The Technological Trends That Will Define 2026: A Comprehensive View from Multiple Perspectives

Technology investors should anticipate the future by studying every aspect of the industry. This analysis gathers key perspectives on how artificial intelligence, data infrastructure, and new forms of digital interaction will transform the business landscape in 2026.

Data Chaos: From Problem to Opportunity

Navigating Multimodality

Organizations are drowning in an avalanche of information: PDFs, videos, emails, screenshots, and scattered logs. The real bottleneck is not data processing capacity but the entropy surrounding it. 80% of business knowledge resides in unstructured data, where freshness and authenticity are constantly degrading.

RAG systems fail, agents make subtle but costly errors, and critical flows still require manual inspection. The speculative function of AI promises to solve this but needs clean data as a foundation.

Startups that manage to build platforms capable of extracting structure from documents, images, and videos—resolving conflicts, repairing pipelines, and maintaining recoverability—will dominate enterprise knowledge management. Applications are everywhere: contract analysis, onboarding processes, claims, compliance, engineering search, and all agent flows dependent on reliable context.

Revitalizing Cybersecurity

The cybersecurity sector faced a paradox over the last decade: although vacancies grew from less than 1 million (2013) to 3 million (2021), the problem was not a real talent shortage but tedious work. Teams bought tools capable of detecting everything, generating an unsustainable volume of alerts that no human could review efficiently.

By 2026, artificial intelligence will break this vicious cycle by automating repetitive tasks. When manual work is reduced, security professionals can finally focus on what truly matters: pursuing real threats, designing new systems, and patching vulnerabilities.

Native Infrastructure for Agents

The most profound architectural change in 2026 will be recognizing that traditional enterprise backend systems are not prepared for autonomous agents. Current systems were designed for a 1:1 relationship: a user initiates an action, the system responds.

Agents operate radically differently. An agent goal can trigger 5,000 subtasks, database queries, and API calls in milliseconds. For traditional databases, this resembles a DDoS attack. The bottleneck is not computing power but coordination: routing, blocking, state management, and policy execution.

The “avalanche of executions” is the default state. Cold start times must be drastically reduced, latency variability nearly eliminated, and concurrency limits multiplied. Only platforms capable of managing this chaos at scale will succeed.

Creativity Enters the Multimodal Era

Now we have the building blocks: voice, music, images, and video generation. But achieving complex results remains slow and frustrating. Why not feed a model a 30-second video and have it continue the scene with characters generated from references? Or view the same video from different angles?

2026 will be the year of true multimodal unlocking. You will provide the model with any type of reference content, and it will use it to create new content or edit scenes. The first products (Kling O1, Runway Aleph) show the way, but much ground remains to be conquered.

Content creation is one of AI’s most powerful use cases. Expect an avalanche of products for all kinds of scenarios: from memes to Hollywood productions. Fine control and visual coherence will be the differentiators.

Video Stops Being Passive

By 2026, video will cease to be content you watch and become a space where you are truly present. Models will finally understand time, remember what they already showed, react to your actions, and maintain reliable coherence.

They will no longer generate just fragments but environments that sustain characters, objects, and physics long enough for actions to have consequences. A robot will practice a task, a game will evolve, a designer will prototype, an agent will learn in practice.

For the first time, you will feel inside the video you generated. Video becomes a living medium, not a static clip.

Logging Systems Lose Relevance

The real disruption in enterprise software by 2026 will be the slow death of “logging systems” as centers of value. AI can read, write, and reason directly over operational data, transforming ITSM and CRM from passive databases into autonomous workflow engines.

Traditional systems become generic persistence layers. The interface turns into a dynamic layer of agents. The strategic advantage will belong to those who control the environment where agents are executed daily by employees.

Vertical Intelligence: From Search to Collaborative Work

Vertical software has driven unprecedented growth. Healthcare, legal, and real estate companies reached >$100M in ARR in years. First came information retrieval: search, extract, summarize. 2025 brought reasoning: Hebbia analyzes financial statements, Basis reconciles spreadsheets.

