What is AGI? AI’s Next Era: When Machines Start Taking on the Work
As AI systems move from answering prompts to handling complex, multi-step tasks, the boundary between today’s AI agents and the broader idea of artificial general intelligence is becoming harder to ignore. This article examines what AGI means, how autonomous AI is changing knowledge work, and what the shift could mean for India.
A programmer works with code across multiple screens, reflecting the growing role of AI in software development and autonomous coding. Representational image. Image credit: Mikhail Nilov/Pexels
The next phase of artificial intelligence is taking shape inside the companies building it. AI agents are being trained to handle hours-long assignments, while researchers are using AI to write code, investigate technical problems and help develop the next generation of AI systems.AGI generally refers to an AI system capable of learning, reasoning and applying knowledge across a broad range of tasks, rather than being limited to a narrow set of functions.
There is no universally accepted definition or test for AGI, so there is still no agreed threshold for declaring that a system has reached it. But many jobs gradually moving into automation, pushing thousands into uncertainty might help us understand the whole story.
OpenAI says more than 70% of sampled Codex users in May 2026 asked the coding agent to handle tasks estimated to take more than an hour. About a quarter made at least one request estimated at more than eight hours. Anthropic reported in August that Claude was leading 26% of the AI research and development work covered by its internal measurement system, compared with less than 1% in February. Both figures come from the companies themselves and are not measures of the wider economy.
For most people, AI is still something they consult. Ask a question, get an answer. Give it a document, get a summary. Ask for code, get code. That model is changing. The newer systems can take an assignment, break it into steps, use software and other tools, check their progress and continue working with less human intervention.
What happens when AI can handle much more of the work itself? That question sits at the heart of the debate over artificial general intelligence, or AGI.
From Prompts to Assignments
A programmer can ask AI to write a function. An agent can be given a larger job: inspect an existing project, build a feature, run tests, find problems and make corrections. The AI is handling a sequence of tasks rather than producing one answer. That distinction could eventually change how many kinds of knowledge work are organised.
People handle cash at a counting table as digital code and data appear on a projected screen, illustrating the changing relationship between technology, automation and work. Representational image. Image credit: Tima Miroshnichenko/Pexels
AI is Helping Build AI
Frontier AI companies are already making the change clear. OpenAI says it has developed an “automated research intern” capable of performing defined research tasks under human direction. The company says it is working towards an automated AI researcher by March 2028.
Anthropic’s August figures point in the same direction. The company says Claude is increasingly being used in its own AI research and development, although it remains dependent on human researchers and is not fully autonomous in the measured work. AI is helping researchers build better AI.
So, When does AGI Arrive?
Researchers disagree about the capabilities AGI should demonstrate, and no accepted test exists. Google DeepMind CEO Demis Hassabis said in May that he expected AGI could arrive within roughly four years, possibly sooner. If an AI can research a subject, analyse data, write software, use several digital tools and complete a complex assignment, how much of that work still needs to be done by a person? Work will change before we have an answer.
A researcher could delegate a literature review and data analysis. A programmer could hand over an entire software feature. Some tasks may disappear from jobs. Others may become faster. New work will emerge around supervising, testing and governing AI systems.
India is Preparing for the Shift
India is investing heavily in the infrastructure needed for the next phase of AI. The IndiaAI Mission has an approved outlay of ₹10,371.92 crore over five years. The government said in August 2026 that more than 45,000 GPUs had been onboarded through its shared computing programme and 237 projects had accessed subsidised capacity. Twenty indigenous foundation-model proposals had also been selected from 506 applications.
AI systems need to work across Indian languages and very different economic and institutional settings. OpenAI announced an India initiative with Tata Group in February covering areas including AI infrastructure and local capability. Google DeepMind has partnered with Indian institutions on applications in science, education, agriculture and energy.
It becomes harder when an AI system spends hours researching, writing code, analysing information and making decisions before presenting a result. The human role will increasingly involve setting the objective, judging evidence and deciding when a system should be trusted. AGI remains undefined, and nobody can put a reliable date on its arrival. But the shift towards more autonomous AI is already visible.
EP Staff is the editorial team at EdPublica, an independent media organisation focused on science, education, environment and public policy. The team produces evidence-based news, features, explainers and analysis on issues that shape society and everyday life.
