Technology is changing faster than ever, but 2026 is showing an important shift in the way innovation is developing. Instead of focusing only on software and smartphones, many of the most significant technology developments are moving into the physical world.
Artificial intelligence is becoming more capable of performing multi-step tasks, AI infrastructure is expanding to support growing computing demand, robots are becoming more intelligent, and technologies such as smart glasses and advanced computing are creating new ways for people to interact with digital systems.
A recent McKinsey Technology Trends Outlook 2026 identified 14 technology trends and reported that several areas—including agentic software development, AI infrastructure, AI for scientific discovery and engineering, space technologies and robotics—were on track for substantially higher investment in 2026 than in 2025. McKinsey also emphasizes that investment figures are signals of market activity rather than guarantees of future outcomes.
This article explores the most important emerging technology trends in 2026, how they work, where they are being used and what users and businesses should know about the technology landscape.
What Are Emerging Technology Trends?
Emerging technology trends are new or rapidly developing technologies that have the potential to change how people work, communicate, create products and solve problems.
Some technologies take years to become mainstream. Others can move from research laboratories into consumer and business applications much faster.
Artificial intelligence is a clear example.
AI was already widely used before 2026, but the technology is now moving beyond simple chatbots and content-generation tools. AI systems can increasingly interact with software, use tools, analyze information and perform multiple steps toward a goal.
At the same time, AI is creating demand for new infrastructure, including specialized processors, data centers, networking technologies and energy-management systems.
Why Is Technology Changing So Quickly in 2026?
Several factors are contributing to the rapid development of technology.
1. Artificial Intelligence
AI is becoming a foundation for many other technologies. Software developers, researchers, businesses and consumers are integrating AI into existing products and workflows.
2. Better Computing Infrastructure
More powerful AI systems require significant computing resources. This is increasing demand for advanced processors, high-bandwidth memory, networking and data-center infrastructure.
3. Automation
Businesses are increasingly interested in technologies that can automate repetitive tasks and assist employees with complex workflows.
4. Connected Devices
Smartphones, wearables, vehicles, computers and other devices are becoming increasingly connected and intelligent.
5. Advances in Robotics
AI is helping robots understand their environments and perform tasks that previously required direct human control.
Key Emerging Technology Trends in 2026
1. AI Agents and Agentic Software
One of the most important developments in technology is the move from traditional AI assistants toward AI agents.
A traditional chatbot generally waits for a user to provide a prompt and then produces a response.
An AI agent can be designed to take a goal, plan multiple steps, use tools and interact with software to complete a task.
For example, an AI coding agent might analyze a software project, identify an issue, modify code, run tests and report the results.
Google is also expanding infrastructure for AI agents. In September 2026, Google Cloud announced that its API Gateway can function as a native remote Model Context Protocol server, allowing existing REST APIs to become discoverable tools for AI agents while retaining authentication, quotas and logging policies.
This is important because AI agents need access to useful tools. Connecting agents with existing software systems can make them more practical for business workflows.
However, greater autonomy also creates security and governance challenges. Organizations need to understand what an AI agent can access, what actions it can perform and how those actions are monitored.
2. AI Infrastructure and Data Centers
AI applications require powerful computing infrastructure.
Modern AI workloads can require specialized processors, high-speed networking, large amounts of memory and substantial electricity.
This has turned AI infrastructure into a major technology sector of its own.
McKinsey’s September 2026 technology analysis estimates that investment in AI infrastructure and model architectures could rise substantially in 2026 compared with 2025, based on first-half investment and extrapolated growth. The organization estimated roughly $770 billion in 2026 investment for this area, compared with about $145 billion in 2025. These figures are projections based on investment trends, not guaranteed spending totals.
NVIDIA is also working with infrastructure partners on larger AI computing deployments. In September 2026, the company announced collaborations in Australia targeting up to a 2-gigawatt buildout by 2027 for AI infrastructure.
The expansion of AI infrastructure also raises an important question: how much energy will increasingly powerful AI systems require?
3. Energy-Efficient AI Computing
The growth of AI means technology companies are increasingly interested in improving computing efficiency.
The goal is not simply to build faster computers. Companies are also looking for ways to perform more AI work using available power and infrastructure efficiently.
NVIDIA, Google and Emerald AI announced the AI Energy Management Alliance in September 2026. The initiative focuses on flexible AI data centers that can adjust electricity usage in response to grid conditions.
This illustrates an important development in modern technology: AI infrastructure is becoming connected to energy infrastructure.
Future data centers may need to consider computing performance, electricity availability, grid stability and environmental impact at the same time.
4. Physical AI and Robotics
AI is increasingly moving from screens into physical machines.
This area is sometimes described as physical AI—the combination of artificial intelligence, sensors, robotics and machines capable of interacting with the physical world.
