Clinical BCI Superpower
+Brain-computer interfaces will demonstrate major improvements to the quality of life and capabilities of clinical patients — some of which will exceed the capabilities of healthy individuals.
+The BCI App Store
+We believe a major catalyst will occur when BCIs transition from vertically integrated medical products into open platforms. A standardized software layer that allows third-party developers to build applications on top of approved BCI hardware could dramatically increase the utility of neural interfaces. Just as smartphones became more valuable through app ecosystems, BCIs could unlock a wave of innovation once many developers can deploy neural augmentations through scalable software deployments.
+
Neural Distillation
+One potential breakthrough is the emergence of methods that directly align AI systems with human neural activity. If neural recordings can help models learn more efficiently, or think in ways that better reflect human cognition, neural data could become a foundational resource for AI development. A specific inflection point would be 100,000,000 hours of human neural data recorded across cognitive tasks and recording device types.
+The Neuromorphic Energy Pivot
+A second possibility is that energy constraints push the AI industry toward biologically inspired hardware and algorithms. If brain-inspired systems achieve orders-of-magnitude improvements in efficiency vs current hardware or software designs, neuroscience could become a core driver of future AI progress.
+
Memory Retrieval in Simulation
+The most catalytic milestone may be surprisingly simple. If a reconstructed mouse brain can be simulated and reliably reproduce a learned behavior or memory from its biological counterpart in a virtual environment, whole-organism emulation moves from speculation to demonstration. Such a result would establish a concrete benchmark for the field and fundamentally change how researchers, funders, and policymakers view the possibility and impact of emulation.
+Clinical BCI superpower
-BCIs will demonstrate major improvements to the quality of life and capabilities of clinical patients, some of which will exceed the capabilities of healthy individuals.
-The BCI app store
-We believe a major shift will occur when BCIs move from vertically integrated medical products to open platforms. A standardized software layer that lets third-party developers build applications on top of approved BCI hardware could increase the utility of neural interfaces by orders of magnitude. Smartphones became more valuable once app ecosystems formed on top of standard hardware; BCIs could follow the same path once many developers can deploy neural augmentations through scalable software.
-Neural distillation
-One potential breakthrough is the emergence of methods that directly align AI systems with human neural activity. If neural recordings help models learn more efficiently, or reason in ways that better reflect human cognition, neural data could become a foundational resource for AI development.
-The neuromorphic energy pivot
-A second possibility is that energy constraints push the AI industry toward biologically inspired hardware and algorithms. If brain-inspired systems achieve orders-of-magnitude improvements in efficiency, neuroscience could become a core driver of future AI progress.
-Memory retrieval in simulation
-The most catalytic milestone may be a simple one. If a reconstructed mouse brain can be simulated and reliably reproduce a learned behavior or memory from its biological counterpart in a virtual environment, whole-organism emulation moves from speculation to demonstration. That result would establish a concrete benchmark for the field and change how researchers, funders, and policymakers view the possibility and impact of emulation.
-![\"Neurotechnology:](\"/images/blog/neurotechnology-hero.webp\")
At PL R&D, we focus on fields that have the potential to unlock transformative new capabilities for humanity. The Neurotechnology Focus Area aims to accelerate computing breakthroughs across neuroscience and neurotechnology.
\nAdvances in neuroscience, brain-computer interfaces (BCIs), biologically inspired AI, and whole-organism emulation are accelerating rapidly. Together, they open a path toward understanding intelligence, restoring and expanding human capabilities, and building new forms of human-machine interaction. The field today remains fragmented: technical, regulatory, infrastructure, and capital bottlenecks continue to slow progress. We aim to change that.
\nPL Neuro's mission is to help secure a future of human flourishing by bridging minds and machines in ways that expand human potential while preserving autonomy, dignity, and individual agency.
\nWe focus on three opportunity spaces that could reshape both neuroscience and computing over the coming decade:
\nThese areas form a stack. Advances in neuroscience generate new data and understanding. Those insights enable more capable AI systems and neurotechnologies. Progress across the stack opens new possibilities for augmenting human cognition, and each layer feeds the next: better recording produces better data, better data trains better models, and better models improve both the interfaces we build and the emulations we can run.
\n![\"Neural](\"/images/opportunity-spaces/neural-augmentation.jpg\")
Neural augmentation focuses on building high-bandwidth, bidirectional interfaces between brains and computers.