Neuropixels Probes Allow Scientists to Record Hundreds of Brain Cells at Once, Opening New Possibilities for Neuroscience
Neuropixels technology is transforming neuroscience by helping researchers monitor hundreds of individual neurons simultaneously and investigate how the brain processes information.
An artistic representation of neural activity in the brain, where electrical signals travel through interconnected networks of neurons. Representational image. Image credit: Merlin Lightpainting/ Pexels
Understanding how the human brain processes information requires scientists to observe the activity of individual neurons and the connections between them. However, recording electrical signals from large numbers of these cells simultaneously has remained a technical challenge.
A technology called Neuropixels is helping researchers overcome this limitation. Developed initially for neuroscience experiments in animals, these extremely thin silicon probes can record electrical activity from hundreds of neurons simultaneously. Their use is now expanding into human research, where they could help scientists investigate brain functions at a level of detail that was previously difficult to achieve.
How Do Neuropixels Probes Work?
Neurons communicate through electrical signals. When a neuron becomes active, it produces a brief electrical impulse known as an action potential. Recording these impulses allows researchers to study when individual neurons fire and how their activity relates to other cells.
Traditional electrode systems had limitations in the number of neurons they could monitor simultaneously. Researchers commonly used electrodes that recorded from a few dozen channels, making it difficult to study activity across several brain regions at once.
Neuropixels uses a thin silicon probe containing hundreds of closely spaced recording sites. These sites detect electrical signals from nearby neurons, while integrated electronics amplify and convert the signals into digital data that researchers can analyse. The first Neuropixels 1.0 probe, developed through a collaboration between researchers at the Howard Hughes Medical Institute’s Janelia Research Campus and Belgium’s Interuniversity Microelectronics Centre (Imec), was reported in 2017.
It contained 960 recording sites along a hair-thin silicon shaft, with 384 channels capable of reading signals simultaneously. In early experiments, researchers inserted two probes into a mouse brain and recorded activity from approximately 700 neurons across five brain regions. Neuropixels 2.0 followed in 2021. Its four-shank version contains 5,120 recording sites, of which 1,536 can be read out simultaneously. The smaller and lighter design also supports longer-term recordings in small animals.
What are the Possibilities of the Technology?
Neuropixels has helped researchers study how different regions of the brain coordinate during sensory processing, movement and decision-making. Earlier experiments often examined relatively small groups of neurons. Recording larger populations has revealed that some brain functions are distributed across several regions rather than confined to a single location.
The technology has also helped scientists investigate neural manifolds, mathematical patterns formed by the collective activity of populations of neurons. Studying these patterns can provide insights into how the brain represents information and organises its activity. One major effort involved the International Brain Laboratory, a collaboration of 12 research laboratories in the United States and Europe.
In a 2025 study, researchers recorded activity from more than 620,000 neurons across 279 brain regions in 139 mice. The resulting dataset provided a large-scale map of neural activity associated with functions including vision, movement and decision-making. Neuropixels is now being used in more than 1,700 laboratories worldwide and has been applied to over two dozen species, including zebra finches, bearded dragons and octopuses.
To Human Brain Research
One of the most significant developments is the technology’s entry into human neuroscience. Researchers at the National Hospital for Neurology and Neurosurgery in London have been working with neurosurgeons to use Neuropixels probes during brain operations.
Electrical discharges radiating from a central electrode in a plasma globe. Neuropixels probes help neuroscientists record the electrical impulses produced by individual neurons and study how brain cells communicate. Representational image. Image credit: R Bude/ Pexels
In one procedure described by Nature, a patient undergoing surgery for normal pressure hydrocephalus, a condition involving excess cerebrospinal fluid inside the skull, agreed to participate in a research experiment.
During the operation, researchers briefly inserted a probe into her brain to record neuronal activity. They played sequences of familiar and unfamiliar word combinations to examine how the brain responded to changes in auditory stimuli.
The recordings allowed researchers to observe electrical responses from individual neurons as the patient received the sounds. Human brain research has traditionally relied on techniques such as electrocorticography, which measures electrical activity through electrodes placed on the brain’s surface. Other devices can record activity at the single-neuron level, but generally monitor fewer cells.
