Issue #07 | June 2025 |5 Next Generation AI Weak Signals
Early signals not late stories
This report highlights another 5 of the most intriguing scan hits from our horizon scanning. All point to emerging AI possibilities, moving beyond today’s LLM-centric hype. Each signal is evaluated through the CIPHER lens, analysing implications, timelines, and confidence. All signals surfaced or gained fresh attention in May and June 2025.
1. AI-Driven DNA Computing with Temporal Control and Quantum-Inspired Mechanisms
Source - Clinical Genetic Research, 3(1)
June 10, 2025
Signal
An extraordinary biocomputing architecture integrating DNA-based logic systems with artificial intelligence feedback loops and quantum-inspired decay mechanisms emerges.
Signal Description
The system is designed to perform biological computing, activating for a limited time and then safely shutting itself down. It uses built-in genetic "off switches," chemical reactions that introduce precise time delays, and a control model inspired by quantum computing. Together, these elements allow biological computers to work inside living organisms in carefully controlled ways.
One potential use is in mental health treatment, where this technology could help regulate brain activity by working alongside devices like deep brain stimulators. The research also explores future possibilities like changing mental states using specially designed genetic instructions delivered through injections or pills.
Overall, this represents a bold shift away from traditional, silicon-based computers toward a new kind of intelligence that is built directly into living biology.
CIPHER Category
Primary – Extremes: Pushes biological computing to unprecedented limits by combining genetic circuits, quantum-inspired dynamics, and AI control in living systems, representing instances of technologies being pushed to new limits that might change their nature
Secondary – Contradictions: Challenges fundamental boundaries between biological and digital computation, creating programmable biological intelligence that can modify cognitive states, demonstrating opposing forces at play simultaneously
Implications
Short-term (2025–2028): Experimental prototypes for bio-logical computing emerge in research laboratories, with early therapeutic applications targeting neuropsychiatric conditions through temporally controlled genetic circuits. AI-guided DNA computing systems begin validation in controlled environments, potentially leading to new biomedical devices operating within living systems.
Mid-term (2030s): Clinical applications for temporally controlled biological interventions become feasible, enabling programmable therapeutics to modify neural activity and cognitive states with unprecedented precision. Hybrid biological-silicon systems emerge as a new computing paradigm, where biological processes perform computation while AI systems provide control and optimisation.
Cultural Shifts: Paradigm shift toward biological hacking and programmable living systems fundamentally alters privacy, identity, and autonomy concepts. Society grapples with the implications of programmable biological intelligence that can modify human cognition, leading to new ethical frameworks and regulatory approaches.
Broader Trend: Convergence of synthetic biology, AI, and quantum-inspired computing signals the emergence of "biological software" that can program living systems from within, potentially leading to entirely new forms of intelligence that exist at the intersection of artificial and biological systems.
Estimated Timeline to Impact
2025–2028: Proof-of-concept systems demonstrate temporal control of genetic circuits with AI feedback. Early therapeutic applications focus on time-limited interventions for neuropsychiatric conditions.
Post-2028: Clinical validation and regulatory approval for biological computing therapeutics could emerge by the early 2030s, assuming successful navigation of safety and ethical considerations. Widespread adoption depends on overcoming technical challenges related to biocontainment and long-term safety.
Signal Type
Technological/Conceptual: A visionary framework signal that integrates multiple cutting-edge domains. It represents a specific technical proposal and a broader conceptual shift toward biological programming.
Confidence Level
Low-Medium: Highly speculative but theoretically grounded in established synthetic biology and AI principles. The technical feasibility is uncertain, but the underlying concepts are based on validated scientific principles from DNA computing, synthetic biology, and AI control systems.
Signal Strength
High: This represents a fundamental departure from silicon-based computing toward programmable biological intelligence. A single theoretical proposal that aligns with broader trends in synthetic biology and biocomputing, suggesting potential for wider adoption if technical barriers are overcome.