\nNear-term applications are therapeutic: restoring communication, movement, and independence for people living with paralysis or neurological conditions. Longer-term, these same technologies may enable new forms of interaction, communication, and cognition.
\nBCIs have already demonstrated life-changing benefits in clinical settings. The next challenge is moving from isolated medical devices to scalable platforms that support broad innovation.
\nAreas we find particularly promising include:
\nHigher data-rate interfaces are advancing toward the clinic: Paradromics received FDA approval for the Connect-One clinical study of its Connexus® brain-computer interface, designed to restore speech and computer control for people with severe motor impairment. Less invasive devices are clearing regulatory review in parallel: Precision Neuroscience received FDA 510(k) clearance for its Layer 7 Cortical Interface, a high-resolution cortical electrode array roughly one-fifth the thickness of a human hair. For a fuller picture of where the clinical BCI market is heading, see PL's strategic vision for the future of brain–computer interfaces and its analysis of the path to clinical revenue.
\nClinical BCI superpower
\nBCIs will demonstrate major improvements to the quality of life and capabilities of clinical patients, some of which will exceed the capabilities of healthy individuals.
\nThe BCI app store
\nWe believe a major shift will occur when BCIs move from vertically integrated medical products to open platforms. A standardized software layer that lets third-party developers build applications on top of approved BCI hardware could increase the utility of neural interfaces by orders of magnitude. Smartphones became more valuable once app ecosystems formed on top of standard hardware; BCIs could follow the same path once many developers can deploy neural augmentations through scalable software.
\n![\"Biologically](\"/images/opportunity-spaces/biologically-inspired-intelligence.jpg\")
NeuroAI uses insights from biological intelligence to build more capable, efficient, and accessible AI systems. It treats the brain not only as an object of study but as a source of architectural, representational, and algorithmic ideas.
\nModern AI has achieved remarkable capabilities, often at enormous computational and energy cost. Brains show that intelligence can emerge from systems that are dramatically more efficient than today's machine learning architectures. Understanding how biological intelligence works may help unlock the next generation of AI.
\nAreas we find particularly promising include:
\nLarge-scale neural data is reaching new resolution: the MICrONS project mapped a cubic millimeter of mouse visual cortex, resolving roughly 200,000 cells and 523 million synapses in a single functional connectome. Neural foundation models are learning to predict brain activity directly: Meta's TRIBE v2 predicts human brain responses to naturalistic video, audio, and text, trained on more than 1,000 hours of fMRI across 720 subjects. These two threads point in the same direction: more data at higher resolution, feeding models that treat neural activity as a first-class training signal.
\nNeural distillation
\nOne potential breakthrough is the emergence of methods that directly align AI systems with human neural activity. If neural recordings help models learn more efficiently, or reason in ways that better reflect human cognition, neural data could become a foundational resource for AI development.
\nThe neuromorphic energy pivot
\nA second possibility is that energy constraints push the AI industry toward biologically inspired hardware and algorithms. If brain-inspired systems achieve orders-of-magnitude improvements in efficiency, neuroscience could become a core driver of future AI progress.
\n![\"Whole-Organism](\"/images/opportunity-spaces/whole-organism-emulation.jpg\")
Whole-organism emulation seeks to build computational models that reproduce the behavior of biological organisms using detailed neural and biological data. Often discussed as science fiction, it is increasingly an engineering challenge shaped by advances in connectomics, imaging, simulation, and neuroscience.
\nA working emulation system would give researchers a new tool for understanding intelligence, learning, memory, and behavior. It could also accelerate neuroscience by enabling experiments that are difficult or impossible to run in living systems.
\nAreas we find particularly promising include:
\nHigh-throughput connectomics is getting cheaper and faster: a Nature study on light-microscopy-based dense connectomic reconstruction of mammalian brain tissue shows a route to mapping wiring and molecular identity together. Reconstruction pipelines are removing the manual-correction bottleneck: Janelia's PATHFINDER uses AI to segment volumetric image data and assemble neuron reconstructions with far less human proofreading. And modeling work is beginning to translate static structure into dynamics: PL-supported research on compiling molecular ultrastructure into neural dynamics proposes mapping ultrastructural data to the physiological parameters that govern neural activity. PL has also laid out how a complete human connectome could be obtained at synaptic resolution within the next decade.
