The next technological revolution may not arrive through a single invention. Instead, it could emerge from a collection of breakthroughs that reshape how people access energy, healthcare, food and essential resources.

That is the picture painted by the World Economic Forum’s Top 10 Emerging Technologies Report 2026, produced in collaboration with scientific publisher Frontiers. The report identifies a new generation of technologies that are moving from laboratories into practical applications and could begin influencing everyday life within the next three to five years.

What makes this year’s list particularly striking is a common thread connecting many of the innovations. They are designed to make critical resources—whether electricity, medicines or industrial materials—more accessible, more efficient and less dependent on geography or large centralized systems.

Emerging Technologies 2026 Are Bringing Energy Closer to People

For more than a century, electricity has largely travelled in one direction—from power stations to homes and businesses. That model may soon begin to change.

One of the technologies highlighted in the report is Everything-to-Grid Energy, or X-to-Grid. The concept allows buildings, electric vehicles, factories and even data centres to send stored electricity back into the grid during periods of high demand. Instead of acting solely as consumers, these assets become active participants in energy generation and storage.

Combined with the rapid growth of renewable energy, such systems could make electricity networks more resilient and reduce waste.

Another energy-related breakthrough is Direct Lithium Extraction, which dramatically shortens the time required to recover lithium from brine sources. Lithium is a critical component of electric vehicle batteries and energy storage systems. Faster and more sustainable extraction methods could strengthen supply chains while reducing environmental impacts.

Meanwhile, Passive Radiative Cooling Materials offer a low-energy response to rising temperatures. These materials cool buildings by reflecting sunlight and releasing heat into the atmosphere, reducing the need for conventional air-conditioning systems. In a warming world, such innovations could become increasingly important.

Emerging Technologies 2026 Could Transform Healthcare

Healthcare is another field undergoing rapid transformation.

Among the technologies attracting attention are Personalized mRNA Cancer Vaccines, which are designed around the unique genetic mutations present in an individual patient’s tumour. Unlike conventional treatments, these vaccines train the immune system to recognise and attack specific cancer cells, potentially reducing the likelihood of recurrence.

Researchers are also making progress with Exosome Drug Delivery, a technique that uses naturally occurring particles produced by cells to transport medicines directly to targeted areas of the body. Scientists believe this approach could eventually help deliver treatments to difficult-to-reach locations, including parts of the brain.

The report further highlights Quantum Simulation for Drug Discovery, which uses advanced computational models to understand molecular interactions with unprecedented precision. If successful at scale, such systems could shorten drug development timelines and reduce costs, accelerating the arrival of new therapies.

Together, these technologies point towards a future in which medicine becomes increasingly personalised, predictive and precise.

Emerging Technologies 2026 Aim to Tackle Climate and Resource Challenges

Many of the technologies featured in the report are aimed at addressing environmental pressures and resource constraints.

One example is PFAS Destruction, designed to eliminate so-called “forever chemicals” that can persist in water supplies and ecosystems for decades. Traditional treatment methods often struggle to break down these substances, but emerging approaches are showing promising results.

Another notable innovation is Precision Fermentation, which uses microorganisms such as yeast and bacteria to produce ingredients, chemicals and materials more efficiently than conventional manufacturing methods. The technology has applications across food production, pharmaceuticals and industrial manufacturing while requiring fewer natural resources.

Such advances reflect a growing effort to decouple economic growth from resource-intensive production systems.

Emerging Technologies 2026 Reveal the Next Frontier of AI and Computing

Artificial intelligence continues to evolve beyond today’s machine-learning models.

The report highlights World Models, a new class of AI systems capable of building sophisticated representations of physical environments. By combining multiple forms of data, these systems may help machines better predict outcomes, plan actions and interact with the real world. Applications could range from robotics and autonomous vehicles to industrial automation.

At the same time, advances in quantum computing are forcing researchers to rethink cybersecurity.

