Educational Technology and Change Journal

By Jim Shimabukuro (assisted by Claude)
Editor

These five developments—neuromorphic computing, hybrid quantum-AI systems, AI protein engineering, retrieval-augmented generation, and edge AI semiconductors—share a common theme: they represent architectural innovations and practical deployments rather than incremental improvements in model size or capability. While the media focuses on the latest chatbot features or generative AI controversies, these quieter developments are building the infrastructure and capabilities that will define AI’s next decade. Their impact may only become apparent in retrospect, but the groundwork being laid in late 2025 positions them to transform industries throughout 2026-2028 and beyond.

1. Neuromorphic Computing: Intel’s Hala Point and the Brain-Inspired Revolution

While generative AI models continue to dominate headlines with their impressive but energy-hungry capabilities, a quieter revolution is unfolding in neuromorphic computing—technology that mimics the human brain’s architecture to deliver unprecedented efficiency. Intel has built the world’s largest neuromorphic system called Hala Point, which contains 1.15 billion neurons and packages 1,152 Loihi 2 processors in a data center chassis the size of a microwave oven, consuming a maximum of 2,600 watts of power. This system, initially deployed at Sandia National Laboratories, represents a fundamental departure from conventional computing architectures.

The significance of neuromorphic computing lies in its approach to processing information. Unlike traditional digital computers that rely on synchronized clock cycles and separate memory and processing units, neuromorphic chips process information through spiking neural networks that communicate asynchronously, much like biological neurons. Hala Point can support up to 20 quadrillion operations per second with an efficiency exceeding 15 trillion 8-bit operations per second per watt when executing conventional deep neural networks, rivaling and exceeding levels achieved by GPU and CPU architectures. This efficiency advantage becomes critical as AI models grow increasingly complex and energy-intensive.

Mike Davies, director of the Neuromorphic Computing Lab at Intel Labs, stated that the computing cost of today’s AI models is rising at unsustainable rates, and the industry needs fundamentally new approaches capable of scaling. The environmental and financial costs of training large language models have become untenable; a recent report revealed that AI queries can use ten times as much energy as standard searches, and sometimes dozens of times more. Neuromorphic systems offer a path forward that doesn’t require building new power plants next to data centers.

Intel’s Loihi 2 is a second-generation neuromorphic processor with one million neurons and real-time on-chip learning capabilities, while IBM has developed the NorthPole architecture that tightly integrates computation and memory, reducing the von Neumann bottleneck. These systems aren’t just theoretical—they’re being tested for real-world applications. At Sandia National Laboratories, researchers plan to use Hala Point for advanced brain-scale computing research, particularly for optimization problems that involve searching for and planning the shortest path in a map, where the system has achieved speedups of as much as fifty times with one hundred times savings in energy.

The ecosystem supporting neuromorphic computing is expanding rapidly. Intel has established the Intel Neuromorphic Research Community with more than two hundred members, including leading academic groups, government labs, research institutions, and companies worldwide. Open-source software frameworks like Intel Lava and Norse (a PyTorch-based library supporting spiking neural networks) allow AI developers to move between conventional and neuromorphic paradigms.

The commercial trajectory is accelerating. In early 2025, BrainChip Holdings partnered with Raytheon under a U.S. Air Force Research Laboratory SBIR Phase II contract worth $1.8 million, demonstrating how neuromorphic chips are transitioning from laboratory experiments to mission-critical defense applications. Additionally, mid-2025 saw the introduction of a GHz-scale photonic neuromorphic chip built using silicon photonics that can process event-based spikes at light speed while consuming a fraction of the power of electronic systems.

Looking ahead to 2026 and beyond, neuromorphic computing is positioned to transform edge AI applications—robotics, autonomous vehicles, smart sensors, and wearable devices that require real-time processing with minimal power consumption. In healthcare, neuromorphic systems are already powering wearable neurotechnology, enabling early seizure detection and prosthetics. The technology’s ability to process sparse, event-driven data efficiently makes it ideal for applications where traditional AI approaches are impractical due to power or latency constraints.

What makes this development particularly significant is its potential to democratize AI deployment. Current AI infrastructure favors large technology companies with the capital to build massive data centers. Neuromorphic computing could enable sophisticated AI capabilities to run on smaller devices with minimal energy requirements, shifting the competitive landscape and enabling new categories of intelligent applications that were previously impossible.

