown a new form of optical propulsion—using lasers to lift and steer tiny devices called “metajets” in full three-dimensional motion. The twist is that the control isn’t mainly in fancy sculpted light patterns; it’s built into the material itself. These micron-scale objects use engineered metasurfaces—nanoscale patterns that change how light’s push transfers into motion. No physical contact, no onboard fuel, just momentum from light. The team tested the metajets in fluid to make gravity less dominant and to better watch the laser-driven maneuvers. Next, they want to try microgravity, where the idea can be judged without Earth constantly pulling things down. It’s interesting because it moves laser-driven propulsion from a conceptual demo toward something that looks more controllable—and, potentially, more scalable over time.

Sticking with lasers, MIT researchers are reporting something that flips a long-held expectation about multimode optical fibers. Normally, if you crank up the power through these fibers, the light can get chaotic—bad news for imaging and precision work. But MIT says that, under the right conditions, a high-power laser can spontaneously self-organize into a tightly focused “pencil beam.” That effect showed up when the laser was injected precisely on-axis and the power reached a critical level where the fiber’s imperfections and the glass’s nonlinear behavior essentially counterbalanced one another. Why it matters: the beam forms without complex custom beam-shaping hardware, and it produces a stable, high-quality focus with fewer image-distorting artifacts. In a practical demonstration, the team used it for multiphoton imaging and produced 3D, cellular-level images of a human blood–brain barrier model far faster than a standard approach, while keeping similar resolution. That could help researchers watch, in real time, whether candidate drugs for neurodegenerative disease actually reach relevant brain targets—potentially speeding early-stage screening and reducing dependence on animal models.

Now to public health and pandemic prevention—where the goal is to figure out which animal viruses deserve the closest attention, without taking unnecessary risks. A Nature study out of the UK describes a safer way to evaluate whether certain animal coronaviruses might be able to infect humans. Instead of working with live viruses, the researchers used genome sequences to recreate just the spike proteins on “pseudotyped” particles. Those particles can latch onto cells but can’t replicate, lowering the biosafety stakes. When they screened these against human cells, most of the bat alphacoronaviruses they tested didn’t bind well to human entry receptors. But one lesser-known virus found in Kenyan bats—called KY43—bound strongly to a human cell-surface protein. Importantly, that doesn’t mean an outbreak is imminent; binding is only the first gate, and there’s no evidence people in the region are infected. But the work does something valuable: it helps triage which viruses should be monitored more closely, and it offers a scalable template for pre-pandemic risk checks whenever a genome sequence is available.

To health policy, where prevention often comes down to what people can realistically stick with. South Africa’s Health Department is preparing a phased rollout of lenacapavir, a long-acting HIV prevention injection taken once every six months. Clinicians and officials see it as a potential leap forward compared with daily oral PrEP, mainly because adherence tends to improve when you’re not relying on a pill every single day. The plan is to begin at roughly 30 sites in high-burden districts, focusing first on groups at highest risk—like sex workers, men who have sex with men, adolescent girls and young women, and pregnant or breastfeeding women. Officials are also stressing a key point: this injection doesn’t protect against other sexually transmitted infections, so it’s not a replacement for condoms or broader prevention strategies. The big question is access. Supply is limited and largely donor-funded, and advocates warn that education, demand, and reliable follow-up for repeat doses will determine whether the impact matches the hype—especially after funding cuts weakened some community prevention efforts. Still, if uptake holds, it could be a meaningful tool toward South Africa’s goal of ending HIV as a public health threat by 2030.

Now to the collision of AI, power, and governance—where the industry is trying to write rules while also sprinting ahead. OpenAI CEO Sam Altman has released a set of new operating principles describing how the company says it will pursue increasingly advanced AI while trying to spread benefits broadly. The themes include democratization and empowerment, but also a clear note that access could tighten if safety or security risks climb. Compared with OpenAI’s older charter language, the updated guidance is seen as more flexible—fewer hard commitments, more room to adapt. This arrived at an awkward moment: OpenAI is heading into court for jury selection in a case arguing it drifted from its nonprofit mission toward a for-profit model. And that court backdrop got even louder with Elon Musk testifying in a dispute with Altman. Musk argues OpenAI was meant to be a nonprofit bulwark against profit-driven AI, and he’s seeking major damages and a court order that would push OpenAI back toward that original structure, including leadership changes. OpenAI’s side counters that Musk previously supported a for-profit approach and that massive funding—often tied to partnerships like Microsoft—is necessary to compete. Why this matters beyond the personalities: it’s a test case for how the world will structure and trust AI institutions when building these systems requires enormous capital, but the consequences affect everyone.

Related to that, there’s another sign that advanced AI is being pulled deeper into national security. According to reporting from The Information, Google is in talks with the U.S. Department of Defense to deploy its most advanced AI models inside classified environments. The wording described—“any lawful government purpose”—is drawing attention because it’s broad, potentially widening use cases well beyond narrowly defined missions. This is a notable shift for Google, which stepped away from Project Maven back in 2018 after internal protests and later set out AI principles aimed at limiting certain military applications. Now, employees are reportedly raising concerns again, warning that open-ended terms could enable harmful or escalatory uses. The larger issue here is accountability. Powerful AI systems can be opaque and can make confident mistakes. Once models are embedded in defense operations, questions multiply: who audits outcomes, who bears responsibility for errors, and how much control the company truly retains after deployment.

Let’s finish with two big stories in biomedical data—one about women’s health, and one about the future of genomics. First, researchers at the Barcelona Supercomputing Center have built what they call the first large-scale atlas showing how women’s reproductive organs age across the menopausal transition. Using AI to analyze tissue images and gene-expression data from hundreds of samples spanning ages 20 to 70, the atlas suggests menopause doesn’t affect all organs in the same way. Some tissues appear to shift gradually even before menopause, while others—like parts of the uterus—show more abrupt changes around the transition. It also points to the idea that different layers within the same organ can age at different speeds. The practical promise: the team reports blood-detectable molecular signals linked to reproductive aging in a much larger dataset, raising the possibility of tracking organ health without biopsies. If validated and used carefully, this could support more personalized care and earlier detection of menopause-related risks. Second, an analysis making the rounds argues that pairing AI with quantum computing could eventually speed up genomic analysis enough to make personalized medicine more feasible in clinics. Today, AI can help sift through genetic variants, but linking genes to disease reliably often takes huge comparisons across many genomes—slow, complex, and sometimes messy. The pitch is that quantum computers could accelerate certain pattern-finding and optimization steps, potentially cutting timelines dramatically for time-sensitive diagnoses. But the cautions are just as important: quantum computing remains immature, likely staying mostly lab-bound for years, and there are real equity and privacy risks if expensive, scarce tools concentrate in elite centers. The bottom line is that faster genomics could be transformative—but only if access and data governance are built in from the start.