Most scientists think that consciousness is created by the brain. After all, most assume consciousness vanishes if the brain is destroyed. But what if this consensus view is radically mistaken? Join distinguished Cambridge scientist Rupert Sheldrake as he argues that the mind extends beyond the brain and explores the radical implications of this account.
For more than two decades, researchers have identified hundreds of genes that increase the risk of autism spectrum disorder (ASD). Yet multiple fundamental questions have remained unanswered: among them, how do mutations in these genes lead directly to changes in brain development and how can that knowledge be translated into more effective therapies?
In a landmark study published in Science, scientists have taken a major step toward answering both questions. The findings are the result of more than a decade of work. By building the largest-ever molecular interaction map of autism, the team revealed how hundreds of genes and dozens of mutations converge within a surprisingly small number of shared protein networks, hurdling a major roadblock to the development of new precision medicines.
Rather than focusing only on the genes linked to autism, the researchers mapped the proteins encoded by those genes and discovered exactly how individual disease-causing mutations can rewire the molecular machinery of the developing brain. The work uncovers an entirely new layer of disease biology that can be targeted therapeutically and provides a framework for designing medicines that directly address a wide range of underlying molecular causes of autism.
Token caching can cut enterprise AI costs by 80%
In 2026, the biggest cost advantage isn’t choosing a cheaper model — it’s token caching.
When the same system prompts or knowledge base get reused, providers discount that input by 80–90%. Claude Sonnet 5, for example, drops from $2.00 to $0.20 per million tokens on cached content.
Enterprises with heavy RAG or multi-turn workflows are leaving $400K–$520K on the table annually by not using it.
Key takeaway for procurement:
Stop comparing raw token prices. Start modeling cost-per-result on your actual query patterns. Smaller models with strong caching often win.
Full analysis:
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World Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest world model, and what it reveals about the pursuit of spatial intelligence. At the center of Atlas is what the team calls “new view prediction”: given images or views of a scene, the model predicts what that environment should look like from a different position in space and time. This brings generation and 3D reconstruction into the same model, and raises a broader question about whether predicting views could become a useful primitive for understanding the physical world. They discuss the technical bets behind the model, what it can and can’t yet capture, and the importance of dynamics, editability, and simulation as world models develop. The conversation also explores applications in creative work, architecture, and robotics, where Fei-Fei argues that one of today’s biggest constraints is access to real-world training data. Timestamps: 00:00 — Intro 00:51 — What Atlas Is & Why It Matters 05:15 — Is This a Scaled-Up Video Model or a New Architecture? 08:15 — Spatial Intelligence & Why New View Prediction Matters 21:27 — Did You Know It Was Going to Work? 24:42 — Use Cases: Creatives, Games & Robotics 35:21 — The Elephant in the Room: Video Models vs World Models 37:55 — Will We Get 4D Video You Can Walk Around In? 42:22 — Why New View Prediction Is the Next Token Prediction Resources: Follow Fei-Fei Li on X: https://twitter.com/drfeifei Follow Justin Johnson on X: https://twitter.com/jcjohnss Follow Ben Mildenhall on X: https://twitter.com/BenMildenhall Follow Martin Casado on X: https://twitter.com/martin_casado Learn more about Atlas: https://www.worldlabs.ai/blog/atlas Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: / a16z Find a16z on LinkedIn:
/ a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RD… to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast… Follow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.
Beneath the Arctic Ocean is an orchestra featuring natural and human composers, from cracking sea ice and whistling beluga whales to humming shipping-vessel engines. Researchers from MIT Lincoln Laboratory heard some of this cacophony when analyzing data from commercial off-the-shelf sensors that they integrated and deployed in 2024 during the U.S. Navy’s Operation Ice Camp (OIC). This past March, during OIC 2026, the researchers returned to the Arctic with a higher-fidelity version of one of the sensors, a geophone that detects vibrations in the sea ice.
“We’re interested in things that make sound underneath the ice,” says Ben Evans, a researcher in the laboratory’s Advanced Undersea Systems and Technology Group. “For example, our OIC 2024 data contained marine mammal songs. We need a better understanding of how such signals propagate through ice and how to distinguish these signals from other sources.”
This underwater soundscape is shifting as sheets of Arctic sea ice rapidly break and melt, opening previously impassable maritime routes for military and commercial activity. Determining the unique sound profiles, or acoustic signatures, produced by fracturing ice will enable researchers to develop predictive capabilities that can build coastal community resilience, inform geopolitical strategy and surveil adversary Arctic activity.
