Was erwarten die Geschäftstransformatoren im Jahr 2026? Die neuesten Trendbeobachtungen von a16z

As one of the world's most influential venture capitalists, Andreessen Horowitz (a16z) has long been at the intersection of technological development and industrial change. At the end of each year, a16z invites its various investment teams and partners to present their assessments of the "biggest challenges that business transformers will face in the coming year" from the areas in which they are actively involved.

BIG IDEAS 2026 is a concentrated presentation of these assessments. It is not about predictions for a single technology but about describing a whole set of emerging new paradigms, ranging from agent-native infrastructures, multimodal content creation, collaborative AI for multiple people to individualized systems, AI-native education and research.

This article is based on the contents of the three parts of a16z BIG IDEAS 2026. The views on AI capability paradigms, industrial implementation, and development of education models have been selected and summarized to provide an overview of the possible technological system landscape in 2026.

Vertical AI goes from information extraction and inference to the "multi-player mode" Alex lmmerman

AI has brought the growth of software for vertical industries to an unprecedented level. Companies in the medical, legal, and real estate sectors have achieved annual revenues of over $100 million within a few years, closely followed by the finance and accounting sectors. The development has generally gone through two phases: Initially, information extraction was in the foreground (finding, extracting, summarizing), while from 2025, inference ability became crucial (e.g., Hebbia analyzes business reports and creates models, Basis conducts calculations between systems, EliseAI diagnoses maintenance problems and plans suppliers).

In 2026, vertical AI will unlock the "multi-player mode." Vertical software has industry-specific interfaces, data, and integration capabilities, but the work in vertical industries is essentially a collaboration between multiple parties. If agents are to actually represent the work, they must collaborate. From buyers and sellers to tenants, advisors, and suppliers, each party has different access permissions, processes, and compliance requirements that only vertical software can understand.

Today, parties often use AI in isolation from each other, resulting in a lack of authorization transfer: An AI that analyzes purchase contracts does not communicate with the adjustment of the CFO's model, and a maintenance AI does not know what promises the on-site employee has made to the tenant. The multi-player mode solves this problem through coordination between different roles: Tasks are forwarded to experts, the context remains consistent, and changes are synchronized. Opposing agents can negotiate within given parameters and mark asymmetric situations for human review; Notes from leading partners can train the entire system backwards. Tasks performed by AI are completed with a higher success rate.

If the value comes from the collaboration between multiple people and agents, the switching costs increase. The network effect, which has been difficult to establish in AI applications so far, becomes visible here – the collaboration layer becomes the protective wall.

The first AI-native university Emily Bennett

By 2026, we expect the establishment of the first AI-native university – an educational institution that is entirely built on intelligent systems.

In recent years, universities have already experimented with AI-assisted evaluation, counseling, and scheduling, but now a more profound change is emerging: an academic organism that learns in real-time and optimizes itself.

Imagine a university: Curricula, student counseling, research collaboration, and even campus management are continuously adjusted based on data feedback. The schedule is automatically optimized, reading recommendations are updated daily and rewritten based on the latest research, and learning paths are adjusted in real-time to the rhythm and situation of the students.

We have already seen the first signs of this. The partnership between Arizona State University (ASU) and OpenAI has produced hundreds of AI-assisted teaching and administrative projects; the State University of New York (SUNY) has incorporated AI competence into general education. These are the foundations for a more profound implementation.

At an AI-native university, professors will become learning architects: They plan data, optimize models, and teach students how they can question the logical steps of machines. The evaluation method will also change: Detecting and blocking plagiarism will be replaced by an AI-sensitive evaluation – instead of judging whether students use AI, it will be judged how they use it. This means that a transparent and careful use of AI instead of a simple ban policy will become the new standard.

While all industries are looking for talents who are able to design, manage, and collaborate with AI systems, this type of university will become the training ground for new economic talents and promote the rapid transformation of the workforce. This AI-native university will become the central engine for talent development in the new economy.

Agent-native infrastructure will become the "standard equipment" Malika Aubakirova

By 2026, the greatest impact on infrastructure may not come from external competitors but rather from changes in the internal workloads of companies: The systems are changing from an access pattern that is "for humans, with low competition, and relatively predictable" to a new type of load that is "agent-driven, recursively triggered, and sudden and massive."

Today's corporate backend systems are built on a 1:1 pattern, where human intervention triggers a system response. They are not designed to process thousands of subtasks, database queries, and internal API calls that are decomposed and triggered by an agent in milliseconds. When an agent tries to restructure a code repository or process security logs, it looks more like abnormal data traffic or even a DDoS test (a network attack in which thousands of computers send requests simultaneously to paralyze a server) in traditional databases and throttling mechanisms.

Building systems for agents means that the control layer must be redesigned. We will witness the emergence of "agent-native" infrastructures: The "Thundering Herd" phenomenon (in which multiple processes/tasks are awakened simultaneously to compete for the same resource, but only one is successful and the others run in vain) will be regarded as the standard state, the cold start time will be greatly reduced, latency fluctuations will be reduced, and the competition limit will be increased by several orders of magnitude. The real bottleneck lies in the coordination problems – routing, locking, state management, and the execution of strategies in massive parallel execution. In the end, it is the platforms that can handle frequent tool calls and complex competition coordination that are competitive.

