Seven trends that will define technology in 2026

Following the tsunami caused by Generative AI in recent years, 2026 looks set to be a year of consolidation of many of the profound changes it has brought about, which are decisively affecting areas such as software design, the way companies work, and even the role humans are expected to play in increasingly complex and automated environments.

In this article, we look at seven trends that are beginning to leave the experimental phase in which they have been developed over the past two years and are expected to be part of the technology infrastructure of more and more organisations by 2026.

Some promise efficiency, others control, others simply avoid the collapse prophesied by some of the more pessimistic gurus. Taken together, they paint a clear picture: more autonomy for machines, more complexity, and less room for superficial technological decisions.

The development of agentic AI

Of all the trends that "gurus" associate with Artificial Intelligence, few arouse more enthusiasm (and some misgivings) than agentic AI; that is, moving from a model in which the user "converses" with a chatbot or enters a prompt in a search engine, to a model in which the user "talks" with achatbot or enters a prompt in search of a result, to one in which artificial intelligence itself acts on the user's behalf in all kinds of everyday tasks, both at home and in the enterprise.

For example, an agentic AI could be a model that checks your diary, detects that you have an important dinner to go to, books a table at a restaurant it knows you will like, orders a taxi, sets your alarm and reminds you to leave home early because there is traffic.

When it comes to going on holiday, another model plans the best routes according to the destination you want to visit, reorganises plans in case of incidents, changes a possible reservation and proposes alternatives in real time; in the company, the model that receives a specific business objective is capable of dividing tasks, assigning responsibilities, following up and even proposing meetings if something is blocked .

At the moment there are some agentic AI developments that move in controlled environments and mostly in a pilot phase: AI already has the ability to plan, use tools and execute actions, but issues such as who responds if the AI makes a mistake, how its decisions are audited, or how far they can act without human permissionhave not yet been solved .

What do we expect to see in 2026? Probably standardised agentic AI in very specific domains (financial, logistical, energy consumption, support agents) that will start making real decisions and execute routine actions, leaving humans as strategic supervisors. What we will not yet see are general agents managing complex tasks.

Cobots 2.0: humanoid robots in companies

In 2025, humanoid robots have gone from the theoretical plane to flooding social networks and platforms such as Youtube with all kinds of promises of what they will be able to do... in the future. Robots such as Optimus, Atlas or Figure have even begun to leave the "laboratory", to start "working" in pilot projects in multinationals such as Amazon, which has not tired of proclaiming that in the very near future, robots and humans will share the same workspace.

It is true that in the last year, the motor skills of robots have improved, especially in basic manipulation and balance, but we are still talking about expensive, fragile robots with little autonomy, useful mainly for demonstrations, proofs of concept and very specific repetitive tasks. In real factories, traditional cobots are still much more reliable and cost-effective.

In this sense, we will not see an invasion of humanoids walking around industrial plants in 2026, but we will certainly see the first limited and controlled commercial deployments in warehouses and logistics centres, where they will take on "hazardous tasks" (chemical or thermal risk areas, night-time logistics, maintenance in critical infrastructures) where human costs are high.

In addition to improving their motor skills, the big leap for these robots is expected to be their integration of agentic AI systems, which, once refined, will allow them to execute work sequences with less human supervision. Even so, they will remain specialised tools, not generalist workers.

Green AI: doing more... but consuming less

The development of AI has in many cases meant saying goodbye to meeting companies' environmental targets. The zero emissions strategies that many organisations promised for 2030 have ended up being little more than well-intentioned declarations, abandoned in favour of much more ambitious (and polluting) business objectives.

But even from a business logic, the problem in 2025 has become more than obvious: data centres consume amounts of energythat are hard to justify even for big tech, and training larger and larger models is starting to yield diminishing returns.

Until now, part of the answer has been to make a strong commitment to renewable energy. However, this is no longer enough. The key in 2026 will be tostart designing models that do more with less. This is what has begun to be called Green AI: smaller models, selective training, optimised inference and algorithms that prioritise efficiency over brute force.