Researchers at the University of Southern California (USC) developed a novel tool in which they can detect ‘softness’ of a tumor and help inform therapeutic outcomes. The physical attributes of tumors, particularly hardness, is a persistent obstacle for cancer immunotherapy. Historically, firmness of most solid tumors correlates with the penetration of therapy and could lead to drug resistance. Scientists that developed this technology recently published their findings in Nature Biomedical Engineering. The team, led by Dr. Yingxiao Wang, details how the softness of tumors can allow them to adapt and evade the immune system and treatment. This finding is contradictory from what previous articles in the field have stated.
Wang is the Dwight C. and Hildagarde E. Baum Chair in Biomedical Engineering and Professor of Biomedical Engineering and Molecular Microbiology & Immunology in the USC Viterbi School of Engineering and associated with the Keck School of Medicine. Collaborations with the Wang Lab developed a way to target soft stem-like cancer cells, which are extremely hard to treat. Cancer stem-cells are key drivers of tumor growth, drug resistance, and immune evasion. These cells are a major topic of interest in the Wang Lab. Wang and his team also focus their research on techniques to detect biomarkers and visualize molecular events in cells. These investigations could lead to optimal treatment delivery to the tumor site and enhancement of immunotherapy.
Researchers are using an immunotherapy known as chimeric antigen receptor (CAR)- T cells, which programs T cells to specifically target the tumor. T cells are specialized immune cells tasked with identifying and eliminating disease. They are a critical component of the immune system and correlate to survival. However, in the context of cancer, they become inert and less active due to tumor-secreting molecules and proteins that dysregulate their function. As a result, scientists in the field of immuno-oncology (IO) have focused on these cells to overcome therapeutic resistance. To generate CAR-T cells, scientists take T cells from a patient and engineer them to redirect the immune response toward the tumor. The CAR-T cells are then able to identify specific proteins on the tumor, which reduce off-target cell death and limit toxicity. Unfortunately, CAR-T cells are less effective against solid tumors.
The human body and its gut microbiome produce thousands of small molecules that shape how the body functions—influencing immunity, metabolism and more. Identifying what those molecules actually are has been one of the biomedical sciences’ most persistent bottlenecks. More than 80% of compounds detected in a typical biological sample cannot be matched to any known structure using current methods.
Researchers at the Boyce Thompson Institute (BTI) and Cornell University have developed a tool that begins to change that. AIMe, short for AI Molecule Explorer, uses a form of artificial intelligence called neuro-symbolic AI to predict, organize and search the mass spectra of more than 100 million known small organic molecules—effectively building a vast searchable map of chemical space that can accelerate hypothesis generation and compound identification.
The work is a collaboration between Frank Schroeder, professor at BTI and in Cornell’s Department of Chemistry and Chemical Biology, and Carla Gomes, professor of computing and information science and director of Cornell’s AI for Science Institute.
As the BepiColombo mission prepares to enter the final phase of its journey to Mercury, a series of studies conducted by researchers at the University of Liège and KU Leuven sheds new light on the early stages of the evolution of the planet closest to the sun. Using experimental petrology, the researchers are reconstructing in the laboratory the formation of Mercury’s core, the crystallization of its magma ocean and the formation of its mantle. The studies are published in Earth and Planetary Science Letters, Nature Communications and Advances in Geochemistry and Cosmochemistry.
The terrestrial planets (Mercury, Venus, Earth and Mars) are the result of more than four billion years of evolution, which began with accretion from the disk surrounding the young sun. During the early stages of evolution, the heat released caused these planets to melt, creating what is known as a magma ocean.
This key stage determines the distribution of elements between the metallic core and the mantle. As it crystallizes, this ocean structures the solid mantle, the partial melting of which will subsequently generate the magmas that form the crust. It is also at this stage that an initial atmosphere may form.
While many fear artificial intelligence will replace humans, using AI to take over some human roles has benefits. Companies can use the technology to conduct surveys and polls, while behavioral scientists can run experiments on digital twins to gather faster insights without risking harm or distress to real participants.
But there’s a huge snag.
Today’s digital twins are just not up to it. New research published in the journal Science Advances found that digital twins seriously misrepresent human behavior.