# Agentic Video Is Real and Here’s What You Should Try — Transcript (2026-11-08)

https://aidailybrief.ai/e/2026-11-08 · Listen: https://pod.link/1680633614

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When Opus 4.5 came in November of last year, it took about a month, honestly, till through the holiday break for people to really realize that it represented not just another incremental change, but in fact, a step change in agentic coding capabilities.



What happened after that set up the entirety of 2026, where agentic coding became by far the most important and most dominant AI use case

And set us all racing to figure out how we could bring coding into our normal knowledge worker lives.



Opus 5.5 has been out for a similar amount of time, and people are once again discovering that it represents a more subtle but significant step change when it comes to not coding capabilities, but video editing

In today's episode

We're exploring both what changed and what video use cases people who haven't made videos before should be considering based on the new capabilities

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. [00:01:00] All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Robots and Pencils, CDATA, and Blitzy. To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.

And if you wanna learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai. 

Today, we have an operator's cut that is 

very, 

very on trend

Whenever we get a new model

people race to see based on benchmarks or their own experience how it compares at doing the tasks that are their most important, the ones that they do all the time Sometimes, however, usually with the benefit of a few weeks or even a monthor two of experience, people start to realize that a model or more often a set of models released around the same time have unlocked

some new capability that allows them to do

either something they were doing in a totally different way, or even something that they had never been able to do [00:02:00] before The quintessential example of this

is the models around Opus 4.5, which was quickly followed by GPT 5.2, I believe unlocking agentic coding in a real way and making it so that those of us who were non-coders and not developers 

weren't just using the simplified tools

But could actually just use the core models in their own native interfaces and in their own native harnesses 

to build and code things of increasingly higher complexity. For a lot of knowledge workers, the entire story of 2026 has been in figuring out how that new set of agentic coding capabilities was going to change their work

today. Over the past month, I have seen more and more people come to a determination

That right now, thanks especially to Opus 5.5 We're experiencing something similar with video editing.

Now already in the last decade of TikTok and YouTube more people than ever have been not just consuming video, but creating it

But But there have been, of course, fairly significant limits

to what those nouveau creators could do

Those limits Those limits are increasingly gone

[00:03:00] And now

It is increasingly viable For knowledge workers 

who have never spent a ton of time learning how to make videos, to not just create short-form vertical videos with their iPhones and CapCut

But to actually produce

quality, edited, graphic-enriched video

And my guess is that over the next six to twelve months

You're going to see more people using video for more things than ever before. So in this episode, we're gonna talk about what changed

And what you should try 

First up, what changed?



to understand how the latest generation of AI models have changed video editing and creation, it's worth thinking step by step about what is actually involved in producing video and who did it before



Despite what our phones have trained us to think, producing a video is not actually just pointing a camera at something and letting it rip

First of all, there's the planning. That can involve the script and structuring, where you're thinking about what you want that video to be about, how you want it to look and feel, what beats or narrative elements you want within it, and roughly what [00:04:00] sequence they go in

There's the background material and B-roll that might be involved in making the video depending on what it is There is, yes, of course, the recording itself

But then there's the editing, the cutting, the assembling, the tweaking, the sound perfection, all of that

And as if all that weren't enough, there's also graphics and animations to take it to that next level And depending on how widely you want it distributed, The potential for translations, 

And even in the absence of that



The cut downs that allow you to make shorter videos which are optimized for the distribution channels like social media

A lot of steps in the past tended to mean either a lot of people

or a small number of people 

working some serious overtime

So so how did models start to shift this? Well, the first change was that editing became a conversation

There had already been a bunch of updates over the past couple of years around how we edit videos Descript, for example, helped innovate timeline-based editing where you could simply highlight the words of a script and delete or modify them in some way, which already simplified the process

But that went to the next level When the models got good enough to simply explain to them what you [00:05:00] wanted with English language prompts. Find the good takes, trim silences and filler, place B-roll, lay a first cut. These are the types of things you can say to these models now, and they can actually do it



