In 2026, AI and modern data stacks will rebuild enterprise infrastructure

A16z’s annual “Big Ideas” report is once again attracting attention this year. Multiple investment teams analyzing the technology industry in 2026 have identified a major shift: AI is no longer just a set of individual tools but is becoming the infrastructure of entire companies. In particular, the evolution of the modern data stack is at the core of this transformation.

Over the past year, breakthroughs in AI have shifted significantly from model performance improvements to system-level functionalities. Capabilities necessary for real-world operations—such as understanding long-term time series, maintaining consistency, executing complex tasks, and coordinating among multiple agents—have begun to emerge. Correspondingly, the focus of industry-wide innovation is moving from isolated breakthroughs to a comprehensive redefinition of infrastructure, workflows, and user interaction methods.

Agent-Led Infrastructure Transformation

Enterprise backend systems face significant challenges. Current architectures are designed around a one-to-one model: “human action → system response.” However, once intelligent agents start operating, the situation changes dramatically.

A single “instruction” can trigger chain reactions involving 5,000 subtasks, database queries, and internal API calls. This resembles recursive “attacks” occurring in milliseconds. Traditional rate limiters and databases may behave almost like DDoS attacks under such patterns.

To address this, a complete redesign of the control plane is necessary. Agent-native infrastructure will begin to emerge rapidly. Shortening cold starts, reducing latency, and vastly increasing parallel processing capacity will become essential requirements. Ultimately, only platforms capable of handling floods of tool calls will survive the competition.

Frontlines of Modern Data Stack Evolution

Processing unstructured multimodal data remains the biggest bottleneck for enterprises. Countless companies are overwhelmed by PDF documents, screenshots, videos, logs, emails, and semi-structured “data slush.” While models are becoming increasingly intelligent, input data is becoming more chaotic.

Many issues with RAG systems hallucinating and intelligent agents making costly errors stem from this. In the unstructured world that accounts for 80% of corporate knowledge, data freshness, structure, and reliability are constantly declining.

This data entropy is the true limiting factor for modern AI companies. Over the past year, the modern data stack has clearly become more integrated. Mergers like Fivetran/dbt and expansions by Databricks exemplify a shift from modular services to integrated platforms.

However, the realization of truly AI-native data architectures is still in its early stages. By 2026, the modern data stack is expected to evolve rapidly in the following areas:

First, mechanisms for continuous data inflow into high-performance vector databases will be established. Deep integration of data and AI infrastructure will accelerate, forming new layers beyond structured storage.

Next, AI agents will enter a stage where they solve the “context problem.” Continuous access to correct data semantics and business definitions will enable consistent understanding across multiple systems.

Furthermore, the automation and intelligence of data workflows will be advanced, raising questions about how traditional BI (business intelligence) tools and spreadsheets will evolve. The era will come when there is no need to stare at Grafana, as AI automatically analyzes telemetry and provides insights via Slack.

Autonomous Enterprise Software

The true transformation of enterprise software will stem from fundamental structural changes. The central roles of ITSM, CRM, and other record-keeping systems will finally begin to decline.

AI is bridging the gap between “intent” and “execution.” As models can read, write, and infer directly from enterprise operational data, systems that were once passive databases will evolve into autonomous workflow engines.

Rapid advances in inference models and agent workflows mean these systems will not only respond to requests but also predict, coordinate, and execute end-to-end processes.

Interfaces will become a layer of dynamic intelligent agents, while traditional system record layers will retreat to “cost-effective persistent storage.” Strategic advantage will be inherited by players who control these intelligent execution environments.

The Era of Multi-Party Collaboration with Vertical AI

Vertical AI is experiencing explosive growth in healthcare, legal, and housing sectors. Several companies already have ARR (annual recurring revenue) exceeding $100 million, with finance and accounting sectors following suit.

The first revolution was in information retrieval—search, extraction, and summarization. By 2025, inference was introduced, enabling complex business analysis. Hebbia analyzes financial statements, Basis reconciles trial balances across multiple systems, and EliseAI diagnoses maintenance issues and creates supplier schedules.

In 2026, the “multiplayer mode” will be unlocked. Work in vertical industries is inherently collaborative among multiple stakeholders—buyers, sellers, tenants, consultants, and suppliers—each with different permissions, processes, and compliance requirements.