Analyst Intuition
This signal represents an ambitious conceptual leap at the frontier of DNA computing and AI-guided biological control. While rooted in validated components such as strand displacement, kill-switch genes, and AI-driven feedback, the integration proposed here is theoretical and lacks empirical demonstration. Including quantum-inspired decay dynamics adds a novel abstraction layer, enhancing temporal control but remaining metaphorical rather than truly quantum.
The most compelling aspect of this signal is its reimagining of biological substrates as programmable, temporally bounded computational media. The framework shifts the locus of computation from silicon into the cellular and molecular realm, mediated by AI logic and potentially interfaced with neurostimulation systems. However, its more radical projections—such as cognitive state modulation via oral nanoparticle-delivered plasmids—are speculative and extrapolative rather than evidence-based.
Overall, the signal suggests the emergence of a hybrid conceptual terrain where computation, biology, and AI are co-evolving. The proposal’s strength lies in its visionary synthesis and modular logic, not in any immediate feasibility. It signals a direction of travel, calling attention to the rising plausibility of biological software architectures while underlining the technical and ethical thresholds yet to be crossed.
2. Wetware Goes Commercial – Biological Computer Built from Living Human Neurons
Source - Forbes
June 04, 2025
Signal
Australian startup Cortical Labs has launched the CL1, the world’s first commercially available biological computer, integrating 800,000 living human neurons cultured on silicon chips.
Signal Description
Priced at $35,000, the system supports direct electrical stimulation and response from the neurons via a proprietary biological operating system (biOS). Unlike simulated neural nets, the CL1 uses evolved biology—neurons that naturally learn, adapt, and self-organise—to process information. The CL1 can be purchased as a lab device or accessed via wetware-as-a-service, suggesting a cloud-era model for living computing substrates.
Building on their 2022 DishBrain proof-of-concept (where neurons learned to play Pong), Cortical Labs’ commercialisation of live-neuron computation shifts AI paradigms from synthetic mimicry to living intelligence. CL1 offers a bidirectional interface with neurons demonstrating spontaneous electrical activity, trainability, and analogue pattern recognition. It signals the start of a new class of hybrid machine—biological-digital intelligences.
CIPHER Category
Primary – Contradictions: The CL1 blurs the boundary between organic life and machine logic. Neurons, long considered biological matter for study, now act as active computing components. This subverts our distinction between “intelligence” as a product of silicon engineering and that which emerges from wet biology. It introduces a machine that is, in part, alive.
Secondary – Inflections: This marks a shift in the history of computing—an inflection point from simulating intelligence toward growing it. The biological substrate operates with radically different energy, adaptability, and processing characteristics. Unlike conventional AI, which struggles with generalisation and energy constraints, biological computers could offer novel pathways to autonomy and resilience, seeded not through code, but cells.
Implications
Short-term (2025–2028):
The CL1 will first impact neuroscience, pharmaceutical research, and bio-AI labs. Its ability to model human neuron behaviour in situ may reshape brain research, cognitive science, and drug testing. Some labs may begin training neuron cultures for specific pattern recognition tasks—blending wetware with classical ML pipelines.Mid-term (2030s):
As tools mature, hybrid systems may emerge where living neurons process analogue input alongside AI models, offloading parts of cognition to biological substrates. AI agents with embedded wetware may self-adapt in ways current systems cannot. At the same time, this will provoke debate: What rights, if any, do “trained” neural cultures possess? Is awareness possible? Do neurons “suffer” in closed feedback loops?Cultural Shifts:
The CL1 signals a new era in which life and computation merge. Researchers will need to confront questions long held at the edge of science fiction: What counts as consciousness, autonomy, or exploitation? Regulation, ethics, and even definitions of intelligence may need rewriting.Broader Trend:
Part of the biological AI wave, alongside organoids, synthetic cells, and biohybrid robots, the CL1 is the first commercial signal that biology may not just inspire computing, but become it.