One response is Lattice-Based Cryptography, a form of encryption designed to remain secure even against future quantum computers. As quantum machines become more powerful, such technologies may play a crucial role in protecting financial systems, government infrastructure and personal data.

A Future That Is More Local, Personal and Resilient

Viewed individually, each of these technologies addresses a specific challenge. Together, however, they reveal a broader trend.

Energy generation is becoming more distributed. Healthcare is becoming more personalised. Manufacturing is becoming more efficient. Critical resources are moving closer to the people who need them.

The World Economic Forum notes that scientific breakthroughs alone are not enough. Infrastructure, regulation, investment and public trust will ultimately determine whether these innovations achieve widespread adoption. Technologies that perform well in laboratories often face significant hurdles when scaled to real-world conditions.

Yet the report offers a glimpse of a future that looks markedly different from today’s systems—one where energy, healthcare and essential resources are more accessible, adaptable and resilient.

The coming decade may reveal whether these emerging technologies remain promising experiments or become the foundations of everyday life.

Researchers at the Massachusetts Institute of Technology have introduced DAAAM AI system, a new artificial intelligence system designed to enable robots to better approximate human-like understanding of space, memory, and context. DAAAM stands for Describe Anything, Anywhere, Anytime, at Any Moment. This framework represents a major step toward enabling machines to interpret and remember the world in a meaningful way.

DAAAM AI System Moving Beyond Traditional Mapping

Conventional robotic systems depend largely on spatial maps to understand environments. While these maps are effective for movement, they often lack descriptive depth. A robot may know where something is, but not what it is or how it relates to its surroundings.

DAAAM addresses this limitation by combining robotic mapping with advanced computer vision. As a robot explores its environment, it not only builds a three-dimensional map but also attaches detailed descriptions to objects within that space. This allows the system to transform raw spatial data into a structured, information-rich memory.

“If we want robots to work side-by-side with humans and interact better with humans, they must speak the same language. The robot must be able to reason about time and space the same way humans do. That is essentially what our method is doing. It is turning a traditional map into a language-based map that is easier for the robot to think about and access using language,” said Luca Carlone (Associate professor in MIT’s Department of Aeronautics and Astronautics) while addressing the media.

Understanding Space and Time Together

What makes DAAAM particularly significant is its ability to incorporate both spatial and temporal reasoning. The system records observations over time, enabling robots to recall not just locations, but also past interactions.

For instance, instead of simply identifying a workspace, a robot can remember that a specific component was placed there during a previous task. This capability allows it to respond to complex, real-world queries, such as retrieving an item that was last used at a certain time and place.

Efficiency at Scale

One of the key challenges in developing such systems is managing the large volume of visual data encountered in real-world environments. DAAAM overcomes this by selectively processing key visual frames and grouping nearby objects, allowing it to generate detailed descriptions efficiently.

“We annotate every object only once, so our framework can run in very large-scale environments in real time. And by clustering objects into regions, it can answer a wide range of queries about objects and locations in the environment,” one of the lead authors, Gorlo, explains.

With this, the system reduces computational demands while maintaining accuracy. This makes it suitable for real-time operation, even in large and complex environments.

Integrating Language for Interaction

Another defining feature of DAAAM is its integration with LLMs with tool-based retrieval to reduce hallucinations. This enables robots to interpret and respond to natural language queries, making interactions more intuitive for human users.

Rather than requiring precise commands, users can communicate with the system in everyday language. The AI then retrieves relevant information from its structured memory, providing accurate and context-aware responses.

Potential Applications

The implications of this technology extend across multiple domains. In industrial settings, robotic assistants could help workers locate tools or components based on verbal instructions. In augmented reality, similar systems could assist with navigation or highlight anomalies in complex environments.

A Step Toward Human-Like Intelligence

By merging spatial awareness, contextual understanding, and language processing, DAAAM represents a significant advancement in artificial intelligence. It shifts the role of robots from simple task executors to systems capable of reasoning about their surroundings.