Being efficient in 2026 will no longer be a competitive advantage but a basic requirement. Companies that do not optimise consumption simply do not scale.

Synthetic data to further train AI

Few technologies are as voracious as Generative Artificial Intelligence. In the last year, it is estimated that large models havescraped almost all the "useful" public text from the Internet, which is to say that virtually all the human knowledge that has ever been published on the Web has already been "gobbled up" by ChatGPT, Gemini and other algorithms.

And while the AI continues to train itself with new (newly created) knowledge, it also incorporates (and at high speed), "contaminated content", i.e. SPAM, hallucinations created by other AIs, misinformation and hoaxes. In other words, continuing to scrap the web as we have been doing is not only becoming inefficient (as well as generating more and more copyright infringement lawsuits), but also worsens the quality of the results.

The solution? For many experts, training AIs with synthetic data: information artificially generated by computers or AIs to simulate real scenarios, without relying on real people or existing records. These can be images, text, audio, medical records or financial transactions, created following statistical patterns or behavioural rules that mimic reality. The idea is to make this data sufficiently similar to real data without privacy risks or direct biases associated with specific individuals.

The challenge will not be technical, but epistemological: how to prevent AIsfrom learning increasingly self-contained and artificial versions of reality. If the data is well designed, AI can infer behaviours and solutions that were not explicitly in any original data. However, if synthetic data only replicates what already exists, then AI is not really creating new knowledge, only refining known patterns.

Spatial computing light: the beginning of the end of screens

In the last decade, the smartphone has emerged as the absolute dominator of consumer technology. Our pocket computer has long been the device we use most, the one that holds all our "secrets" and the one we are least willing to give up.

So in 2025, the question is obvious: can we develop something to replace the smartphone? How about an almost "invisible" technology that allows us to move away from the ubiquitous screens?

Thanks to the efforts of companies like Apple or Meta and devices like Meta Quest and Vision Pro, we have seen what can be done with AR/VR, but its size, weight and power consumption limit its daily adoption. In parallel, prototypes are already being developed for discrete glasses with retinal projection or directional audio that display information without blocking normal vision, and non-invasive neural wearables (bracelets, rings, gloves) capable of reading basic motor intentions (Meta Ray Ban Display). It is to be expected that in 2026 these types of devices will start to reach a very early adopteraudience , technology enthusiasts who, in apparently normal glasses, will have basic AR functionalities: notifications, translation or contextual navigation.

The backend is decentralised

Looking ahead to 2026, the edge-firstapproach is emerging as the dominant model for modern application design, definitively displacing the classic centralised backend architecture .

In this paradigm, business logic no longer lives comfortably on a single server and is fragmented into lightweight functions that run as close to the user as possible. It is no longer just the frontend that is optimised: authentication, personalisation, validation and data access happen at the edge, not in a remote data centre.

The less obvious consequence is that the backend becomes more complex at a conceptual level. Replicating data, resolving conflicts, working with eventual consistency and designing fault-tolerant systems is no longer the exclusive territory of system architects. In 2026, the full-stack developer will need to understand distributed databases, global caches and serverless functions with strict time and memory limits. In return, you get something very concrete: applications that are faster, more resilient and less dependent on a single point of failure.

Consolidating predictive defence

Looking ahead to 2026, predictive cyber security is emerging as the dominant approach to traditional defence based on delayed alerts. Using AI models trained on large volumes of traffic, user behaviour and attack patterns, security platforms are able to identify anomalies before they materialise into an actual intrusion .

This enables automatic real-time responses, such as isolating services, dynamically blocking identities or reconfiguring access policies, without human intervention and without stopping the application.

In parallel, confidential computing redefines data protection by extending encryption to runtime. Thanks to hardware-supported secure enclaves, sensitive information remains protected even as code processes it, drastically reducing the attack surface. For full-stack development, this means taking on zero-trust environments, designing APIs that operate on encrypted data, and understanding how to deploy critical logic in secure contexts.