Estimated Timeline to Impact
Medium (3–5 years): While technically validated, the CL1 introduces a novel substrate, living neurons, which requires new protocols for training, stability, and integration. Early research and pharma applications are underway, but broader adoption will hinge on demonstrating capabilities beyond novelty. Regulatory uncertainty and ethical considerations will likely delay mass deployment. Still, growing interest in neuromorphic and embodied AI systems suggests hybrid biological-digital computing could reach meaningful uptake within 3–5 years, especially in research, edge sensing, and biologically grounded learning environments.
Signal Type
Technological: This is a clear technological signal, a functioning, shippable device with practical applications. CL1 is not theoretical; it is real, tangible, and purchasable, which amplifies its weight.
Scientific: The system sits at the frontier of multiple fields, AI, neuroscience, synthetic biology, and systems engineering. It prompts new scientific exploration around embodied cognition, learning thresholds in living tissue, and the biophysics of intelligence.
Philosophical/Conceptual: The CL1 also functions as a philosophical provocation. It asks us to consider whether intelligence must be synthetic, or whether "natural intelligence in a box" still qualifies as AI. This signal could influence discourse in AI ethics, metaphysics of mind, and the ontological status of hybrid agents.
Confidence Level
Moderate (Trending Up): Multiple independent sources (Forbes, LiveScience, El País, Cortical Labs directly) confirm the CL1's technical reality. The wetware device exists, ships in 2025, and builds on earlier DishBrain results published in Neuron and other peer-reviewed outlets.
However, confidence in its strategic significance is still developing:
Strengths: Commercial availability, proven bidirectional neuron stimulation, strong novelty.
Unknowns: Longevity of neuron cultures under load; reproducibility of results; generalisability beyond neuroscience; limits of self-organisation in biological neurons.
Dependencies: Whether it outperforms silicon in meaningful ways, the development of regulatory frameworks, and acceptance in the broader AI and computing ecosystems.
Overall, the confidence that this is a real development is high. The confidence that it will become a paradigm-shifting foundation for AI remains moderate, but is rising with every new experiment and application.
Signal Strength
Medium (with Asymmetric disruption potential): This is a first-of-its-kind signal. It’s the first commercial computing device made of human neurons, representing a radical shift in AI substrates. That uniqueness alone gives it signal strength.
Analyst Intuition
This may be the start of the next substrate revolution. While neuromorphic chips mimic brains, the CL1 uses elements of the brain itself, raising fundamental questions about performance and meaning. What is intelligence when it lives? This signal doesn’t just shift our technological assumptions; it disturbs our philosophical ones. That makes it potent, precarious, and worth tracking closely.
3. Quantum Biosensing: Diamond Nanoparticles Enable Cell-Level Quantum Monitoring
Source - Industry Tap
May 31, 2025
Signal
A breakthrough in quantum sensing sees diamond nanoparticles embedded into living cells, enabling real-time magnetic field monitoring and challenging assumptions about the role of quantum effects in biological systems.
Signal Description
Researchers at the University of Chicago have successfully integrated nitrogen-vacancy (NV) diamond quantum sensors into living cells by coating them with a siloxane shell inspired by QLED television materials. This protective shell preserves quantum coherence and fluorescence, delivering up to 4× longer coherence times and 1.8× brighter emission, both crucial for sustained quantum readout inside biological systems' noisy, fluctuating environments (PNAS, 2025; University of Chicago News, 2025).
This innovation allows for continuous quantum-level magnetic sensing within live cells, observing biological processes involving quantum effects, such as signal transduction, enzymatic reactions, or protein folding phenomena—long considered too “warm and wet” for quantum coherence to survive (Nature Physics, 2024).
While this technology could eventually enable AI systems to learn from quantum-level biological data, such applications remain speculative and have not yet been demonstrated in published research.
CIPHER Category
Primary – Contradictions: This signal contradicts long-held assumptions that quantum coherence cannot exist or be meaningfully applied in biological contexts. It reveals a counterintuitive fusion of delicate quantum sensing with the harsh conditions of cellular interiors.