Neuropixels offers a more detailed approach by allowing researchers to examine the activity of large numbers of individual neurons simultaneously. The technique is particularly valuable because some existing imaging methods, including calcium imaging, cannot capture the rapid electrical activity of neurons with the same temporal resolution. Calcium imaging also relies on genetically encoded sensors in many experimental settings, limiting its application in humans.
Understanding Brain Disorders
The ability to monitor individual neurons across larger brain networks could help researchers investigate how changes in neural activity contribute to neurological and psychiatric conditions. Diseases such as Alzheimer’s involve changes in brain cells and the circuits connecting them. Understanding how these changes affect communication between neurons could provide important information about disease progression and the development of potential treatments.
Neuropixels is already being used in research examining neural circuit activity in animal models of neurodegenerative conditions. Its expansion into human research could eventually help scientists compare findings from animal experiments with neuronal activity recorded directly from patients.
However, the technology remains a research tool. Its use during surgery is still developing, and further work is needed to establish safe and reliable procedures for different research applications. The recordings themselves do not diagnose brain disorders or provide an immediate treatment.
For now, the major contribution of Neuropixels is the scale and precision of the information it can collect. By allowing scientists to follow the electrical activity of large populations of individual neurons, it is helping them investigate how the brain processes information through interconnected networks of cells.
New Semiconductor Could Help Make Solar Cells More Efficient, Oxford Study Finds
Researchers from the University of Oxford and Wuhan University have identified bismuth sulfide as a semiconductor material that could overcome a longstanding trade-off between light absorption and electrical charge retention in solar energy conversion.
Advances in semiconductor materials could help improve the efficiency of solar energy conversion. Image credit:Pexels
Researchers from the University of Oxford and Wuhan University have identified a semiconductor material that could help overcome a longstanding limitation in solar energy conversion. The study, published in Nature on October 28, 2026, demonstrates how bismuth sulfide can absorb light strongly while slowing the loss of electrical charges generated during the process.
The discovery could offer a new direction for developing solar cells that convert sunlight into electricity more efficiently. It challenges the conventional understanding that materials with stronger light absorption necessarily lose their electrical charges more rapidly.
Solar cells use semiconductor materials to absorb sunlight and generate electrical charges. When sunlight strikes a semiconductor, it releases negatively charged electrons, leaving behind positively charged gaps known as holes. These charges must reach the electrodes to generate electricity. However, electrons and holes can recombine before being collected, releasing their energy and reducing the amount available for electricity generation.
This process, known as electron-hole recombination, is a major source of energy loss in solar cells. Materials that absorb sunlight efficiently often experience faster recombination, while those that retain electrical charges for longer periods generally have weaker light absorption. This trade-off has restricted the development of materials that combine both properties.
How bismuth sulfide slows electrical charge loss
The researchers found that bismuth sulfide (Bi₂S₃) combines characteristics of two conventional semiconductor types: direct and indirect semiconductors. Direct semiconductors absorb light strongly but generally allow electrons and holes to recombine quickly. Indirect semiconductors have slower recombination rates but weaker light absorption.
Bismuth sulfide exhibits a combination of these properties. The researchers describe it as a pseudo-direct semiconductor, in which closely spaced electronic energy levels allow the material to absorb light strongly while providing a lower-energy pathway that slows electrical charge recombination.
To investigate this mechanism, the team studied thin films of bismuth sulfide using ultrafast terahertz spectroscopy. By exciting the material with extremely short laser pulses, researchers measured the behaviour of the resulting electrical charges across different temperatures.
The findings, supported by electronic-structure calculations, showed that the material’s unusual electronic arrangement could reduce the rate of electron-hole recombination by approximately 75% at room temperature compared with conventional materials having similar light absorption.
Slower recombination could give electrical charges more time to reach the electrodes, potentially reducing energy losses during electricity generation. The researchers also suggest that the mechanism could be relevant to solar fuel technologies, where sunlight is used to drive chemical reactions.
Building on advances in solar-cell materials
The findings add to ongoing research aimed at improving solar-cell performance by controlling charge-carrier recombination. A study published in Nature Photonics in 2025 demonstrated how surface engineering could reduce recombination losses and improve the stability of perovskite solar cells.