And importantly, this was not just dedicated tools like Descript's agent Underlord

Or Adobe's Firefly AI assistant

or Adobe's Paper Edit for Premiere

With the latest generation of models, these became core capabilities of Codex and Claude Code as well

The The second big change is that AI can now watch footage. The slowest part of editing was always the watching, and in some ways, this is the change that I think people are underestimating

With the latest generation of models, you can realistically ask things like, " Find the three moments where customers complained about onboarding across twenty hours of calls," and the models can go do that

that Now, Now, for some of the models, they're not actually watching the video



they're using the transcript and timestamps to approximate it But even that's starting to shift Google launched agentic video understanding at the beginning of September



And they say that it's up to 88% more token efficient and around [00:06:00] 7% more accurate, and sees its biggest gains on bigger files like ninety minute lectures or multi-hour recordings.

This ability to watch or approximate watching means in short that when you're having that conversation about editing, it can actually go deliver against it

The third big change is that the series of connectors that now surround our most important models can bring in the raw material

that's required to make lots of different types of videos work



this is especially true for internal facing video, which is gonna be a major topic that we discuss in the what you should try section where connectors give easy access to meeting recordings, call recordings, demos, docs, dashboards

You don't have to reinvent the wheel to go get those things or have some complicated system for the model reaching them. There are so many connectors that already exist from all those different apps and locations to bring that material directly into the model that's going to do the watching and editing

and editing On top of that, thanks to advancements in computer use, agents can even increasingly do the recording



this is gonna matter for types of videos like demos, where [00:07:00] the agent can not only plan the demo, but actually open the page in its own browser in its virtual computer

and record its own demo with an animated cursor

Now, this might be a nascent capability, but it does exist, and it's going to get nothing but better

Another Another huge change



is the ability for AI to animate things and bring those animations into your video



Claude Code, for example, can write animations as React and use ReMotion agent skills to render the file

That native integration has completely transformed

Claude's ability toinclude animations into videos. And animations in this case aren't just useful for looking pretty or cool or keeping people's attention during videos. although that's certainly been extraordinarily valuable for a long form asset like I'm producing on How We Got There

Animations can also be an extremely valuable way to explain complex or dense information the old maxim of a picture being worth a thousand words has always been true, and animations can take that to the next level

Now, Now, further speaking to the fact that all of these systems are increasingly integrated and you can set things up however you need

[00:08:00] Even though Claude Code doesn't have native image generation, You can use APIs like OpenAI's image generation APIs or other image dedicated APIs to give Claude Code that capacity as well, even if you want to use a model like Opus 5.5 to manage things overall

Now on Now on top of animations f-change number six is that generated footage has also made major improvements over the last few months



between Kling 4.0 and Gemini Omni 1.1

You've got better, longer, higher quality, and more controllable generated clips which can provide B-roll to the extent that you don't have access to something from existing stock footage What's more, sound in generated video has become normalized. Ever since Veo 3.1, Runway Gen 4.5, and Kling 3.0



the leading video generation models have also generated audio with the picture

Now I will warn that not all generated footage escapes the uncanny valley. and certainly I have found in our process with how we got here that animations work a lot better for our purposes than generated B-roll

But there are plenty of settings where I can imagine

[00:09:00] generated footage that isn't animations being extremely useful

A seventh A seventh change, which is one that actually happened pretty early but has gotten new context and life because of the other changes



is that the digital twin models for video avatars as 

well as synthetic voice copies

have themselves brought other opportunities. You're starting to see some prominent video creators experiment with secondary channels that are all AI versions of themselves. Although I think it's fair to say that the jury is still out on whether those actually have a place in the video ecosystem or where people still reject them as being weird and synthetic in some way

Even if in general though, people decide that they don't want social media avatars and you still have to record your own face for certain types of videos. There are plenty of settings where avatars and voice twins are extremely valuable inside of a work context.