As research continues, technologies like DAAAM could redefine how humans and machines collaborate, bringing us closer to a future where robots not only operate in our world but truly understand it.

The rapid ascent of generative artificial intelligence is actively reshaping how we learn, create, and work. It offers a seductive promise of instant knowledge and effortless productivity, a modern-day magic trick available at our fingertips. But like any good magic act, the most important part of the illusion is what the audience doesn’t see. Behind the curtain of flawlessly formed paragraphs and instant data analysis lies a complex and often invisible world of ethical trade-offs and profound physical costs.

Consider the experience of a college student in 2025. When she asked an AI tutor for help with an essay, she watched in amazement as articulate, well-structured text appeared in seconds, complete with what looked like flawless references. The magic, however, quickly faded. As she began to engage critically with the output—tracing sources and questioning claims—she discovered some references were entirely fictitious, the reasoning was hollow, and the fluent prose was merely a sophisticated imitation of insight.

This student’s discovery is a microcosm of a much larger challenge. Her story moves us beyond the simple wonder of a new technology to the central question of our time: Are we truly prepared for the full consequences of the AI revolution? And in this new age, what does it mean to be genuinely “AI literate”?

Education Publica explores the good, the bad, and the hidden “ugly” of artificial intelligence. Our future depends not on whether if we use this transformative tool, but how we choose to use it—effectively, ethically, and with full awareness of its staggering environmental footprint. The path forward requires moving past the illusion and understanding the true cost of the bargain we are making.

The Promise and The Peril: AI’s Double-Edged Sword

To navigate the new AI landscape responsibly, we must first appreciate its dual nature. AI is neither a pure panacea nor an unmitigated threat; it is a powerful tool with the capacity for both transformative good and significant harm. Understanding this duality is the first step toward harnessing its potential while mitigating its inherent risks.

The Promise: A Revolution in Access and Efficiency

Proponents rightly point to a suite of transformative capabilities, particularly in making education more efficient and accessible. The key benefits are already becoming clear:

• Personalized Learning and Access: AI-powered tutors can provide students with 24/7 support, offering rapid feedback and accessible explanations. In a 2025 survey of undergraduates at a large US public university, students confirmed they value this immediate assistance. This technology holds particular promise for bringing personalized learning to underserved regions, such as India.

• Administrative Efficiency: For educators, AI can streamline time-consuming tasks like drafting lesson plans, summarizing readings, and assisting with grading. This frees up valuable time for them to focus on mentoring students and engaging in higher-order teaching.

• Research Acceleration: In academic and scientific fields, AI is a powerful catalyst. It can dramatically speed up literature reviews, process vast datasets, and even help generate new hypotheses, significantly boosting research productivity.

The Peril: The Costs to Integrity, Privacy, and Equity

Alongside its immense promise, AI introduces tangible risks that threaten the core tenets of academic inquiry and social equity. These perils require careful management and proactive policy.

1. Academic Integrity and Critical Thinking: The ease of generating text with AI presents a significant threat to academic integrity. A 2023 educational study warned that this makes it easier than ever for students to submit work they did not write. A more subtle danger is the phenomenon of AI “hallucinations”—false but convincingly presented information—which many students are ill-equipped to identify. Over-reliance on these tools risks weakening the essential skills of critical reasoning and research.

2. Privacy and Surveillance: The use of AI tools in education often involves storing vast amounts of student data on remote servers. Without robust policies and oversight, this sensitive information can be misused or profiled, creating significant privacy and surveillance risks.

3. The Widening Digital Divide: The benefits of AI are not universally accessible. Effective use requires stable internet, modern devices, and reliable electricity. Students from disadvantaged backgrounds who lack these resources risk falling even further behind, deepening existing educational and social inequities.

But these visible debates are a distraction from a far larger, physical cost that is being silently added to a global environmental ledger.