2026 will unlock true multi-user collaboration. Vertical work is inherently collaborative: buyers and sellers, tenants and providers, advisors and contractors. Each party needs different permissions and compliance flows, which only vertical software understands.

Today, each party uses AI independently, causing lack of synchronization. As the value of multi-agent collaboration scales, so will the cost of change. We will see network effects never before achieved: the collaboration layer will be the defensive moat.

Designing for Agents, Not Humans

In 2026, people will interact with the web through agents. Content optimized for human consumption will no longer be relevant for machines. For years, we optimized for Google ranking, Amazon appearances, initial TL;DR.

But agents will read the fifth page effortlessly. Content humans would never see, they will process completely. This change revolutionizes design: from visual interfaces to machine readability.

Engineers no longer look at Grafana dashboards; AI SREs interpret telemetry and publish analyses on Slack. Sales teams no longer search in CRM; AI automatically extracts patterns. We no longer design for humans but for machines.

The End of Screen Time as a Metric

For 15 years, screen time was the ultimate metric. Streaming on Netflix, clicks on medical records, time on ChatGPT: all critical KPIs. But result-based pricing models are changing this.

You run Deep Research on ChatGPT and get enormous value without looking at the screen. Abridge magically captures medical conversations and performs follow-ups without the doctor seeing anything. Cursor develops end-to-end applications while engineers plan the next feature.

AI adoption will increase medical satisfaction, developer efficiency, and analyst well-being. Companies that articulate ROI most clearly will dominate. Per-user fees require more complex ROI measurement: screen time is no longer enough.

Healthy Monthly Active Users

The traditional healthcare system serves chronic patients, critical patients, and healthy young people who rarely seek care. A fourth group emerges: “healthy monthly active users”—people who want to monitor their health regularly without being sick.

They are probably the largest consumer segment. Reimbursement systems, focused on treatment, do not reward prevention. But with AI reducing costs and new insurance products focused on prevention, companies will begin to serve this group massively.

They are engaged users, driven by data, focused on prevention. The modern data stack must evolve to support this.

AI-Native Data Stack

The data ecosystem has matured significantly but remains in early stages of truly AI-native architecture. Consolidation continues (Fivetran/dbt, Databricks on the rise). Now, data and AI infrastructure become inseparable.

Important directions: data flow into high-performance vector databases alongside structured data, AI agents resolving the “context problem” for continuous access to correct business data, BI tools and spreadsheets transforming as data flows automate.

Virtual Worlds and Generative Narrative

In 2026, AI-driven world models will revolutionize storytelling. Technologies like Marble and Genie 3 generate complete 3D environments from text, explorable like video games. These will be “generative Minecraft” worlds where players co-create ever-evolving universes.

Creators will earn income by creating assets, guiding novices, developing tools. Beyond entertainment, these worlds will be rich simulations for training AI agents and robots. The rise of world models marks the emergence of a new kind of game, a creative medium, and an economic frontier.

Radical Personalization: Year One

2026 will be the year products cease mass production and become fully customized. In education, AI tutors like Alphaschool adapt to each student’s pace and interests. In health, AI designs personalized supplement, exercise, and diet plans based on physiology. In media, creators reconfigure news and stories for fully personalized flows.

The biggest companies of the last century succeeded by finding the average consumer. The next century’s winners will succeed by finding the individual within the average. In 2026, the world stops optimizing for everyone and begins optimizing for you.

Toward an AI-Native University

Expect to see in 2026 the birth of the first university built from scratch around AI systems. Universities have applied AI to grading and tutoring, but something deeper is emerging: an adaptive academic system that self-optimizes in real time.

Imagine: courses, advising, research collaboration, and building management continuously adjusting. Schedules auto-optimize. Reading lists rewrite themselves each night as new research emerges. Learning paths adapt in real time.

Professors will be architects of learning, responsible for data and teaching students how to question machines. Plagiarism detection tools will give way to AI-awareness assessments: grading will depend not on whether you use AI but on how you use it.

This university will be a talent engine, producing experts in coordinating AI systems for an evolving labor market. It will be the foundation for training the new economy.