Creative tools will become multimodal Justine Moore

We already have the basic ability to tell stories with AI: We can generate voices, music, images, and videos. But when it comes to the stable creation of content outside of single short films, it is still time-consuming, laborious, and sometimes impossible – especially when the creative desires a control similar to that of a traditional director.

An obvious question is: Why can't we input a 30-second video into a model and ask it to continue the plot in the same scene and add a new character based on a reference image and sound? Or have the same video played from different camera positions or adapt the movement of the image to a reference video?

2026 could be the year when AI truly becomes multimodal. No matter what kind of reference content you have, you can give it to the model and collaborate with it to create new content or edit existing scenes. We have already seen some early products, such as Kling O1 (a "universal" multimodal AI video model from Kuaishou that supports the direct editing of video content via text, images, and other commands) and Runway Aleph (a new AI video model from Runway that enables the smooth and consistent editing of characters and scenes via dialog-shaped commands), but there is still a lot of work to be done, both on the model and application sides.

Content creation is one of the most effective application areas of AI. I expect several successful products to emerge that appeal to different user groups, from emoji designers to Hollywood directors.

The year of entering videos Yoko Li

By 2026, video will no longer be just passively consumed but will become a space that we can actually "enter." Video models will finally be able to understand time, store already presented content, react to our behavior, and maintain a coherence similar to that in the real world.

These systems no longer only generate isolated frames but can maintain characters, objects, and physical rules over a sufficiently long period of time, so that actions make sense and consequences can occur. This change makes video a medium that can be "built": Robots can be trained in it, games can be continuously developed, designers can create prototypes, and agents can improve through practical learning. The result is no longer like a fragment but more like a "living environment" that closes the gap between perception and action. For the first time, we will really feel that we can live in the generated videos.

The end of the "screen time KPI" in AI applications Santiago Rodriguez

In the past 15 years, screen time in both consumer and enterprise applications has been the best measure of value creation: The viewing time on Netflix, the number of clicks in medical EHR systems, and even the usage time of ChatGPT.

With the development towards a pricing based on results and a better alignment of incentives between providers and users, the screen time KPI will fall first. We are already seeing this change in reality: When I perform DeepResearch queries in ChatGPT, I spend almost no time on the screen, but I get a great benefit; Abridge automatically records doctor-patient conversations and takes care of the subsequent processes, so that doctors hardly ever look at the screen; Cursor automatically creates an entire application, so that software developers can already work on the next function; Hebbia generates presentation material from hundreds of public documents. These tools free analysts from tedious, repetitive tasks.

The challenge is that the calculation of the achievable price per user of an application will depend on a more complex ROI measurement. The satisfaction of doctors, the efficiency of developers, the mental state of financial analysts, and the well-being of consumers are improved by AI applications. The companies that can explain the ROI in the simplest way will still be ahead of their competitors.

World models will become the focus of storytelling Jonathan Lai

By 2026, AI-driven world models will completely change the way of storytelling through interactive virtual worlds and the digital economy. Technologies such as Marble (World Labs) and Genie 3 (DeepMind) can already generate complete 3D environments from texts that users can explore like in a game.

With the adoption of these tools by creatives, new forms of storytelling will emerge and may even evolve into a "generative Minecraft" in which players together build a constantly evolving universe. These worlds can combine game mechanics with natural language programming, e.g., with a direct command: "Create a brush that colors everything I touch pink."

World models will blur the boundary between players and creatives and make users co-authors of a dynamic, shared reality. A networked, generative multiverse could emerge in which different themes exist side by side and the digital economy thrives. In addition to entertainment, these worlds will also become high-quality simulation environments for the training of AI agents, robots, and even AGI. The rise of world models is not only a new way of playing but a completely new creative medium and an economic frontier.

"My year" Joshua Lu

2026 will be the "year for me" – Products will no longer be produced for the masses but really developed for "you."

This trend can be seen everywhere. In the education sector, companies like Alphaschool build AI trainers that adapt the teaching method to the rhythm and interests of each individual student, so that each child gets an individual learning experience; in the past, this individual care was only possible if thousands of dollars in teaching costs were spent on each student.

In the health sector, AI develops individual combinations of dietary supplements, training plans, and nutrition plans based on a person's biological characteristics without the need for a personal trainer or laboratory tests.

In the media sector, AI enables creatives to mix news, shows, and stories into a content stream that matches your personal interests and style.

The largest companies of the last century have distinguished themselves by finding the "average user."

The largest companies of the coming century will be characterized by finding the "average individuals."

In 2026, the world will stop optimizing for everyone and instead optimize for you.

Acceleration of scientific discoveries Oliver Hsu

With the continuous improvement of the multimodality of models and the progress in robotics, teams will accelerate the exploration of "autonomous scientific discovery." The combination of these two technologies will lead to autonomous laboratories that can handle the entire process of scientific discovery in a closed loop – from formulating hypotheses, planning and conducting experiments to deriving conclusions, generating results, and planning the next research direction.

The teams that build such laboratories will...