The Ugly: AI’s Invisible Environmental Footprint

While academia and industry wring their hands over plagiarism and bias, they remain wilfully blind to a far more inconvenient truth: the AI revolution is built on a foundation of staggering energy and water consumption. This isn’t an abstract cost; it’s a physical debt being charged to the planet with every query. This is the engine room of the illusion, an immense, energy-hungry global infrastructure that our collective failure to recognize is a critical flaw in our current understanding of the technology.

A Stark Literacy Gap

In a recent survey of over 30 undergraduate and postgraduate students from India, the UK, and Canada, a startling consensus emerged. These students, from diverse fields including engineering and humanities, were either enthusiastic or casual users of AI. Yet, with the exception of a single master’s scholar, not one of them had any meaningful understanding of AI’s physical and environmental footprint.

Their perception of AI was telling, revealing a profound disconnect between the digital tool and its physical reality.

Students frequently described AI as “free,” “virtual,” “weightless,” or “just code.” The notion that AI has a physical footprint—servers, cooling systems, chips, power draw—was almost entirely absent.

This gap represents a fundamental failure of AI literacy. Current education and discourse overwhelmingly focus on what AI does for us, not what it costs the planet. This blind spot is shaping policy and user behaviour at a moment when the stakes could not be higher.

Quantifying the Cost: From a Single Query to Global Demand

The feeling of “weightlessness” is an illusion. In terms of energy, a simple Google search and a generative AI query are worlds apart. The difference is not incremental; it is exponential.

The implication of this data is staggering: One long AI query can consume as much electricity as 30–100 Google searches. When multiplied by hundreds of millions of daily queries worldwide, this individual cost scales into a global crisis.

The scale of this shift is not theoretical; it is being meticulously tracked by global energy watchdogs, and their findings are alarming. The International Energy Agency (IEA) provides a chilling macro-level view of this trend:

• 2024 Consumption: Data centres consumed an estimated 415 TWh of electricity, representing 1.5% of global demand.

• 2030 Projection: Driven primarily by the explosive growth of AI, this demand is projected to more than double to 945 TWh.

• A Shocking Equivalence: This projected demand is equal to the entire annual electricity consumption of Japan.

IEA’s recent analysis signals that AI is no longer just a technological tool but an energy-intensive industrial sector. Its electricity demands are now large enough to reshape consumption patterns in advanced economies and rival global investment in oil — a striking sign of the world’s transition into the “Age of Electricity.”

“Analysis in the World Energy Outlook has been highlighting for many years the growing role of electricity in economies around the world. Last year, we said the world was moving quickly into the Age of Electricity – and it’s clear today that it has already arrived,” said IEA Executive Director Fatih Birol. “In a break from the trend of the past decade, the increase in electricity consumption is no longer limited to emerging and developing economies. Breakneck demand growth from data centres and AI is helping drive up electricity use in advanced economies, too. Global investment in data centres is expected to reach $580 billion in 2025. Those who say that ‘data is the new oil’ will note that this surpasses the $540 billion being spent on global oil supply – a striking example of the changing nature of modern economies.”

This global problem is coming to a head in nations where the balance between progress and sustainability is most delicate, nowhere more so than in India.

India at the AI Crossroads

India stands as the global epicenter of AI’s collision between digital ambition and physical limits. Its unique combination of immense economic opportunity, significant digital disparity, and acute environmental stress makes its approach to AI adoption a high-stakes paradox—and a bellwether for the entire developing world.

The Multi-Billion Dollar Promise

The economic incentives for embracing generative AI are enormous. An EY report estimates that by 2029-30, the adoption of GenAI could add US359 billion to US438 billion to India’s GDP, promising to accelerate growth and enhance productivity across the nation.

A Collision Course with Reality

Yet, this multi-billion-dollar vision, articulated by consultancies like EY, is on a direct collision course with the stark physical limitations outlined by energy and environmental analysts. For India, the promise of virtual wealth is tethered to the reality of stressed power grids and scarce water. Unregulated AI adoption threatens to exacerbate several pre-existing, systemic challenges:

• Digital Disparity: Large segments of the population still lack reliable access to the stable internet and modern devices required for AI-driven learning and work.