A robot can potentially use cameras and other sensors to understand its environment, make decisions and perform physical actions.
Applications include:
- Manufacturing
- Warehousing
- Logistics
- Healthcare assistance
- Agriculture
- Inspection
- Delivery
- Industrial automation
- Dangerous or repetitive tasks
McKinsey describes physical AI as a major emerging area but also notes that truly autonomous multipurpose robots remain constrained by computational requirements, sensing capabilities and safety concerns.
This means that while robotics is progressing rapidly, fully general-purpose robots should not be treated as an already solved technology.
5. AI for Scientific Discovery
Another major technology trend is the use of AI in scientific research.
Scientists can use AI systems to analyze huge datasets, identify patterns, generate hypotheses and assist with computational research.
This is particularly valuable in fields where datasets are too large or complex to analyze manually.
Examples include:
- Biology
- Genomics
- Drug discovery
- Materials science
- Climate research
- Weather forecasting
- Astronomy
- Chemistry
McKinsey lists AI for scientific discovery and engineering among the technology areas showing strong investment momentum in 2026.
The important point is that AI does not eliminate the need for scientists.
Computational predictions still need to be evaluated, tested and, where appropriate, validated through experiments.
6. Smart Glasses and AI Wearables
Wearable technology is also entering a new phase.
Traditional smartwatches primarily focus on notifications, fitness tracking, communication and applications.
AI-powered wearables can potentially provide a more conversational interface.
Smart glasses, for example, can combine cameras, microphones, speakers and AI services.
Potential applications include:
- Voice assistance
- Navigation
- Translation
- Object recognition
- Accessibility
- Information retrieval
- Hands-free communication
However, smart glasses also introduce serious privacy questions because cameras and microphones can capture information from surrounding people.
Reuters reported in September 2026 that privacy concerns are becoming an important issue as technology companies explore AI-powered glasses and other wearable devices.
For wearable AI to become widely trusted, companies will need clear privacy controls, visible recording indicators and understandable data policies.
7. Local and On-Device AI
Another important direction is running AI models closer to the user.
Instead of sending every task to a remote cloud server, some AI workloads can run directly on smartphones, computers or other devices.
Google’s Antigravity SDK update in September 2026 introduced support for local AI models and offline agentic workflows, including the ability to run certain models locally using LiteRT.
On-device AI can provide several potential advantages:
- Lower latency
- Better offline functionality
- Reduced dependence on cloud connectivity
- Potentially improved privacy for certain workloads
- More control over local data
However, local AI also depends on the processing power, memory and battery capacity of the device.
Cloud and local AI are therefore likely to coexist rather than one completely replacing the other.
8. AI-Powered Software Development
Software development is another field experiencing major changes.
AI coding assistants can already help developers generate code, explain functions, identify errors and work with large codebases.
The next stage involves AI agents that can participate in larger parts of the development process.
An agent may be able to:
- Understand a software requirement.
- Inspect an existing codebase.
- Propose a solution.
- Modify code.
- Run tests.
- Analyze errors.
- Make additional changes.
- Prepare the final result for human review.
McKinsey’s 2026 technology research identifies agentic software development as one of the areas experiencing particularly strong investment momentum.
Human developers remain important because generated code still requires testing, security review, architectural decisions and appropriate human oversight.
9. Quantum Computing
Quantum computing remains an emerging technology rather than a mainstream replacement for conventional computers.
Quantum computers use quantum-mechanical principles to process certain types of problems differently from classical machines.
Potential long-term applications include:
- Chemistry simulations
- Materials research
- Optimization
- Cryptography research
- Scientific computing
Hardware development is continuing.
IBM’s September 2026 Quantum Compute Service documentation reports the availability of its first Nighthawk r2 QPU, described as a 120-programmable-qubit device with higher throughput than its previous Heron processor.
Quantum computing still faces major engineering challenges, so users should view it as an evolving research and computing field rather than an everyday replacement for laptops and cloud servers.
How These Technologies Work Together
The most interesting part of today’s technology landscape is that these developments are not isolated.
They increasingly depend on one another.
For example:
AI agents + cloud computing + AI infrastructure
AI agents require models, data, software tools and computing resources.
AI + robotics
AI provides perception and decision-making capabilities that can help robots operate in changing environments.
AI + scientific computing
AI can analyze scientific datasets and help researchers explore potential hypotheses.
AI + smartphones
Local AI can provide intelligent features directly on consumer devices.
AI + cybersecurity
As organizations deploy more AI agents, they also need systems capable of monitoring and securing those agents.
Benefits of Emerging Technology
The rapid development of technology can create significant opportunities.
Greater Productivity
AI tools can automate repetitive tasks and assist with information processing.
Faster Research
AI can analyze large datasets much faster than many traditional manual workflows.
Improved Automation
Robotics and intelligent software can automate repetitive or dangerous tasks.