2. Hybrid Quantum-AI Systems: The Convergence Beyond the Hype

While quantum computing has oscillated between breathless enthusiasm and skepticism over the past decade, a more pragmatic and potentially transformative development is emerging quietly: the integration of quantum computing with classical AI systems into hybrid architectures. Rather than waiting for fault-tolerant universal quantum computers that may be decades away, researchers and companies are building systems that leverage quantum processors for specific computational bottlenecks while classical AI handles the rest.

In August 2025, IBM and AMD announced plans to develop next-generation computing architectures based on quantum-centric supercomputing, leveraging IBM’s leadership in developing quantum computers and software with AMD’s leadership in high-performance computing and AI accelerators. This partnership signals a shift from purely quantum or purely classical approaches toward hybrid systems that combine the strengths of both paradigms. The teams are planning an initial demonstration to show how IBM quantum computers can work in tandem with AMD technologies to deploy hybrid quantum-classical workflows.

The rationale for hybrid systems is pragmatic. In March 2025, Quantum Machines and NVIDIA debuted DGX Quantum, a tightly integrated system linking a quantum controller to a classical AI superchip with only microseconds of latency, with early trials at research labs showing this setup could perform real-time quantum error correction and AI-assisted operations. These systems address quantum computing’s fundamental challenge: qubits are extremely fragile and error-prone, requiring constant correction and calibration. Classical AI can manage these tasks in real-time, making quantum operations more stable and practical.

Industry leaders predict that hybrid quantum-AI systems will impact fields like optimization, drug discovery, and climate modeling, while AI-assisted quantum error mitigation will significantly enhance the reliability and scalability of quantum technologies. The synergy works both ways: quantum computing can accelerate certain AI tasks that are computationally intractable on classical hardware, while AI can optimize quantum operations and error correction.

Amazon has announced a more comprehensive integration between its Braket quantum cloud and NVIDIA’s CUDA-Q Quantum tools, facilitating workflows that seamlessly blend quantum processors with GPU-accelerated high-performance computing clusters. This infrastructure development is critical—it creates the pathways for researchers and developers to experiment with hybrid algorithms without building their own quantum computers. D-Wave Systems released its Advantage2 system in 2025, packing over 4,400 qubits tailored for industrial-scale optimization, with early results showing stronger performance in areas such as scheduling and machine learning supported by hybrid solvers that process millions of variables.

What distinguishes this development from the quantum hype cycles of years past is its focus on near-term practical applications rather than waiting for universal quantum computers. Enrique Lizaso Olmos, CEO of Multiverse Computing, stated that AI and quantum computing solve two completely different issues—while AI can enhance productivity and act as a force multiplier for human teams, quantum computing represents a complete paradigm shift in compute power, revolutionizing our ability to solve problems that would otherwise be impossible with classical computing.

At IBM’s appearance at SXSW 2025, CEO Krishna argued that quantum can help unlock how nature behaves, while AI learns from what we know, suggesting the two technologies are complementary rather than competitive. Krishna listed areas where he believes quantum computers will make a meaningful impact before the decade is out, including carbon sequestration, materials discovery, pricing models, nutrition, and business optimization.

The timeline for impact appears more concrete than previous quantum predictions. In March 2025, research efforts reported achieving real-time error correction and extended qubit lifetimes, with the once-distant goal of a stable, large-scale quantum computer visibly on the horizon. Companies aren’t waiting for perfection. Pharmaceutical firms are already using hybrid quantum-AI systems for molecular simulations, financial institutions are testing them for portfolio optimization, and logistics companies are exploring their use for route planning.

The launch window for commercially viable hybrid quantum-AI services appears to be 2026-2027, with early adopters in finance, pharmaceuticals, and materials science expected to see practical benefits. This represents a significant acceleration from the “quantum is twenty years away” narrative that has persisted for decades. The key insight is that hybrid systems don’t need perfect quantum computers to deliver value—they need quantum processors good enough to solve specific subproblems faster than classical alternatives, and that threshold is approaching rapidly.

3. AI-Powered Protein Engineering: Beyond AlphaFold to Drug Discovery at Scale

The 2024 Nobel Prize in Chemistry awarded to David Baker, Demis Hassabis, and John Jumper for computational protein design and structure prediction marked a mainstream recognition of AI’s impact on biology. However, the most significant developments are occurring not in protein structure prediction itself—that problem is largely solved—but in leveraging these capabilities for practical drug discovery and protein engineering at unprecedented speed and scale.