Think AI dubbing and translation for multiple languages across an international company

That's something where the value of the output so clearly outweighs any potential weirdness in the avatar



that it's still opening up incredibly valuable use cases

Finally, Finally, change eight is that a lot of these capabilities are already [00:10:00] arriving inside tools that you're using. And I think that this cuts both ways. Not only are the existing tools like Loom or Premiere or Firefly adding these advanced video features, I think the more important one is that you can now just manage this end to end from your main platforms of Codex or Claude Code?

Way Way more than any one particular video tool, I think the democratization of video is going to come from within those harnesses that are offered by OpenAI and Anthropic

Now to be Now to be clear, everything is not perfect and there's still lotsof challenges. Generated clips remain seconds long, not minutes

Timeline editing is not perfect and often requires multiple passes



the cost of advanced animation, video generation, and editing can certainly get expensive fast

And yet none of these things change the fact that some fundamental inflection point has been met

where the average knowledge worker, I believe, should now be spending at least some time thinking about whether they can use video to aid in their work communications in some way

way.

Let's move on now to what those knowledge workers should actually [00:11:00] try to use video for

And while I am going to get into specific examples of use cases, I think it's valuable to maybe start with the principles as everyone's experience work context is different

And it may be more useful to understand the circumstances under which a

video beats the format that you'd otherwise use

I think there are six criteria when that happens. The first is when showing beats telling

And the principle here is basically if a picture helps Or is better than a description alone. And the thing that you're showing a picture of

would benefit from any sort of movement or pacing, that's a good candidate for a video



Sometimes it's gonna be an actual visual asset like UI Sometime that's going to be a multi-step process like a workflow

We're showing the steps moving from one to the other is going to be more valuable than just describing it. Another example maybe a little less obvious is numbers changing. Think the difference between Q1 and Q2 in the same way that a chart helps dramatize that, a video that shows the flow between them can take that to the next level





the the second criteria is less about the best native [00:12:00] format for conveying the information and more a realistic acknowledgement of the limitations of your audience

You can basically ask yourself, who needs this thing that I'm making but won't actually read the doc?

Time-constrained executives, 

frontline teams who are handling, well, the front lines



customers who don't want a block of text. Anything that's for those types of audiences who have natural limits to their attention spans is another good candidate for video

Criteria three for where a video might beat your other formats



is when you give the same explanation over and over again

So think about what do I or what does my company explain on repeat? Onboarding is a great example. Customer education could be another example. And something that we'll get into that's a new capability

is the fact that increasingly these videos will be able to update themselves in a way that wasn't possible before

A fourth potential criteria for where a video could work is when the asset needs to exist and operate in more than one language.

This is one of those things that used to be extremely difficult that now AI has made incidental





a fifth category For when to consider [00:13:00] using video is when a bunch of stuff is already sitting there recorded and just needs to be put together

Basically where the raw material already exists

And some of these will actually be transposing one type of video to another Think about it like this. Is anyone going to watch a 60-minute Zoom that they weren't on? Absolutely not. But would they watch a 90-second version or even a two or three-minute version with the highlights?



it's a lot more plausible



con the sixth condition that might make you wanna consider video instead of a different format 



is areas where conviction matters where you want something to land, where you want to convey the emotions behind a potential decision It's certainly not that you can't do that with words, but video gives you a more comprehensive medium





that can, if done well, better convey that conviction even if you're not there to pass it on yourself

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So with those criteria in mind, let's actually move on now to a set of specific ideas and use cases that I think have a lot of applicability across a lot of different types of environments, work and workplaces.

And we're actually going to start first 

with those workplace-related ideas. I think a lot of people when they hear 

video, they assume that we're talking about something external facing, something for marketing. But a huge amount of the communication that could get differentiated value from video is actually internal facing Use case one is the concept video, showing the thing that doesn't exist yet [00:17:00] Now already we've gotten incredible advances in tools to show off our concepts.