\nMemory retrieval in simulation
\nThe most catalytic milestone may be a simple one. If a reconstructed mouse brain can be simulated and reliably reproduce a learned behavior or memory from its biological counterpart in a virtual environment, whole-organism emulation moves from speculation to demonstration. That result would establish a concrete benchmark for the field and change how researchers, funders, and policymakers view the possibility and impact of emulation.
\nField-building needs shared targets. We are organizing PL Neuro's work around three measurable milestones that mark real progress across the stack:
\nReaching these milestones would move neurotechnology from isolated demonstrations to a compounding platform: bidirectional implants plus neural foundation models make closed-loop control possible, which opens a new therapeutic category and, eventually, true neural augmentation, along with AI systems that think like humans rather than only behave like them.
\nBreakthroughs do not come from technology alone. They require healthy ecosystems that connect research institutions, domain experts, capital allocators, startup founders, and more.
\nToday, neurotechnology is fragmented across disciplines, institutions, and stakeholder groups. Researchers, founders, funders, policymakers, and engineers often work in separate communities despite pursuing related goals. Building the field itself is therefore a critical leverage point.
\nOur field-building strategy centers on:
\nA stronger ecosystem increases the likelihood that advances in BCI, NeuroAI, and emulation reinforce one another rather than developing in isolation.
\nThe coming decade could determine whether neurotechnology remains a niche scientific endeavor or becomes one of the defining technological frontiers of the century.
\nWe believe the field is approaching several important inflection points. Reaching them will require coordinated effort across research, entrepreneurship, policy, infrastructure, and capital. PL R&D exists to help accelerate that coordination.
\nWe are actively building relationships with researchers, founders, funders, policymakers, and technologists working across neurotechnology, NeuroAI, connectomics, and related fields.
\nIf you are working on neural interfaces, neural foundation models, connectomics, emulation, or the infrastructure and governance around them, we would love to hear from you: research@protocol.ai.
\nWe invite you to explore the projects and publications already contributing to this vision. Follow PL R&D and Protocol Labs for future publications, field maps, convenings, and opportunities to participate in the ecosystem as it grows.
\nThe future of neurotechnology remains unwritten. That is precisely what makes it worth building.
\nJoin us.
\n", - "unlisted": true + "html": "At PL R&D, we focus on fields that have the potential to unlock transformative new capabilities for humanity. The PL Neuro Focus Area aims to accelerate computing breakthroughs within the fields of neuroscience and neurotechnology.
\nAdvances in neuroscience, brain-computer interfaces (BCIs), biologically inspired AI, and whole-organism emulation are accelerating rapidly. Together, they unlock a future where we can better understand intelligence, restore and expand human capabilities, and build entirely new forms of human-machine interaction. However, the field today remains fragmented: key technical, regulatory, infrastructure, and capital bottlenecks continue to slow progress. We aim to change that.
\nPL Neuro's mission is to help secure a future of human flourishing by bridging minds and machines in ways that expand human potential while preserving autonomy, dignity, and individual agency.
\nWe focus on three opportunity spaces that could reshape both neuroscience and computing over the coming decade:
\nTogether, these areas form a loop: advances in neuroscience generate new data and understanding; those insights enable more capable AI systems and neurotechnologies; and progress across both creates entirely new possibilities for augmenting human cognition.
\nNeural augmentation focuses on building high-bandwidth, bidirectional interfaces between brains and computers.
\nNear-term applications are therapeutic: restoring communication, movement, and independence for people living with paralysis or neurological conditions. Longer-term, these same technologies may enable entirely new forms of interaction, communication, and cognition.
\nBCIs have already demonstrated life-changing benefits in clinical settings. The next challenge is moving from isolated medical devices to scalable platforms that support broad innovation.
\nKey areas of interest include:
\nClinical BCI Superpower
\nBrain-computer interfaces will demonstrate major improvements to the quality of life and capabilities of clinical patients — some of which will exceed the capabilities of healthy individuals.
\nThe BCI App Store
\nWe believe a major catalyst will occur when BCIs transition from vertically integrated medical products into open platforms. A standardized software layer that allows third-party developers to build applications on top of approved BCI hardware could dramatically increase the utility of neural interfaces. Just as smartphones became more valuable through app ecosystems, BCIs could unlock a wave of innovation once many developers can deploy neural augmentations through scalable software deployments.
\nNeuroAI seeks to use insights from biological intelligence to build more capable, efficient, and accessible AI systems.
\nRather than treating the brain as merely an object of study, NeuroAI treats it as a source of architectural, representational, and algorithmic inspiration.