There have been more than 500 FDA submissions with AI components from 2016 to 2023, and heading into 2025, the growth trend for pharmaceutical R&D budgets will continue and only gain speed. Enes Hosgor, CEO and founder of clinical AI validation firm Gesund.ai, noted that with an incoming Trump administration expected to stimulate M&A activity, there will be even more focus on spending in fundamental science to control breakthrough AI technologies.

The progression from AlphaFold’s structure prediction to functional protein design represents a quantum leap in capability. Researchers from the University of Sheffield and AstraZeneca developed a machine learning framework called MapDiff that outperformed state-of-the-art AI in making successful predictions for inverse protein folding in simulated tests. Peizhen Bai, Senior Machine Learning Scientist at AstraZeneca who developed the AI, explained that MapDiff helps design protein sequences that are more likely to fold into desired 3D structures—a key step towards advancing next-generation therapeutics.

This matters because designing proteins is fundamentally different from predicting their structures. In drug development, researchers need to create entirely novel proteins with specific functions—antibodies that bind to disease targets, enzymes that catalyze useful reactions, or therapeutic proteins with enhanced stability. Companies such as Cradle have been using protein language models to design better enzymes, receptor-binding proteins, and antibodies, with applications across the entire biotechnology spectrum from industrial and food biotech to biopharma, while Absci is transforming the biologics discovery pipeline by enabling de novo antibody design.

The economic implications are staggering. Traditional drug development costs billions of dollars and takes over a decade from discovery to market. Estimations of overall expenses for research and development prior to product launch range from $161 million to $4.54 billion, with only a small fraction of drug candidates making it to clinical trials and many failing as late as Phase 3, resulting in an overall success rate of about 10-20% in clinical drug development. AI-powered protein engineering promises to dramatically compress these timelines and costs by predicting which molecules will succeed before expensive laboratory work and clinical trials begin.

Google DeepMind colleagues are using AlphaFold to predict the structure and interactions of all of life’s molecules, which has the potential to transform our understanding of the biological world and drug discovery. The scale is breathtaking—moving from analyzing individual proteins to mapping molecular interactions across entire biological systems. This systems-level understanding enables researchers to design drugs that account for complex interactions and side effects from the start.

In February 2025, NVIDIA released GenMol, a foundational model for molecular generation offering a versatile molecular generative framework based on discrete diffusion and non-autoregressive decoding designed to streamline drug discovery from molecule design to lead optimization. These tools are becoming increasingly accessible, democratizing capabilities that were previously confined to well-funded pharmaceutical giants and academic institutions with specialized expertise.

The current state of deployment shows AI protein engineering is moving from research to production. Multiple biotechnology companies have announced partnerships with AI firms to develop therapeutic candidates. In October 2025, AstraZeneca partnered with gene therapy startup Algen Therapeutics to develop therapies for genetic diseases, with AstraZeneca providing capital, scientific resources, and manufacturing support while Algen brings its proprietary gene therapy platform. These collaborations signal that AI protein design has matured from a promising technology to an essential component of pharmaceutical development pipelines.

Looking toward 2026 and beyond, the most significant impact will likely emerge from the integration of multiple AI capabilities—protein structure prediction, molecular dynamics simulation, interaction modeling, and synthesis planning—into comprehensive platforms that can design, test, and optimize therapeutic candidates in silico before any laboratory work begins. Early-stage startups building these integrated platforms secured significant funding throughout 2025, and their first AI-designed drug candidates are expected to enter clinical trials in 2026-2027. If successful, these trials would validate the approach and trigger a wave of adoption across the pharmaceutical industry, fundamentally reshaping how new medicines are discovered and developed.

4. Retrieval-Augmented Generation (RAG): The Unsung Architecture Powering Enterprise AI

While large language models like GPT-4 and Claude capture public imagination, a less glamorous but potentially more transformative technology is quietly being deployed across thousands of enterprises: Retrieval-Augmented Generation. RAG represents a fundamental architectural shift that makes AI systems more accurate, current, and aligned with specific organizational needs—without requiring the massive computational resources needed to train frontier models.

Retrieval-augmented generation merges retrieval-based methods with generative AI, boosting the performance of AI models by enabling them to access and generate information from extensive external datasets, resulting in more accurate and contextually relevant outputs. The elegance of RAG lies in its simplicity: rather than encoding all knowledge in a model’s parameters (requiring massive training runs every time information updates), RAG systems retrieve relevant information from databases, documents, or knowledge bases in real-time and use that context to generate responses.

