What will business changemakers face in 2026? a16z's latest trend observations

As one of the world's most influential venture capital firms, Andreessen Horowitz (a16z) has long stood at the intersection of technological evolution and industrial transformation. At the end of each year, a16z invites its various investment teams and partners to share their insights on "the biggest challenges that business disruptors will face in the coming year" based on their in - depth involvement in front - line sectors.

BIG IDEAS 2026 is a comprehensive presentation of these insights. It is not a prediction of a single technology but a portrayal of a new emerging paradigm, covering diverse areas such as Agent - native infrastructure, multi - modal content creation, multi - user collaborative AI, personalized systems, and AI - native education and research models.

This article selects and compiles views related to AI's capability paradigm, industrial implementation, and the evolution of educational models from the three sections of a16z BIG IDEAS 2026, aiming to outline the overall landscape of the technological system in 2026.

Vertical AI: From Information Retrieval and Reasoning to the "Multi - User Mode" Alex lmmerman

AI has driven unprecedented growth in vertical industry software. Companies in the medical, legal, and housing sectors have reached annual revenues of over $100 million in just a few years, followed closely by those in the finance and accounting sectors. The evolution has generally gone through two stages: initially focused on information retrieval (finding, extracting, and summarizing), and by 2025, reasoning ability became the key (for example, Hebbia analyzes financial reports and builds models, Basis reconciles across systems, and EliseAI diagnoses maintenance issues and schedules suppliers).

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

Currently, parties often use AI in isolation, resulting in a lack of authorized hand - offs: the AI analyzing purchase agreements does not communicate with the CFO's model adjustments, and the maintenance AI is unaware of the commitments made by on - site staff to tenants. The multi - user mode solves this problem through cross - role coordination: routing tasks to functional experts, maintaining consistent context, and synchronizing changes. Counter - party AIs can negotiate within established parameters and flag asymmetrical situations for human review; annotations from senior partners can also retrain the entire system. Tasks executed by AI will be completed with a higher success rate.

When value comes from multi - user and multi - agent collaboration, the switching cost increases. The network effect that has been difficult to establish in AI applications will become apparent here - the collaboration layer itself becomes a moat.

The First AI - native University Emily Bennett

By 2026, we expect to see the birth of the first AI - native university - an educational institution built from the ground up around intelligent systems.

In the past few years, universities have experimented with AI - assisted grading, tutoring, and course scheduling. Now, a deeper transformation is emerging: an academic organism capable of real - time learning and self - optimization.

Imagine a university where courses, academic guidance, research collaboration, and even campus operations are continuously self - adapting based on data feedback loops. The course schedule is automatically optimized, the reading list is updated daily and rewritten according to the latest research, and the learning path is adjusted in real - time based on students' rhythms and situations.

We have seen early signs. The university - wide collaboration between Arizona State University (ASU) and OpenAI has spawned hundreds of AI - driven teaching and management projects; the State University of New York (SUNY) has incorporated AI literacy into general education requirements. These are the foundations for deeper implementation.

In an AI - native university, professors will become learning architects: curating data, tuning models, and teaching students how to question machine reasoning. The assessment methods will also change. Plagiarism detection and bans will be replaced by AI - aware evaluations - instead of judging whether students use AI, it will judge how they use it. This means that transparent and prudent AI application will replace the "one - size - fits - all" simple ban as the new standard.

As industries struggle to find talent capable of designing, governing, and collaborating with AI systems, such universities will become talent training grounds for the new economy, producing graduates proficient in system orchestration and facilitating the rapid transformation of the labor force structure. This AI - native university will be the core talent engine of the new economy.

Agent - native Infrastructure Becomes the "Standard Configuration" Malika Aubakirova

By 2026, the biggest impact on infrastructure may not come from external competitors but from changes in internal corporate workloads: systems are shifting from an access mode that is "human - oriented, low - concurrency, and relatively predictable" to a new type of load that is "agent - driven, recursively triggered, sudden, and large - scale."

Today's corporate backend systems are built around a 1:1 model of "one human operation - one system response." They are not designed for an agent to break down and trigger thousands of subtasks, database queries, and internal API calls within milliseconds. Therefore, when an agent tries to refactor a codebase or process security logs, in the eyes of traditional databases and rate - limiting mechanisms, it resembles abnormal traffic or even a DDoS (a network attack that floods a target server with requests from multiple computers, causing it to crash) stress test.

Building systems for agents means redesigning the control plane. We will see the rise of "Agent - native" infrastructure: treating the "thundering herd problem" (a system inefficiency where multiple processes/requests are awakened simultaneously to compete for the same resource, but only one succeeds and the rest idle) as the default state, significantly reducing cold - start times, compressing latency fluctuations, and increasing the concurrency limit by several orders of magnitude. The real bottleneck will shift to coordination issues - routing, locking, state management, and policy execution in large - scale parallel execution. Ultimately, the competitive platforms are those that can withstand high - frequency tool calls and complex concurrent coordination.

Creative Tools Go Multi - modal Justine Moore

We already have the basic ability to tell stories with AI: generating sound, music, images, and videos. However, beyond creating one - off short clips, producing content that meets expectations consistently remains time - consuming, iterative, and sometimes even difficult to achieve - especially when creators desire the level of control similar to that of traditional directors.