One of the best uses for vibe coding is to be able to explain some new feature that you want, even if you're not a developer, for example. But a concept video can be anything from a UI to a new event you wanna hold to something else entirely



And video is inherently so much more information dense than text

that anything from very standard product feature sort of concepts

To store layouts, reorganizations, customer journeys, new processes

Using a concept video for that work, I think has major opportunity. So what would it So what would it look like to actually do that? 

Well, 

if you're dealing with a product, it might still start with a vibecoded prototype.

From there, either you could walk through it using something like Loom, or with computer use, you could simply let an agent record the walkthrough



And from there even generate ideas of where it could go in the future

For these more abstract concepts like

a new onboarding flow or a reorg, I think the place to start is, one, articulating the concept, which once again you can do with AI, whatever the concept is that you're trying to [00:18:00] convey. Two, discussing with your AI build partner, what the right way to visualize that concept is.

And then three, thinking about what the anchor visual content for that video is going to be. So the new onboarding flow, the anchor visual content will likely either be a walkthrough of the flow if it can be done with existing parts





or again some demonstration or prototype. layout, a new store layout, on the other hand, might start with a floor plan and also include mock-ups and renderings of what that store might look like In any case, your AI build partner is going to be able to help you flesh out the concept, pick those visuals, generate those visuals if necessary

plan and script the video, execute the recording of said video, find the relevant external assets if there are any to be included in that video, and then edit it all into one complete package

Video use case number two. Again, we're still internal facing. This is one I mentioned before, but that two, three, four-minute version of the meeting is an incredible use of the new video editing capabilities of AI

Now, some of you might rightly point out that for a lot of meetings, the written AI summary might [00:19:00] be sufficient And that absolutely could be the case, especially if it's a sort of de rigueur standard standup type meeting Where there's not a lot of particularly new information And there's not a lot of debate or discussion but what about where you missed the meeting where there was some meaningful and even intense conversation or debate about a particular decision?

Having AI be able to take the raw recording and hone in on that important piece and show you not just the transcript, but the actual exchanges as they happened, I think could be quite a valuable use case. So this is one where it's a great example of we're not trying to turn everything into a video, and frankly, it's pretty unlikely that most meetings that we hold are going to be video-worthy But for the ones that are, this could be a game changer.

Now, I would also humbly submit that if a meeting isn't video worthy maybe don't have that meeting, and maybe just do it via text in the first place. But that's a whole separate conversation.



A A third internal video use case is about giving people inside the company access to what customers outside the company are saying. this is relevant if the company does sales calls, has records or recordings of support calls, or does any sort of [00:20:00] recorded customer research



and the value of video here is really two parts. the first part is the process of aggregating and then curating the most important themes from all of that raw information. And this is not something to be clear that needs a video output to be valuable.

In general, it is a great use case of AI to be able to take a huge amount of aggregate information 

like customer feedback, and turn it into actionable themes and key insights. But sometimes those things don't matter unless

they can actually find their way to the decision-makers inside the company And that's where video can be really valuable

valuable The fourth internal use case idea gets back to that idea that the people who often need the information most are the least likely to take the time to get it. For shorthand, we're calling this the exec pre-read, but it's any time you need to get a leader or an executive team up to speed fast and frankly conversant in some important strategy document or business case.



think the type of stuff that they need before going into an important meeting



now maybe some of you are sitting there feeling like it's overly cynical 



to rank an asset built around the presumption of executives not reading things so highly



and if your company functions well enough [00:21:00] that that is surprising to you 

Well, 

my friends, I am very impressed with your company

The fifth internal use case idea isn't even really a specific use case as much as a new category which I think is going to be extremely exciting and which is uniquely the domain of agentic video. And that's the video that updates itself. one of the reasons that we don't currently make videos is a rational calculation of the cost in terms of time and money to make them and their likely shelf life For example, if you have how-tos or standard operating procedures or UI overviews or anything like that, which changes with any sort of frequency, whether it's annually or every six months or even every quarter, justifying taking the time to make a video for those things where that video is gonna be out of date in just three or six months 



has historically likely just not been worth it

it but now that we can build agentic video pipelines where agents can take a mission and mandate and be in charge of everything from sourcing the raw materials

to writing or updating the script, to creating the visuals, to [00:22:00] editing the final document