Currently, AI for each stakeholder operates independently, leading to confusing points of authority delegation. Multiplayer AI will enable automatic coordination among stakeholders, maintain context, synchronize changes, route functions to experts, and implement asymmetric tagging for human review.

As collaboration among multiple agents and humans improves transaction quality, switching costs will rise sharply. This collaborative network will become the “moat” (competitive advantage) that AI applications have long lacked.

Personalization of User Experience

2026 will be “Your Year.” Products will no longer be mass-produced for the “average consumer” but will be customized for “you.”

Until now, companies optimized for predictable human behaviors—Google rankings, Amazon top product lists, news summaries, eye-catching headlines. But by 2026, intelligent agents will instead acquire and interpret content on behalf of humans.

Humans might miss deep insights buried on page 5, but intelligent agents will not. Software will adapt accordingly. The importance of visual design in applications will decline, while machine readability will become more critical.

In education, AI tutors will guide students at their own pace and according to their interests. In health, AI will customize supplements, exercise plans, and meal plans. In media, content will be remixed in real-time to match user preferences.

The giants of the last century succeeded by finding the “average user.” The giants of the next century will succeed by finding the “individual.”

New Trends in Healthcare and Media

In healthcare, a new user group called “Healthy MAU” (monthly active users who are healthy but active) will become central.

Traditional medicine has primarily served three types of people: Unwell MAU (high-cost, cyclical demand), sick DAU (long-term severe patients), and healthy YAU (rarely seek medical care). Healthy YAU can easily become sick MAU/DAU, but preventive care could delay this transition. However, the current “treatment-focused” healthcare system offers little coverage for proactive testing and monitoring.

The emergence of Healthy MAU will change this structure. They are not ill but are willing to monitor their health regularly, representing potentially the largest population segment. As AI reduces healthcare delivery costs, preventive insurance products emerge, and users willingly pay for subscription services, Healthy MAU will become the most promising customer group for next-generation health tech—data-driven, preventive, and continuously active.

Meanwhile, in video media, by 2026, videos will no longer be passive viewing content but will begin transforming into immersive spaces where users can “enter.” Video models will finally understand time, remember presented content, and respond to user actions.

These systems will be able to maintain characters, objects, and physical laws over long periods, enabling actions to have genuine impact and causal relationships to unfold. Videos will evolve from mere media into spaces capable of constructing a wide variety of experiences.

Interactive Media and Adaptive Education

As world model technologies (like Marble, Genie 3, etc.) begin generating complete 3D worlds from text, new forms of storytelling will emerge as creators adopt them. Environments similar to a “general-purpose Minecraft” where players collaboratively create vast, evolving worlds may appear.

The boundaries between players and creators will blur, forming shared dynamic realities. Different genres will coexist, and the digital economy will flourish, allowing creators to earn income through asset creation, player guides, and interactive tools.

Generated worlds will also serve as training grounds for AI agents, robots, and future AGI. World models will not only spawn new game genres but also create entirely new frontiers in creative media and economy.

In education, truly AI-native universities are on the horizon. While traditional universities already utilize AI for grading, personalized tutoring, and scheduling, deeper transformations are underway—namely, “adaptive academic organizations.”

Imagine universities where courses, mentorship, research collaboration, and campus operations are all adjusted in real-time and optimized based on feedback. In AI-native universities, professors will become “learning system designers,” and evaluation methods will shift toward “AI recognition.” Instead of asking students whether they used AI, emphasis will be placed on how they used it.

Integrating the Structural Changes of 2026

The common thread among these trends is clear: AI is graduating from being just a tool to becoming a system that redefines entire infrastructures, workflows, and user interactions within companies.

The evolution of the modern data stack is at the core of this overall transformation. Without ensuring data quality, structure, and accessibility, intelligent agents cannot function. Simultaneously, agent-led workflows must be realized to maximize the value of the modern data stack.

Performance KPIs will also change. Over the past 15 years, “screen time” was the gold standard for measuring product value. But with the advent of outcome-based pricing, screen time will be completely phased out. More sophisticated ROI metrics—such as physician satisfaction, developer productivity, and user satisfaction—will become more important.

The companies that can most effectively communicate their ROI story will continue to succeed. Many of these ROI sources will stem from the integration of data infrastructure and AI agents.