Secondary – Extremes: This signal pushes quantum sensing to the biological extreme, operating at the boundary of life and physics. Achieving stable quantum states within living cells marks an extreme extension of technical performance and conceptual reach, placing this firmly at the edge of current capability and understanding.
Implications
Short-term (2025–2028):
Research labs adopt NV-diamond sensors for high-resolution in vivo magnetic mapping, focusing initially on neuroscience, cancer biology, and metabolic regulation.
Diagnostic prototypes begin to leverage quantum-enhanced cellular sensing for detecting early pathophysiological changes, potentially improving early cancer or neurodegenerative disease detection.
Ethics debates emerge around the implications of observing living systems at quantum resolution.
Mid-term (2028–2035):
Clinical applications, such as implantable biosensors for continuous quantum monitoring, are a long-term prospect (likely post-2030) due to regulatory and biocompatibility challenges.
AI systems that model quantum-like biological processes remain a speculative area for future research.
Convergent platforms may arise, combining AI, quantum sensing, and biocompatible computing substrates for next-gen diagnostics and adaptive implants.
Broader Trend:
This signal is part of a larger convergence between quantum sensing, biointerfaces, and AI frameworks. The blurring of categories, machine/living, observer/participant, data/experience, could fundamentally alter the metaphors that underlie computation, biology, and intelligence design
Estimated Timeline to Impact
2025–2028: Proof-of-concept NV-diamond sensors deployed in experimental biology labs. High-resolution biological magnetic field datasets are beginning to train new types of biologically grounded AI models. Limited early use in advanced diagnostics (e.g., oncology, cardiology).
2028–2035: Clinical trials and integration into next-generation medical implants are possible, but widespread clinical use is unlikely before 2030. Theorists explore biologically inspired quantum algorithms based on in-cell sensing results. Systems biology increasingly accounts for potential quantum interactions.
2035+ (Horizon 3): Full deployment of quantum-AI-bio convergence platforms remains speculative and will depend on future breakthroughs in biocompatibility, data interpretation, and regulatory approval. This could lead to the emergence of AI models trained on biological quantum data and potential new computation paradigms, such as AI neural architectures grounded in quantum-informed biological analogues.
Signal Type
Technological / Scientific / Conceptual: This is a cross-domain innovation signal, combining novel materials engineering, quantum sensing, and cellular biology. While it suggests paradigm-level changes, the integration with AI and the emergence of new computation paradigms remain future possibilities.
Confidence Level
Medium–High
Experimental credibility: Peer-reviewed publication (PNAS), with verified performance metrics and known collaborators from quantum materials science and cell biology.
Technical novelty: Real-world coherence and signal amplification metrics exceed previous in-cell sensors.
Early adoption signs: Interest from synthetic biology, neuroscience, and bio-AI hybrid research communities.
Remaining uncertainty: Long-term biocompatibility, robustness across diverse tissue types, and AI systems' interpretability of biological quantum data remain open challenges.
Signal Strength
High: The underlying NV-diamond platform is compact and increasingly manufacturable. Ties into parallel advances in quantum computing, biosensing, AI-model feedback, and biointerfaces.
Analyst Intuition
The core technical claims are peer-reviewed and verified. Speculative elements, such as AI integration and quantum cognition, are plausible but not yet realised.
That said, this signal could point toward a future in which AI listens to biology at the molecular or chemical level and at the quantum level, where coherence, spin, and field variation may become fundamental variables in intelligent interpretation.
If quantum effects are discovered to underlie key biological functions, such as enzymatic selectivity, neural signal timing, or even cognition, then the entire scaffolding of biologically aligned AI could shift. In this scenario, bio-quantum sensors may function not just as passive data collectors but as gateways into a new form of natural intelligence that AI may learn from or emulate.
It may still be speculative to claim this unlocks quantum cognition, but it’s no longer speculative to say the tools are finally entering the cell. This is a new possibility space.
