The key to winning or losing in AI videos has just been clarified by a16z: In the future, it all boils down to the "invisible post-production team."

In the past two days, Seedance 2.0 has completely gone viral.

In the view of Feng Ji, the founder of Game Science, Seedance 2.0 has brought about a key change: all display methods that previously required repeated consideration of production costs will be quickly "video - ized". E - commerce advertisements, brand materials, and pre - shot content are the first to be affected.

So, when the production barriers disappear, how will AI reconstruct the video workflow?

Today, we focus on a key insight of Justine Moore, a partner at a16z, in the field of AI video. As one of the most active early - stage investors in the application layer of Silicon Valley's AI, she has led investments in a number of landmark projects such as ElevenLabs and Krea, and has continuously released annual consumer - grade AI trend reports, showing strong forward - looking judgment on the evolution of creative tools.

Justine's core conclusion is that in the next stage, what will truly make a difference is not the generation layer, but the "editing layer". And AI Agents are quietly evolving into that invisible "post - production team".

In her view, three conditions have almost matured simultaneously: first, the ability of large visual models to understand content semantics and narrative structures; second, the scheduling and collaboration capabilities of multi - modal tools; third, the leap in the stability and aesthetic quality of generative models.

When these three points simultaneously cross the critical threshold, AI is no longer just "providing materials", but starts to coordinate processes, refine details, calibrate rhythms, and even shape taste to a certain extent. A workflow centered around "AI editing agents" is taking shape.

Next, we will break down this technological inflection point from five aspects: how exactly do AI agents reconstruct the complete chain of video creation, and why will it become the next real competitive high - ground.

01

When the Explosion of AI Video Meets the Dilemma of Creation

The year 2025 is known as the "Year of Video". AI - generated advertisements have become the mainstream, and the launch videos of some seed - stage startups can even receive millions of views; video podcasts and interviews have also witnessed explosive growth, and the ubiquitous screens are being occupied by dynamic images.

However, behind this prosperity lies a long and cumbersome behind - the - scenes work. Refining 90 minutes of raw footage into a 3 - minute short film; painstakingly correcting lighting and audio in post - production; repeatedly searching for the right sound effects, these are the daily routines of video creation.

There is an "80/20 rule" in video production: you will spend 80% of your time and energy on editing and 20% on shooting (now generation). This is a test of "taste" - how to tell a story, how to control the rhythm, and how to touch the audience. Creating truly engaging videos remains a painstaking process that requires great patience and professional judgment.

We now have the technology to delegate some tasks to AI agents, which can help us produce shot and generated content. Large visual models can watch and understand a large amount of video footage. Agents can analyze, plan, and use editing tools on your behalf. We have enough training data to teach the models what makes a good video.

AI video agents will significantly increase the supply of high - quality videos. This type of content currently takes professional video editors days or even weeks to produce. Just as Cursor revolutionized programming, these agents will also revolutionize video production.

02

How Does AI Take Over the "Dirty and Tiresome Work" of Video Editing?

There is a huge market demand for AI agents that can enable anyone to have the skills and taste of a professional video editor. So, why haven't such products become popular yet? Some recent developments are driving the change:

Large visual models can now process a large amount of video. You have to understand the video before you can edit it. This is no easy challenge - even a very short clip requires processing a large amount of information.

We have seen great progress in recent large - language models such as Gemini 3, GPT - 5.2, Molmo 2, and Vidi2, which are essentially multi - modal with longer context windows.

Gemini 3 can now process videos up to an hour long! You can upload it as input and let the model generate timestamp labels, find specific moments, or simply summarize what happened.

The models have learned to use tools. An AI editor needs to be able to perform operations, not just make suggestions. We have seen substantial progress in large models acting as agents that can truly use tools.

One of my favorite examples is Claude using Blender (a 3D creation software), a complex tool that many people find difficult to master. You can imagine how many possibilities there will be when agents can use more tools.

The quality of image and video generation models has improved. I firmly believe that the future video production process will be hybrid - combining AI - generated and real - shot content.

Imagine shooting interviews for a documentary but using AI to generate establishing shots or historical images; or applying an animation reference to a real - life character using a motion transfer model. For these methods to be truly useful, the models must meet certain quality and consistency standards. And now, this is becoming a reality.

What can these AI agents do?

Here are some examples of the types of tasks they can handle for us:

First, process management. Whether it is real - shot or generated, the amount of footage you end up with often far exceeds what you need (sometimes hundreds of times more. Think about how many "alternate takes" there are in a movie or a TV series).

Organizing, screening, and deciding which footage to use is often a challenge. Products like Eddie AI can process hours of uploaded videos, identifying main shots and establishing shots, handling multi - angle camera positions, and comparing shots.

Second, multi - model orchestration. If many future videos contain AI - generated elements, we will need agents that can coordinate all the models.

For example, to add AI animations to an educational video, you need an agent to generate images, send them to a video model, and stitch the outputs together. Products like Glif are launching agents that can coordinate work among multiple models on behalf of users.

Third, detail refinement. It is the correction of details that takes a video from being just okay to being excellent.

But if you are not a professional editor, you may be overwhelmed by the huge number of fine - tuning tasks. For example, adjusting the lighting between clips, removing noise from the audio track, or eliminating filler words like "um" and "ah" in an interview. Products like Descript's Underlord agent can take over the video, make all these modifications, and deliver the final version.

Fourth, format adjustment. After video production is completed, adjustments are often required to increase its reach.

For example, editing a YouTube podcast into short videos with different aspect ratios and posting them on X, Instagram, and TikTok accounts; or even translating the video and re - dubbing it to reach an international audience. Platforms like Overlap allow you to set up node - based workflows for these adaptation tasks.

Fifth, taste optimization. The ultimate goal is not just to replace manual tasks with AI, but to cultivate agents with good taste to improve video quality.

There is a reason why people hire professional editors: they make the pictures more beautiful. They spend years learning how to attract audiences, control the rhythm, and evoke emotions with music. This involves thousands of micro - decisions.

YouTuber Emma Chamberlain once said that she used to spend 30 to 40 hours editing a about 15 - minute vlog.

Imagine if an AI agent could watch your video, ask about your goals, and then generate several editing drafts for you to iterate on. You just need to give feedback - "The beginning is too slow", "Cut out the middle part", "Make the ending more impactful" - and the agent will execute.

Video has become the mainstream. It is how we learn, market, and connect. But the editing bottleneck is becoming increasingly prominent: more footage is captured, more platforms need to be posted on, and more formats need to be adapted.

The good news is that the technology to solve the problem is in place. Visual models, tool - using agents, and a large amount of training data have all matured in the past year. All the pieces of the puzzle are ready.

This means that AI editing agents will significantly improve the quality of all the videos we see and greatly speed up their creation in the next few months or even years.

This article is from the WeChat official account "Silicon - based Observation Pro", author: Silicon - based Jun. Republished by 36Kr with permission.