Better Accessibility
Voice interfaces, computer vision and AI assistants can help people interact with technology in new ways.
New Business Opportunities
Emerging technology creates demand for developers, cybersecurity professionals, AI engineers, data specialists and other technology workers.
Challenges and Limitations
Emerging technology also creates important challenges.
Privacy
AI devices may process personal, workplace or environmental information.
Cybersecurity
More connected systems can create additional attack surfaces.
Reliability
AI systems can produce incorrect information or make inappropriate decisions.
Cost
Advanced AI infrastructure can require significant investment.
Energy Consumption
Large-scale computing infrastructure requires substantial electricity and cooling.
Human Oversight
Organizations need clear rules for when humans should review AI-generated decisions and actions.
Skills Gap
Companies may struggle to find workers with the technical skills needed to build and manage advanced technology systems.
What Businesses Should Know About Technology in 2026
Businesses should not adopt emerging technology simply because it is new.
A practical technology strategy should begin with a specific problem.
For example, a company might ask:
- Can AI reduce repetitive administrative work?
- Can automation improve customer support?
- Can AI help developers test software?
- Can data analytics improve decision-making?
- Can robotics improve workplace safety?
- Can local AI reduce cloud dependency?
- What security controls are required?
The technology should then be evaluated based on cost, reliability, security, privacy and measurable business value.
McKinsey’s research on scaling agentic AI also emphasizes that organizations need to redesign workflows rather than simply adding AI tools to existing processes.
What Consumers Should Know
Consumers should also approach new technology carefully.
Before purchasing an AI-powered device or service, check:
- What data does it collect?
- Where is the data processed?
- Can users disable data collection?
- Does the device require a cloud connection?
- What happens if the AI makes a mistake?
- How long is personal data retained?
- What security protections are available?
- Is the company transparent about its AI capabilities?
This is particularly important for cameras, microphones, smart glasses and AI assistants.
The Future of Technology
The future of technology is likely to be less about one individual device and more about connected intelligent systems.
A smartphone could communicate with AI services, wearable devices and cloud applications.
A business could use AI agents alongside employees, while cybersecurity systems monitor those agents.
A research laboratory could combine AI models with scientific instruments and automated experimentation.
Factories could combine robotics, computer vision, AI and real-time analytics.
These possibilities are still developing, and not every technology trend will become mainstream.
McKinsey’s 2026 technology outlook describes the broader shift as technology moving beyond screens into physical infrastructure, intelligent machines, scientific systems and space technologies.
The most important trend may therefore be convergence: different technologies working together to create systems that were previously difficult or impossible to build.
Frequently Asked Questions
What are the biggest emerging technology trends in 2026?
Major trends include AI agents, AI infrastructure, robotics and physical AI, AI for scientific discovery, smart wearables, local AI, agentic software development and quantum computing. McKinsey’s 2026 technology outlook identifies several of these areas among the technologies attracting strong investment momentum.
What is agentic AI?
Agentic AI refers to AI systems designed to perform multi-step tasks toward a goal, often using software tools, external information or connected applications.
Is robotics becoming more advanced in 2026?
Yes. AI is helping robotics systems improve perception, decision-making and interaction with physical environments. However, fully autonomous multipurpose robots still face significant technical and safety challenges.
What is local AI?
Local AI refers to AI models or workloads that run directly on a device rather than relying entirely on remote cloud servers.
Why is AI infrastructure important?
AI applications require powerful computing resources. AI infrastructure includes processors, memory, networking, data centers, cooling and energy systems needed to run these workloads.
Is quantum computing ready for everyday consumers?
No. Quantum computing remains an emerging field primarily focused on research and specialized computing applications.
Will AI replace technology jobs?
AI is changing many technology workflows, particularly software development, data analysis and automation. However, technology work also requires system design, security, testing, human judgment and business understanding. The effect will vary by role and organization.
How can people prepare for future technology?
Learning AI tools, cybersecurity basics, data skills, programming, automation and critical thinking can help people adapt to changes in the technology industry.
Final Thoughts
The biggest technology story of 2026 is not simply the arrival of another AI model or gadget. It is the convergence of AI with infrastructure, software, robotics, scientific research, wearable devices and advanced computing.
AI agents are becoming more connected to software tools. Data centers are expanding to support AI workloads. Robotics is bringing intelligence into the physical world, while local AI is bringing more computing directly onto devices. At the same time, researchers are exploring AI for scientific discovery and quantum computing continues to develop.
These technologies offer significant opportunities, but they also require responsible implementation. Privacy, cybersecurity, reliability, energy consumption and human oversight will remain important as technology becomes increasingly integrated into everyday life.
For consumers and businesses, the most useful approach is to focus on practical value rather than hype. Understanding what a technology can actually do—and where its limitations remain—will become increasingly important as the next generation of digital tools develops.