4. “Hair-Follicle” Brain–Computer Interfaces Blur Human-Tech Boundaries
Source - PNAS
Apr 7, 2025
Signal
Engineers have unveiled a nearly invisible brain–computer interface (BCI) sensor that nestles between hair follicles. Flexible polymer microneedles painlessly penetrate just beneath the skin, capturing high-fidelity neural signals even as the wearer moves, enabling continuous, real-world brain–machine interaction without surgery or bulky headgear.
Signal Description
This breakthrough BCI leverages microscale, flexible electrodes that slip between hair follicles and under the skin, picking up brain electrical activity with unprecedented comfort and accuracy. In peer-reviewed trials, users controlled augmented reality (AR) interfaces by thought alone for up to 12 hours, while walking, with 96% accuracy.
Unlike traditional BCIs, which require invasive surgery or cumbersome scalp electrodes, this “hair-follicle” device is nearly imperceptible and works seamlessly in daily life.
The significant advance eliminates the longstanding trade-off between comfort and signal quality, making continuous, mobile mind–machine interaction practical.
This opens the door to everyday applications: assistive tech for paralysis, thought-controlled tools for workers, and real-time AI copilots that interface directly with the brain.
The technology challenges the assumption that human and machine intelligence are separate realms, potentially weaving them together in real time. As technical barriers fall, urgent questions about mental privacy, neurodata rights, and societal norms around “augmented” cognition move to the forefront.
CIPHER Category
Primary – Inflections: This signal marks a clear inflection point: it pivots from stationary, clinical, or invasive BCIs to seamless, mobile, everyday neural interfacing.
Secondary – Contradictions: The device delivers both high-fidelity neural data and user comfort, previously viewed as mutually exclusive. This resolves a longstanding tension in the field, flipping the status quo for BCI design.
Implications
Short-term (2025–2028): This technology might enable new assistive devices for home use by patients with paralysis or neurological disorders, dramatically improving independence and quality of life. Early adoption is likely in sectors that require hands-free control, such as defence, industrial safety, and augmented or virtual reality environments, where the ability to interact with systems by thought alone could enhance efficiency and safety. Additionally, there could be accelerated development of AI copilots and digital assistants that interface directly with neural signals, allowing for more intuitive and seamless human–machine collaboration.
Mid-term (2030s): Widespread adoption of “invisible” brain–computer interfaces could enable seamless, direct interaction between human minds and digital systems, fundamentally transforming productivity, communication, and entertainment. As these interfaces become mainstream, artificial intelligence will act as a real-time cognitive partner, interpreting neural signals, anticipating user needs, and augmenting decision-making. At the same time, AI-powered assistants respond instantly to thoughts, blurring the line between human and machine intelligence. This integration will prompt new policy debates and regulatory frameworks around neurodata privacy, workplace augmentation, and mental autonomy, as society confronts complex questions of consent, surveillance, and the boundaries of personal freedom.
Cultural Shifts: Culturally, society will experience significant shifts in how “natural” cognition and interaction are perceived. As AI becomes embedded in devices and the fabric of human experience, traditional notions of self, agency, and intelligence will be challenged. The fusion of AI and BCI technologies will drive new practices and values, with some individuals embracing cognitive enhancement while others raise concerns about equity and authenticity.
Broader Trend: This signal aligns with a broader movement toward seamless human–AI integration, challenging biological and artificial intelligence boundaries.
Estimated Timeline to Impact
2025–2028: Given the device’s demonstrated performance and comfort in peer-reviewed studies, first commercial or clinical deployments will likely accelerate within a few years as technical refinements continue.
Signal Type
Technological, Social/Cultural: This is a technological breakthrough with profound social and cultural ramifications, as it enables new forms of interaction and raises urgent ethical questions.
Confidence Level
High: Feasibility, comfort, and accuracy have been demonstrated in rigorous, peer-reviewed research, and the pace of BCI innovation suggests rapid translation to real-world applications.
Signal Strength
Strong/Accelerating: This signal is moving from laboratory demonstration to early adoption, potentially catalysing a wave of new applications and societal debates.