A straightforward question is: why can't we input a 30 - second video into a model and have it continue the plot in the same scene, adding a new character generated based on a reference image and sound? Or have the same content presented from different camera angles, or align the picture movement with a reference video?

2026 may be the year when AI truly achieves multi - modality. No matter what form of reference content you have, you can provide it to the model to collaborate on creating new content or editing existing scenes. We have seen some early products, such as Kling O1 (a "unified" multi - modal AI video model launched by Kuaishou, which supports direct video content editing through various instructions like text and images) and Runway Aleph (a next - generation AI video model by Runway that enables smooth and consistent character and scene editing through conversational instructions), but there is still a lot of work to be done, and both the model and application layers need continuous innovation.

Content creation is one of the most powerful application scenarios for AI. I expect to see multiple successful products catering to different user groups, from meme creators to Hollywood directors.

The Year of Immersive Video Yoko Li

By 2026, video will no longer be just passive content for viewing but a space that we can truly "enter." Video models will finally be able to understand time, remember what has been presented, respond to our actions, and maintain coherence in a way similar to the physical world.

These systems will no longer just generate fragmented seconds of footage but will be able to maintain characters, objects, and physical rules over a long enough period, making actions meaningful and consequences unfold. This transformation turns video into a medium that can be "constructed": robots can be trained in it, games can continuously evolve, designers can create prototypes, and agents can learn through practice. What is presented will be more like a "living environment" rather than a fragment, beginning to bridge the gap between perception and action. For the first time, we will truly feel that we can inhabit the generated video.

The End of "Screen Time KPI" in AI Applications Santiago Rodriguez

In the past 15 years, for both consumer and enterprise applications, screen time has been the best metric for measuring value delivery: Netflix's viewing hours, the number of clicks in medical EHRs, and even the usage time of ChatGPT.

As we move towards a future with outcome - based pricing and better - aligned incentives for both supply and demand sides, the screen - time metric will be the first to be abandoned. This change is already happening in reality: when I run DeepResearch queries on ChatGPT, I hardly spend any time on the screen but gain great value; Abridge automatically records doctor - patient conversations and completes subsequent processes, and doctors hardly look at the screen; Cursor automatically completes entire application developments, and engineers are already planning the next round of features; Hebbia generates pitch materials from hundreds of public documents. These tools are freeing analysts from high - intensity repetitive work.

The challenge is that determining how much an application can charge per user will require more complex ROI measurement methods. Doctor satisfaction, developer efficiency, the well - being of financial analysts, and consumer happiness will all improve with AI applications. Companies that can explain ROI in the simplest way will continue to outperform their competitors.

World Models Become the Narrative Focus Jonathan Lai

By 2026, AI - driven world models will completely reshape the way of storytelling through interactive virtual worlds and the digital economy. Technologies like Marble (World Labs) and Genie 3 (DeepMind) can already generate complete 3D environments based on text, allowing users to explore like in a game.

As creators adopt these tools, new forms of storytelling will emerge, and it may even evolve into a "generative Minecraft," where players jointly build an ever - evolving universe. These worlds can combine game mechanics with natural - language programming, for example, with direct commands like "Create a paintbrush that turns everything I touch pink."

World models will blur the boundaries between players and creators, making users co - authors of a dynamic shared reality. An interconnected generative multiverse may emerge, with different themes coexisting and a prosperous digital economy. Beyond entertainment, these worlds will also become high - value simulation environments for training AI agents, robots, and even AGI. The rise of world models is not just a new gameplay but a new creative medium and an economic frontier.

"The Year of Me" Joshua Lu

2026 will be "the year of me" - products will stop being mass - produced for the general public and start being truly tailored for "you."

This trend is already evident. In the education sector, companies like Alphaschool are building AI tutors that adjust teaching methods according to each student's rhythm and interests, enabling every child to have a personalized educational experience. In the past, such personalized attention was only possible with a tutoring fee of tens of thousands of dollars per student.

In the health sector, AI is designing daily supplement combinations, training plans, and diet regimens based on individual biological characteristics, eliminating the need for personal trainers or laboratories.

In the media sector, AI allows creators to remix news, programs, and stories into content streams that match your personal interests and tone.

The greatest companies of the last century won by finding the "average user."

The greatest companies of the next century will win by finding the "individual within the average."

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

Accelerating Scientific Discovery Oliver Hsu

As the multi - modal capabilities of models continue to improve and robot manipulation skills progress, teams will accelerate the exploration of "autonomous scientific discovery." The combination of these two technological paths will give rise to autonomous laboratories capable of completing scientific discovery in a closed - loop - from formulating hypotheses, designing and conducting experiments, to reasoning, producing results, and iterating on the next research direction.

The teams building such laboratories will have strong interdisciplinary characteristics: integrating expertise in AI, robotics, physics and life sciences, manufacturing, and operations. Through "lights - out labs" (highly intelligent laboratories that can operate without human supervision, fully automated by machines and AI systems), continuous experiments will be carried out, driving continuous scientific discovery in multiple fields.

ChatGPT Becomes the AI "App Store" Anish Acharya

For a consumer product cycle to succeed, it usually requires three things: a new technology, new consumer behavior, and a new distribution channel.

Until recently, the AI wave had met the first two conditions but lacked a native distribution channel. Most product growth relied on existing networks (such as the social media platform X, formerly known as Twitter) or word - of - mouth.