Cohere is building enterprise-focused language models tailored for business use with their specialty being retrieval-augmented generation, creating models that understand private data securely to unlock AI for industries like finance, legal, and healthcare. This focus on enterprise applications reflects a crucial insight: most organizations don’t need the world’s most powerful general AI—they need reliable AI that works with their specific data, complies with their regulations, and integrates with their existing systems.

The commercial momentum behind RAG is accelerating rapidly but remains largely invisible to consumers. Every company implementing “AI assistants” for their employees or “AI-powered search” for their customers is likely using some form of RAG architecture. The technology solves several critical problems that have limited AI adoption in enterprises: hallucinations (where AI confidently generates false information), outdatedness (since model training captures only past data), and inability to access proprietary information (the vast majority of valuable business data never appears in public training sets).

Vertical AI applications are exploding, with vertical winners in 2025 surpassing other category winners to capture over $1 billion in combined funding year-to-date, spanning ten industries that represent a convergence of high-value problems, rich data availability, and regulatory momentum. Many of these vertical AI applications rely on RAG to ground their responses in domain-specific knowledge. A legal AI doesn’t need to know everything about the world—it needs deep, accurate knowledge of relevant laws, regulations, and case precedents, which RAG provides efficiently.

Lila Tretikov, Partner and Head of AI Strategy at New Enterprise Associates, stated that there is going to be specialization even within the model layer, with innovation especially as we look at verticalization for specific use cases. RAG enables this verticalization without requiring organizations to train specialized models from scratch, dramatically lowering barriers to entry for AI adoption across industries.

The software tools supporting RAG implementations are maturing rapidly. Vector databases like Pinecone, Weaviate, and Chroma enable efficient similarity search across millions of documents. Embedding models that convert text into numerical representations have improved dramatically, capturing subtle semantic relationships. Orchestration frameworks help developers build RAG pipelines that retrieve, rank, and integrate information seamlessly. This ecosystem maturation means that in late 2025, building a sophisticated RAG system requires weeks rather than years.

What makes RAG particularly significant is its economic model. Training frontier models costs hundreds of millions of dollars and requires thousands of specialized GPUs running for months. RAG systems can be built and deployed for orders of magnitude less investment while delivering better performance for specific use cases. A law firm’s AI assistant built with RAG and a modestly-sized language model can outperform GPT-4 on legal questions by having access to current case law and the firm’s internal precedents.

The deployment timeline for RAG is essentially immediate—the technology is mature and being implemented now. However, the full transformation will unfold over 2025-2027 as RAG architectures evolve to incorporate more sophisticated retrieval strategies, multi-modal information (images, tables, diagrams), and dynamic knowledge bases that update continuously. Enterprises that deploy RAG effectively will have significant competitive advantages: their AI systems will be more accurate, more current, and more aligned with their specific needs than competitors relying solely on general-purpose models.

The broader implication is a shift from AI centralization to AI distribution. Rather than a handful of companies controlling the most capable AI through massive model training, thousands of organizations can build highly capable, specialized AI systems by combining modestly-sized models with RAG architectures and their proprietary data. This democratization of AI capability may ultimately prove more transformative than any single model breakthrough.

5. Edge AI Semiconductor Revolution: Intelligence at the Point of Need

While data center AI chips from NVIDIA dominate headlines and investment portfolios, a parallel revolution in edge AI semiconductors is quietly reshaping how and where artificial intelligence operates. Edge AI—processing intelligence directly on devices rather than in distant cloud servers—promises to enable new categories of applications impossible with cloud-dependent architectures, and the specialized chips making this possible are reaching critical maturity in late 2025.

The edge artificial intelligence chips market is expected to grow at a compound annual growth rate of around 17.75% from 2025 to 2034, driven by rising use of smartphones, wearables, and home automation. This paradigm shift moves processing of complex artificial intelligence workloads from distant, centralized cloud data centers directly to local devices—the edge of the network, promising significantly reduced operational costs, enhanced data privacy and security, and drastically decreased latency.