You can build those pipelines so that at some regular cadence, that video actually updates itself

And this is not some far-off future capability. You can set this up in Claude Code right now

This turns video from a one-time point asset to a living asset that can be updated as frequently as is necessary at only the cost of the setup and the tokens to do the updating



a few more internal facing ideas, and we'll start to go through them a little bit more quickly now as there are lots of them 



I think showing numbers in motion

can be incredibly valuable and is a type of video that AI can just crush right out of the gate right now. If dashboards show where things stand, a narrated chart shows what changed and why, and that's exactly the type of animation and underlying data source and output that can be turned into one of those self-updating videos like we were just discussing



Role-based onboarding is another great candidate. Given how much raw information exists out there, like Wiki pages, existing recordings, clips from past meetings

Explainers for complicated things like comp plans, data architectures, pricing models, new policies

[00:23:00] And if you're skeptical here, here would be my challenge. Pick something like that, that you're in charge of explaining to people, and just go ramble at ChatGPT or Claude about it, and task it with coming up with a better way of communicating it

This is the type of thing that AI thrives at Just taking raw inputs, restructuring them, and thinking about how to coherently output it in a simple way. And now that simple way can be animated

one additional internal idea before we turn external is the handoff video. One of the biggest cost centers for companies is transitions between personnel. And that doesn't just mean when someone leaves the company, it can also mean when someone is going out on pregnancy leave or when they're just switching roles Or even when they're just going away for a vacation, depending on the timing

There is always something lost in translation, and usually that person who's doing the switching or leaving is flat out right to the last minute and doesn't have a lot of time tocommunicate everything that they might otherwise like to, to their successor

The reality though is that talking is much easier than writing. It's much faster than writing, it's much easier to be comprehensive, it it doesn't force you to have as [00:24:00] well-structured thoughts. And so once again, this is something where you can just let people who are going through those transitions ramble and have the AI do the heavy lifting of turning that into a handoff video that can help the next person coming in



now the final set of ideas is going to be external facing, and here would be my challenge to you As I said before, I think a lot of folks are intimidated by the idea of external facing video

even if they recognize how valuable and powerful it can be

be

already the advent of short-form video platforms like dedicated platforms like TikTok or just the key features of other platforms like Instagram Reels or YouTube Shorts 

have transitioned our society to consuming a lot more information in the form of short-form video

And while the new capability of AI to edit and produce videos end to end is not on its own going to give you everything you need for a new external video strategy, I would encourage more of you to actually go experiment and see if the experience and output is valuable enough to invest more in.

So a couple ideas on that front. First of all, we've been talking about all of this internal-facing material. Well, if you [00:25:00] have a lot of internal-facing material, whether that's talks or webinars or maybe things that relate to you personally, like podcast appearances, any of that can become clips that are purpose-built for those short-form platforms.

And in this way, you're not having to produce net new video. You're taking video assets that already exist or audio assets that already exist, 'cause that would work as well and getting them remade for a different medium The second idea though, and again, I wanna push you on this, is to invest in some experiments around long form thought leadership

It is almost certainly the case that by virtue of the experience that you've had, the time that you've had in your role, in your industry, doing whatever it is you do, you've built up a canon of knowledge and experiences that would be valuable for someone else

Now, it is a skill in and of itself to know which of those things that you have learned are valuable externally and not just to you But if you have even an iota of an idea towards that end

I would encourage you to collaborate with an AI on some idea for a long-form thought leadership video. The The simplest version of this is to pick a topic and just ramble to camera using Loom or just your phone, [00:26:00] and then letting AI handle the cuts, the graphics, and everything else from there.