Analyst Intuition
This signal marks a genuine transition of BCIs from experimental curiosities to practical reality. The seamless, mobile, and nearly invisible design of these new BCIs removes the last significant barrier to adoption. At the same time, the integration of AI could transform them from passive data collectors into adaptive, real-time cognitive partners capable of decoding, anticipating, and responding to user intent with remarkable accuracy.
As BCIs and AI systems become tightly interwoven, the boundaries between human and machine intelligence blur, ushering in an era where thought-driven interaction, context-aware assistance, and even collective cognition become possible.
This convergence is likely to trigger a cascade of technical innovation, new social practices, and urgent ethical debates—especially around privacy, agency, and autonomy as society grapples with the implications of direct brain–AI connectivity.
5. Energy Aware Development of Neuromorphic Implantables: From Metrics to Action
Source - arXiv:2506.09599
June 11, 2025
Signal
A new framework to evaluate how energy-efficient neuromorphic implants are, highlighting gaps in current tools and proposing better, battery-aware metrics to guide the development of ultra-low-power brain-inspired devices.
Signal Description
This explores how to measure and improve the energy efficiency of brain-inspired computer systems, especially tiny devices like neural implants that go inside the body. These systems use Spiking Neural Networks (SNNs), a brain-inspired computing model that processes information in energy-efficient "spikes," similar to how real neurons behave.
The study looks at 13 different ways to measure energy use in these systems and finds that many are either too hard to use or not helpful enough for engineers building real devices. Some are easy to apply but don’t give accurate results; others are very precise but too complex or slow to be useful early in the design process.
The researchers call for better tools that are easy to use and provide practical insights. They suggest new types of energy metrics, including ones that consider battery life, and recommend ways to help developers make better trade-offs between power use and performance, especially for implants that need to last for years without needing a new battery.
CIPHER Category
Primary – Practices: Establishes new methodological approaches for energy-efficient neuromorphic system development, representing emerging practices that are becoming more pronounced in the field.
Secondary – Hacks: Addresses the creative challenge of measuring and optimising energy in unconventional computing paradigms, representing inventive approaches to assessment and optimisation.
Implications
Short-term (2025–2027): New energy assessment tools emerge for neuromorphic systems, enabling more systematic development of energy-efficient brain-inspired computing. Medical device companies adopt these metrics for developing next-generation neural implants with extended battery life.
Mid-term (2030s): Widespread deployment of long-lasting neuromorphic implants for brain-computer interfaces, neural prosthetics, and continuous health monitoring. Energy optimisation becomes a key differentiator in neuromorphic system design.
Cultural Shifts: The medical paradigm is shifting toward permanent, low-maintenance neural implants that can operate for years without battery replacement, fundamentally changing treatment approaches for neurological conditions.
Broader Trend: Energy efficiency becomes a primary design constraint for neuromorphic systems, driving innovation in ultra-low-power brain-inspired computing and enabling new classes of autonomous, implantable intelligent devices.
Estimated Timeline to Impact
2025–2027: Adoption of energy assessment frameworks by neuromorphic researchers and implantable device developers. Integration with existing neuromorphic development tools.
Post-2027: Commercial deployment of next-generation neuromorphic implants with significantly extended operational lifespans, reducing the need for surgical battery replacements.
Signal Type
Methodological/Technical: A research signal focused on measurement and optimisation methodologies for emerging neuromorphic technologies.
Confidence Level
Medium-High: Addresses an identified need with practical solutions, building on established neuromorphic computing principles with clear applications to medical devices.
Signal Strength
Medium: Important methodological advancement that enables better development of neuromorphic systems, but represents incremental rather than revolutionary progress.
Analyst Intuition
While this may not appear ground-breaking at first glance, it represents an important step in overcoming a key limitation in neuromorphic computing: how to measure and optimise energy use practically.
Focusing on actionable, battery-aware metrics, the research signals a quiet but meaningful shift from theoretical exploration to real-world application, especially in medical contexts where energy efficiency is not optional but fundamental. This lays the groundwork for viable neural implants and, more broadly, for deeper convergence between human bodies, intelligent machines, and adaptive AI systems.