By introducing sodium heptafluorobutyrate to modify the perovskite surface, researchers achieved a certified power-conversion efficiency of 26.96%. The devices retained their initial efficiency after 1,200 hours of continuous illumination and maintained 92% of their initial efficiency after 1,800 hours of ageing at 85°C.
Representative image.Image credit:Pexels
While the two studies use different approaches, they highlight the importance of controlling electrical charge losses in developing more efficient and stable solar technologies.
The Oxford researchers suggest that other materials, including tungsten diselenide and indium selenide, could be investigated for similar electronic properties. They also propose that techniques such as stretching layered crystals could help modify the energy levels of certain semiconductors.
However, the discovery does not mean that more efficient commercial solar panels are immediately within reach. The Oxford study establishes the physical mechanism in bismuth sulfide thin films rather than demonstrating a complete solar cell with improved power-conversion efficiency. Further research will be needed to assess its performance, stability and manufacturing potential.
By showing how strong light absorption can coexist with slower electrical charge recombination, the research offers a new direction for identifying semiconductor materials for future solar energy technologies.
MIT Researchers Develop Origami-Based Method for Lightweight, Load-Bearing Structures
MIT researchers have developed an origami-inspired construction technique that could enable lightweight, rigid structures using less material and simpler assembly methods.
MIT researchers have developed an origami-inspired construction technique that could enable lightweight, rigid structures using less material and simpler assembly methods.Image credit:MIT
Researchers at the Massachusetts Institute of Technology (MIT) have developed an origami-inspired construction method that could enable the fabrication of lightweight, rigid structures using less material and simpler assembly techniques. The approach could eventually be used for emergency shelters, architectural components and lightweight aerospace parts.
The modular system uses components cut from flat sheets of material, folded into shape and connected to form three-dimensional structural shells. According to MIT, simulations indicate that the structures can be approximately 3.5 times stiffer than those produced using some other structural origami techniques, while requiring only about two-thirds as much material.The research was announced by MIT on October 27 and published in the International Journal of Solids and Structures.
Building structures through folding
The researchers drew inspiration from geometric patterns developed by artist and scientist Ron Resch in the 1960s. Unlike methods that fold a single sheet into a complete structure, the new approach uses individually fabricated components that are folded and connected to create a larger framework.
Alfonso Parra Rubio, a graduate student at MIT’s Center for Bits and Atoms and lead author of the study, developed a method for combining two tripod-shaped units into a rigid building block. These blocks can be interconnected to form a network of beams that creates a lightweight structural shell.
To simplify the design process, the team developed computational software that automatically generates components according to the intended structure. The software checks whether individual units can be folded before combining them into the final design.
The resulting files can be sent to computer-controlled cutting equipment to manufacture components from flat sheets of material, including aluminium. Workers can then fold and assemble the units using basic hand tools, without requiring large-scale welding equipment.
Demonstration at architectural scale
To demonstrate the method’s scalability, the researchers constructed a 23-foot folded-aluminium sculpture that was installed in the atrium of the Boston Public Library. According to MIT, a team without professional construction experience assembled the structure in a matter of days.
The researchers say the modular design simplifies assembly because the components are engineered to fit into their intended positions as the structure takes shape.
Representational image.Image credit:Pexels
However, the reported stiffness improvements are based on simulations comparing the method with other origami-based systems. Further testing will be necessary to establish its performance across different materials, loading conditions and real-world construction environments.
Potential applications
One potential application is disaster-relief housing. Flat-packed components could be transported efficiently to areas affected by earthquakes or floods and assembled on site, potentially reducing transportation and construction requirements.
The approach could also be explored for lightweight aerospace components, modular robots, sound-dampening panels and building facades. Its ability to create rigid structural shells without interior support columns could offer additional flexibility in architectural design.
The researchers plan to test the technique in larger architectural structures and investigate more complex component geometries that could improve strength and stiffness. They also intend to develop a more accessible interface for designing structures using the system.
While the method has yet to demonstrate widespread use in buildings, the research illustrates how geometric design and digital manufacturing could offer alternatives for constructing complex structures.
Its practical adoption will depend on factors including durability, manufacturing costs, structural safety and performance under real-world conditions. Nevertheless, the approach presents a potential option for applications where reducing material use and simplifying assembly are priorities.