Tech advances not only made IVF safer and more effective; they fundamentally changed the way we think about our reproduction. Technology is changing the way we make babies. The pioneering work of the scientists who invented IVF led to the birth of the first "test tube baby" in 1978. We've come a long, long way since then. This week, I've been working on a piece about the cutting edge of IVF technologies and what's coming next. Think AI and robots and, potentially, gene-edited embryos.

Bootstrap Bio and Manhattan Genomics, which were pursuing gene editing in human embryos to prevent serious disease, have shut down. Two companies that launched last year with plans to create gene-edited babies have already shut down, citing money issues and internal conflict. One of them, Manhattan Genomics of New York, closed abruptly shortly after announcing a team of scientific advisers in October that included a prominent fertility doctor, a data scientist who worked for de-extinction company Colossal Biosciences, and a scientist who pioneered a "three-parent" IVF technique. The other, California-based Bootstrap Bio, said it ceased operations in late 2025, as first reported by Mother Jones. Manhattan Genomics and Bootstrap Bio had ambitions to edit DNA in human embryos with the goal of preventing serious disease in babies.

The man who made India digital isn't done yet Nandan Nilekani can't stop trying to push India into the future. He started nearly 30 years ago, masterminding an ongoing experiment in technological state capacity that started with Aadhaar--the world's largest digital identity system. Using Aadhaar as the bedrock, Nilekani and people working with him went on to build a sprawling collection of free, interoperating online tools that add up to nothing less than a digital infrastructure for society, covering government services, banking, and health care. They offer convenience and access that would be eye-popping in wealthy countries a tenth of India's size. At 70 years old, Nilekani should be retired. But he has a few more ideas.

He Went to Prison for Gene-Editing Babies. Now He's Planning to Do It Again Chinese scientist He Jiankui wants to end Alzheimer's and thinks Silicon Valley is conducting a "Nazi eugenic experiment." In 2018, a nervous-looking He Jiankui took the stage at a scientific conference in Hong Kong. A hush settled over the packed auditorium as the soft-spoken Chinese scientist adjusted his microphone and confirmed the circulating media reports: He had created the world's first gene-edited babies . Three little girls were born with modifications to their genomes that were intended to protect them against HIV. The changes he'd made to their DNA were permanent and heritable, meaning they could be passed down to future generations.

Why personalized gene editing, genetic resurrections and embryo scoring made our list. Earlier this week, published its annual list of Ten Breakthrough Technologies. As always, it features technologies that made the news last year, and which--for better or worse--stand to make waves in the coming years. They're the technologies you should really be paying attention to. This year's list includes tech that's set to transform the energy industry, artificial intelligence, space travel --and of course biotech and health. Our breakthrough biotechnologies for 2026 involve editing a baby's genes and, separately, resurrecting genes from ancient species.

The Download: mimicking pregnancy's first moments in a lab, and AI parameters explained Plus: Google and Character.AI have settled a lawsuit linking their AI to the death of a teenager At first glance, it looks like the start of a human pregnancy: A ball-shaped embryo presses into the lining of the uterus then grips tight, burrowing in as the first tendrils of a future placenta appear. This is implantation--the moment that pregnancy officially begins. Only none of it is happening inside a body. These images were captured in a Beijing laboratory, inside a microfluidic chip, as scientists watched the scene unfold. In three recent papers published by Cell Press, scientists report what they call the most accurate efforts yet to mimic the first moments of pregnancy in the lab. They've taken human embryos from IVF centers and let these merge with "organoids" made of endometrial cells, which form the lining of the uterus.

This year, hype around AI really exploded, and so did concerns about AI's environmental footprint. We also saw some surprising biotech developments. It's been a busy and productive year here at . We published magazine issues on power, creativity, innovation, bodies, relationships, and security . We hosted 14 exclusive virtual conversations with our editors and outside experts in our subscriber-only series, Roundtables, and held two events on MIT's campus. And we published hundreds of articles online, following new developments in computing, climate tech, robotics, and more.

Infertility is a major global health issue, and while in-vitro fertilization has improved treatment outcomes, embryo selection remains a critical bottleneck. Time-lapse imaging enables continuous, non-invasive monitoring of embryo development, yet most automated assessment methods rely solely on conventional morphokinetic features and overlook emerging biomarkers. Cytoplasmic Strings, thin filamentous structures connecting the inner cell mass and trophectoderm in expanded blastocysts, have been associated with faster blastocyst formation, higher blastocyst grades, and improved viability. However, CS assessment currently depends on manual visual inspection, which is labor-intensive, subjective, and severely affected by detection and subtle visual appearance. In this work, we present, to the best of our knowledge, the first computational framework for CS analysis in human IVF embryos. We first design a human-in-the-loop annotation pipeline to curate a biologically validated CS dataset from TLI videos, comprising 13,568 frames with highly sparse CS-positive instances. Building on this dataset, we propose a two-stage deep learning framework that (i) classifies CS presence at the frame level and (ii) localizes CS regions in positive cases. To address severe imbalance and feature uncertainty, we introduce the Novel Uncertainty-aware Contractive Embedding (NUCE) loss, which couples confidence-aware reweighting with an embedding contraction term to form compact, well-separated class clusters. NUCE consistently improves F1-score across five transformer backbones, while RF-DETR-based localization achieves state-of-the-art (SOTA) detection performance for thin, low-contrast CS structures. The source code will be made publicly available at: https://github.com/HamadYA/CS_Detection.

We'll keep following these developments, but this just wasn't their year. If you're a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future . This group exercise is mostly fun and always engrossing, but at times it can also be quite difficult. We collectively pitch dozens of ideas, and the editors meticulously review and debate the merits of each. We agonize over which ones might make the broadest impact, whether one is too similar to something we've featured in the past, and how confident we are that a recent advance will actually translate into long-term success. There is plenty of lively discussion along the way.

A startup's ads for controversial embryo tests hit the New York City subway. One day this fall, I watched an electronic sign outside the Broadway-Lafayette subway station in Manhattan switch seamlessly between an ad for makeup and one promoting the website Pickyourbaby.com, Inside the station, every surface was wrapped with more ads--babies on turnstiles, on staircases, on banners overhead. To his mind, one should be as accessible as the other. Nucleus is a young, attention-seeking genetic software company that says it can analyze genetic tests on IVF embryos to score them for 2,000 traits and disease risks, letting parents pick some and reject others. This is possible because of how our DNA shapes us, sometimes powerfully.