• Stressed Infrastructure: The nation’s electricity grids are already under significant strain, and the massive energy demands of AI data centres could push them to their limits.

• Environmental Scarcity: Many regions across India face severe water scarcity, a problem that would be intensified by the vast water requirements for cooling data centres.

• Budgetary Constraints: Public educational institutions operate on tight budgets, making it difficult to fund the necessary technological infrastructure and training for students and educators.

For India, blindly pursuing AI adoption is not a viable path. A deliberate, responsible, and human-centered framework is not just an option; it is an absolute necessity.

Redefining AI Literacy for a Sustainable Future

The challenges posed by AI, while significant, are not insurmountable. Addressing them requires a new, more comprehensive definition of AI literacy—one that is human-centered, ethically grounded, and environmentally accountable. The goal is not to restrict AI, but to build a foundation of trust and sustainability for its responsible integration into society. This requires a coordinated effort from educational institutions, organizations, and policymakers. According to UNESCO, AI literacy involves equipping learners and educators with a human-centred mindset, ethical awareness, conceptual understanding, and practical skills to use AI responsibly, understand its implications, and adapt as AI technologies evolve.

Building on UNESCO’s foundation, Education Publica proposes a broader, more future-ready definition: AI literacy is the integrated set of knowledge, skills, attitudes, and ethical principles that enable individuals to understand what AI is and how it works; use AI tools effectively and safely; critically interpret and question AI outputs; recognise the societal, ethical, economic, and environmental impacts of AI systems; and make informed, responsible choices about when, why, and how to engage with AI.

For Educational Institutions and Organizations:

A proactive, principles-based approach is essential for navigating the complexities of AI integration. The following strategies provide a roadmap for responsible adoption:

1. Demand Vendor Transparency: Insist that AI providers not only disclose per-query energy data, carbon metrics, and water consumption but also provide “explainable AI” algorithms, helping users understand the “why” behind an output, not just the “what.”

2. Mandate Comprehensive AI Literacy: Implement formal courses covering not just AI use, but also its limitations, including inherent bias, hallucination risks, data privacy ethics, and its full environmental impact.

3. Establish Clear Ethical Guidelines: Develop and enforce robust academic integrity policies that explicitly define where AI is allowed (e.g., for brainstorming), allowed with declaration (e.g., for drafting assistance), or prohibited (e.g., in exams).

4. Protect Equity: Ensure students without reliable access to technology are not disadvantaged by maintaining viable offline alternatives for key academic activities and assessments.

5. Foster a Culture of Innovation and Inquiry: Beyond just mandating courses, institutions must build a culture that encourages experimentation and critical feedback loops, as recommended by industry leaders at EY. This involves creating cross-functional teams to continually assess AI’s impact on learning and well-being.

6. Invest in Sustainable Infrastructure: Prioritize renewable-powered cloud providers and perform continuous audits of energy and water consumption related to AI workloads.

For Policymakers:

The role of government is crucial in shaping a healthy AI ecosystem. Policymakers must work to create a global consensus on AI regulation, learning from successful international models while crafting domestic policies that support both innovation and responsible, human-centered use.

These steps are not about stifling innovation. They are about building the necessary foundation of trust and sustainability for AI’s successful and long-term integration into our society.

The EP View: Embracing AI with Eyes Wide Open

Artificial intelligence is neither the utopian solution some have promised nor the existential threat others have feared. It is a powerful tool—and like any tool, its ultimate value will be determined by the wisdom and foresight of those who wield it. The magic is compelling, but we can no longer afford to be mystified by the illusion. True literacy means looking behind the curtain and understanding the machinery and the costs.

The future of learning and work will undoubtedly be AI-enabled. It is our collective responsibility to ensure that this future is also human-centered, equitable, and environmentally conscious. To do so, we must move forward with our eyes wide open, ready to ask the hard questions and build a world where technological progress serves human values and planetary health.