\nModern AI carries enormous computational and energy cost.
\nBrains demonstrate that intelligence can emerge from systems that are dramatically more efficient than today's machine learning architectures. Understanding how biological intelligence works may help unlock the next generation of AI.
\nAreas we find particularly promising include:
\nNeural Distillation
\nOne potential breakthrough is the emergence of methods that directly align AI systems with human neural activity. If neural recordings can help models learn more efficiently, or think in ways that better reflect human cognition, neural data could become a foundational resource for AI development. A specific inflection point would be 100,000,000 hours of human neural data recorded across cognitive tasks and recording device types.
\nThe Neuromorphic Energy Pivot
\nA second possibility is that energy constraints push the AI industry toward biologically inspired hardware and algorithms. If brain-inspired systems achieve orders-of-magnitude improvements in efficiency vs current hardware or software designs, neuroscience could become a core driver of future AI progress.
\nWhole organism emulation seeks to create computational models that reproduce the behavior of biological organisms using detailed neural and biological data.
\nWhile often discussed as science fiction, the field is increasingly becoming an engineering challenge shaped by advances in connectomics, imaging, simulation, and neuroscience.
\nA successful emulation system would provide an unprecedented tool for understanding intelligence, learning, memory, and behavior.
\nIt could also dramatically accelerate neuroscience by enabling experiments that are difficult (or impossible) to perform in living systems.
\nPromising areas include:
\nThree major constraints sit between a fixed brain and a running simulation: how much tissue can be imaged, how much human labor reconstruction takes, and whether structural data is sufficient to specify function. Significant progress has been made against all three constraints in the past year.
\nOn imaging, moving off electron microscopy changes the cost curve. A team at the Institute of Science and Technology Austria published a light-microscopy pipeline that expands tissue roughly 16-fold. Light microscopes are cheaper and far more widely installed than electron microscopes, and the method leaves room for molecular labels that electron microscopy cannot read.
\nOn reconstruction, the binding cost at mouse scale is human proofreading hours. Janelia's PATHFINDER pipeline reports 94.2% normalized expected run length for exhaustive axon reconstruction and an 84-fold reduction in projected proofreading cost against prior work.
\nOn sufficiency, the field's hardest open question is whether a connectome constrains dynamics tightly enough to simulate. A recent preprint proposes an ultrastructure-to-dynamics compiler. If mappings like this hold, molecular annotation plus a connectome becomes enough to parameterize a simulation.
\nMemory Retrieval in Simulation
\nThe most catalytic milestone may be surprisingly simple. If a reconstructed mouse brain can be simulated and reliably reproduce a learned behavior or memory from its biological counterpart in a virtual environment, whole-organism emulation moves from speculation to demonstration. Such a result would establish a concrete benchmark for the field and fundamentally change how researchers, funders, and policymakers view the possibility and impact of emulation.
\nBreakthroughs do not emerge from technology alone. They require healthy ecosystems that connect research institutions, domain experts, capital allocators, startup founders, and more.
\nToday, neurotechnology faces fragmentation across disciplines, institutions, and stakeholder groups. Researchers, founders, funders, policymakers, and engineers often operate in separate communities despite working toward related goals.
\nBuilding the field itself is therefore a critical leverage point. Our field-building strategy centers on:
\nA stronger ecosystem increases the likelihood that advances in BCI, NeuroAI, and emulation reinforce one another rather than developing in isolation.
\nThe coming decade could determine whether neurotechnology remains a niche scientific endeavor or becomes one of the defining technological frontiers of the century. We believe the field is approaching several important inflection points:
\nReaching them will require coordinated effort across research, entrepreneurship, policy, infrastructure, and capital. PL R&D exists to help accelerate that coordination.
\nWe're actively building relationships with researchers, founders, funders, policymakers, and technologists working across neurotechnology, NeuroAI, connectomics, and related fields.
\nIf you're interested in contributing to the future of neurotechnology, we'd love to hear from you: research@protocol.ai.
\nFollow PL R&D and Protocol Labs for future publications, field maps, convenings, and opportunities to participate in the ecosystem as it continues to grow.
\nThis post is for informational purposes only. It is not an offer, solicitation, or recommendation of any security or investment product, and nothing here is a commitment or guarantee of any future performance or any outcome.
\n", + "unlisted": false }, { "slug": "better-economies-governance-systems",