The other approach, though, is you can be more intentional and actually architect and plan that video a little bit more

maybe you still do that first ramble, but you instead work with the AI to turn that into a more succinct and clear script that's still in your voice, but then you perform that script rather than just the raw ramble, and that's the material that the AI builds from. Regardless of the approach that you take I think that more of us out there could be doing more long-form thought leadership, and the world would be better for it if we did

Now the next Now the next couple ideas have to do more with sales and accounts

one use case that has long been floating around, but which is now actually really viable is these sort of personalized account videos where you take one base video But can then automatically using a video agent turn it into 20 or 30 versions, each that have the prospect's name, numbers, and use case



similar domain when you send proposals that are PDFs or even websites now if you're getting fancy, there is absolutely no reason at this point to not include the video that goes with it. That is the succinct, clear, most visually dynamic, high conviction version of [00:27:00] that argument for why they should work with you



to the extent that you invest in customer education that customer education can now stay current if you build the pipeline the right way



and maybe let me be clear about the terms here. The difference between making a customer education video and making a customer education video pipeline is the way that you're interacting with the AI. So if I wanted to create a video, I might go into Claude Code and explain

what I wanted the video to say, point it to where the assets are that it could use, give it the specs like approximate length, 

and then let it rip

if instead I wanted to build a pipeline for doing that in an ongoing and self-improving kind of way, instead we're actually building an application using code that's got process logic built into it

So it's still gonna do all of the things that I just described for making the single video, but that's going to be housed within a process that has specific triggers, where, for example, at the beginning of every month or just when I initiate that pipeline an agent is going to be spun up or re-engaged that can go out and search for updates, refresh the script based on those updates, take the latest version of the video and the [00:28:00] raw source material that it has to draw from, and combine that with these script updates to produce the newest version

Basically, a video is a video. A pipeline is an application that produces videos in some recurring way

Now, Now, if at this point you're excited but feeling like there's a lot there and you're not quite sure where to start le let me propose three levels for getting up and running



is, the first level is to find one of those use cases where AI edits what you've already recorded

So the simplest example might be to take some meeting that you've recorded and actually ask the AI to go produce the highlight reel for that video. This is the simplest layer of AI video editing sitting on top of existing assets, but will still give you a feel and flavor for what it can do

The second level is where we move from AI editing what already exists to AI making something that you couldn't before

Another really simple low-hanging fruit example of this is the sort of numbers in motion charts that we've been talking about. Take last quarter's results and this quarter's results, feed those spreadsheets into Claude, and let it build an animated chart sequence

If you have an idea for a product feature, you could build that full concept video

[00:29:00] 

basically now we're moving from taking an existing recording or set of recordings and turning them into an edit, to now producing something net new that just wouldn't have existed before and that you probably couldn't have done, at least not easily, just a few months ago The third The third level Is where you get to AI actually running one of these video pipelines

So an example might be a scheduled weekly video where every Friday an agent pulls either metrics or tickets or shipped features or something like that and renders a two-minute update that is distributed through some specific process



once you build one pipeline like this, I guarantee you'll get a much better sense what this idea of an AI video pipeline can even be and you'll start to come up with more ideas for how you could use it going forward

So So that's the idea

And I guess as we close, it's important to note that in the same way

Agentic coding was never about non-coders somehow replacing developers And was instead about non-coders being able to use code to solve their existing problems or do net new things that they couldn't do before

and generally achieve more in a way that was [00:30:00] impossible, it's the same idea with video. I am as enthusiastic as I can possibly be about these new capabilities, and yet there's no universe in which I'm claiming that me or you who are not video editors and who arehave not spent time building that discipline are going to, even with these tools, be able to produce something as good as actual video editors and producers and directors using these same tools.

That's not the point. those, the point is that those set of people's skills would never have been applied to all these things before because the cost-benefit ratio just wasn't there

These are new opportunities to use video to make work work better, and that's what's exciting Please Please do share what you get out there and produce. But for now, that's gonna do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace 

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