The companies driving this transformation span the semiconductor ecosystem. In Q1 2025, Intel launched the Tiber Edge Platform with the Geti toolkit for computer-vision model training at the edge, while new CEO Lip-Bu Tan is advancing a strategy to outpace Nvidia by focusing on edge AI devices and systems. AMD unveiled its Embedded+ architecture in February 2024, combining Ryzen Embedded CPUs and Versal adaptive SoCs to develop new ways to simplify sensor fusion and establish a low-latency AI inference platform for industrial, medical, and automotive projects. In April 2024, AMD introduced the Versal AI Edge Series Gen 2 with next-generation AI Engines sporting up to three times TOPS-per-watt for true end-to-end edge AI acceleration.

Beyond the semiconductor giants, specialized startups are pushing boundaries further. Tenstorrent, led by legendary architect Jim Keller, raised $700 million Series D in 2024, valuing the company at $2.6 billion, combining its Tensix cores with an open-source software stack to give developers flexibility and reduce dependency on costly HBM memory systems. Mythic focuses on analog computing for AI inference at the edge, while Groq developed tensor streaming architecture for ultra-low-latency inference, and Lightmatter is pioneering photonic computing that uses light instead of electricity for certain AI operations.

The technical innovations enabling edge AI are diverse. Some companies are using analog computing to perform AI calculations more efficiently than digital circuits. Others are exploring photonic approaches that leverage light’s speed and low heat generation. Still others focus on novel memory architectures that eliminate the “von Neumann bottleneck” where data movement between memory and processors consumes most energy. What unites these approaches is radical efficiency—enabling sophisticated AI in power-constrained environments.

The applications emerging from edge AI chips are transformative. Autonomous vehicles require split-second decisions based on sensor data—any delay from sending data to the cloud and back could be fatal. Smart cameras in retail stores, factories, or cities need to analyze video streams in real-time without overwhelming network bandwidth or cloud infrastructure. Medical devices like continuous glucose monitors or cardiac monitors need AI-powered analysis that works even without internet connectivity. Augmented reality glasses require instant AI processing of visual information with minimal battery drain.

Asia Pacific dominated the edge artificial intelligence chips market by capturing the largest share in 2024 due to its robust semiconductor manufacturing ecosystem, quick uptake of consumer electronics with AI, and growing investments in industrial automation and the Internet of Things. North America is expected to grow at the fastest rate in coming years, driven by substantial expenditures in edge computing powered by AI, expanding use of autonomous systems, and robust venture funding for AI startups.

The economic model favoring edge AI is compelling. Cloud AI requires building and operating massive data centers, transmitting enormous amounts of data over networks, and dealing with unpredictable latency and availability. Edge AI shifts costs from operating expenses (cloud services) to capital expenses (device chips), often resulting in lower total cost of ownership for devices deployed at scale. Privacy-conscious customers and regulators increasingly favor edge processing that doesn’t transmit sensitive data to cloud servers.

The semiconductor industry is experiencing a pivotal moment with billions of dollars in investments from Q3 2024 to Q3 2025, with experts predicting the AI/ML market will expand from $46.3 billion in 2024 to $192.3 billion by 2034. In the near-term 2025-2028, AI-driven tools are expected to revolutionize chip design and verification, compressing development cycles from months to days, with AI-powered Electronic Design Automation tools automating tasks, predicting errors, and optimizing layouts.

The launch timeline for edge AI chips varies by application. Consumer products like smartphones already incorporate neural processing units (NPUs) and will see continued capability improvements through 2025-2026. Industrial applications—smart factories, autonomous robots, precision agriculture—are actively deploying edge AI systems now with significant expansion expected through 2026-2027. Automotive applications, particularly advanced driver assistance systems and autonomous features, will see accelerated edge AI deployment as regulations and technology mature through 2027-2028.

What makes this development particularly significant for the broader AI landscape is that it fundamentally changes the architecture of intelligent systems. Rather than a centralized model where all intelligence lives in cloud data centers, we’re moving toward a distributed model where intelligence is embedded throughout devices, systems, and environments. This distribution enables new applications, improves privacy, reduces infrastructure costs, and makes AI more resilient and responsive. The companies winning the edge AI semiconductor race in 2025-2026 will shape this distributed intelligence future, potentially wielding influence comparable to today’s cloud AI giants.

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Prompt: Evening, Claude. What are the five “biggest” ideas in AI that are under the media radar as of October 28, 2025, but preparing to make an impact in the coming months? Describe each idea in a 1000-to-2000-word essay, and include who is involved, where it’s being developed, its current state of development, its estimated launch date, and why it matters. Please use an essay format and avoid bulleted lists as much as possible.

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