AI Predictions for 2026 (Part 2)

Media creation

Last week’s batch of predictions were focussed on AI assistants. This week’s are focussed on media creation.

5.) AI-generated media will become much easier to edit, using both natural language and more traditional tools

As AI-generated images and videos have continued to improve in quality, how easy they are to edit has come to the fore.

Google’s Nano Banana (released last August) made it possible to edit specific elements of an image using natural language without accidentally reimagining everything else.

Qwen-Image-Layered (unveiled before Christmas) is capable of separating images into discrete editable layers - the foundation of Photoshop and most other visual editing software.

I anticipate these two editing approaches - using natural language to edit visual media and enabling AI-generated media to be manipulated using more traditional techniques - will both proliferate in 2026 and expand to more media formats.

Specifically, I anticipate natural language editing for video will mature significantly, increasingly delivering on Runway Aleph’s promise. Currently, asking an AI to ‘show the reverse angle’ in a generated video is hit-and-miss. By year-end, it should be reliable.

I also expect more hybrid interfaces to emerge, pairing AI generation with traditional editing tools. Adobe is placing a big bet here. Google has also been going down this road with Google Flow. Bytedance and Meta will no doubt continue to AI-ify CapCut and Edits and add more editing functionality to TikTok and Instagram. Even Apple is finally starting to sprinkle some AI over its video editing software.

6.) AI slide deck generation will finally become business-ready

As I wrote in October, none of the current AI slide deck generation tools are good enough for most businesses.

Claude creates decent content but generates slides slowly in code and can’t create images. Gamma makes visually appealing decks but mangles text in images and struggles with content accuracy. Copilot in PowerPoint requires too much setup for most users. Template adherence - non-negotiable for established businesses - remains challenging across most tools (it is possible using Copilot in PowerPoint, but requires the creation of a Copilot-specific template).

In November, Google’s Nano Banana Pro emerged with an unexpected approach: generate each slide as a complete image. Because it’s dramatically better at rendering text than previous AI image generators, it can create entire decks requiring minimal fixes.

The challenge with this approach is ease of editing. Writing a text prompt to correct a typo seems absurdly inefficient when we’re used to auto-correcting mistakes simply by clicking/tapping.

I anticipate one of the big AI companies will solve this in 2026 by successfully combining visual AI slide generation with a traditional editing interface. Microsoft should have this at the top of their priority list given PowerPoint’s dominance, but I suspect Google will get there first.

7.) AI will increasingly become invisible infrastructure in TV and film production

A lot of the examples of AI-generated video in mainstream media to date have been high in shock value and low in subtlety. This is partly a consequence of model limitations (short generations and limited character/scene consistency favouring frenetic clip montages) and partly a reflection of the devil-may-care attitude of some (but by no means all) of the creators and brands who’ve dived headfirst into AI-generated video.

Whilst I’m not expecting the bombastic, assault-on-the-eyeballs approach to disappear, I anticipate we’ll see an increasing number of more subtle on-screen applications of AI-generated and AI-modified video in 2026.

Longer generations and greater character/scene consistency will help with this, as will the ability to more easily blend shot footage with AI-generated elements, using tools like Layerhouse. Expect more widespread use of AI for background extensions, crowd multiplication, continuity fixes, and B-roll generation - the unglamorous but essential work that previously required expensive VFX work or second-unit shoots.

We’ll also see more AI use for reconstructions (see Harlan Coben’s Final Twist, which premiered on CBS a couple of weeks ago) and in fantastical entertainment sequences (see the Clue Package videos for season 14 of The Masked Singer on Fox - which has generated predictable backlash).

The productions using AI this way won’t shout about it - it’ll just be part of the toolkit, like green screen or color grading before it. AI use may get a discreet mention (see the closing credits from The Titanic Sinks Tonight, below) whilst it’s still novel and sensitive, but in time I expect declarations will be saved for when the use of AI could potentially mislead viewers in a way which is problematic (see my Dec 2023 post on TV’s generative AI transparency challenge).

I also remain hopeful that the TV and film industry will start taking a less binary approach to AI use. Yes, AI can be used to create sloppy (in both senses) video, which doesn’t respect creatives or IP. But it can also be used responsibly to augment traditional production techniques and empower storytellers with an expanded toolbox.

8.) AI video models will learn to determine scene duration, not just generate to a preset lengths

One of the more frustrating limitations of current AI video generation models is that you need to select a duration up front - typically 3, 5 or 8 seconds - regardless of what the scene actually needs.

Whilst available durations crept up in 2025, many scenes - and some individual shots - need to be longer. Extending generations got easier last year thanks to tools like Google Flow and models got better at rendering multiple shots within a single generation. However, you’re still often left with noticeable joins and/or a lack of continuity between generations. It’s also common to see too much action packed into a short generation.

I anticipate we’ll see tools emerge in 2026 which use reasoning capabilities to determine how long a generation is needed to render a scene.

Managing costs will be a challenge, as longer durations mean more compute. Consequently, this capability may remain limited to premium tiers in 2026. But it would still represent a meaningful step toward AI video generation that understands narrative pacing rather than just executing technical parameters.

9.) A feature-length film made by one person using AI tools will secure distribution from a major streaming platform

Solo creators are already producing increasingly sophisticated short films using AI tools.

In August, I spotlighted Hashem Al-Ghaili’s 15-minute film Kira. Last week, Tunisian filmmaker Zoubeir Jlassi won the 1 Billion's Followers Summit's AI Film Award with his 9-minute animation Lily - a visually coherent, thoughtfully constructed film that kept me engaged throughout, despite some visual inconsistencies.

Lily’s closing credits. Notice anything?

The jump from 9-15 minutes to feature-length is substantial, requiring a level of consistency that current AI tools struggle with. But the capabilities I've predicted above - easier editing, dynamic scene durations, better consistency - will make feature-length production increasingly feasible for solo creators in 2026.

And I predict one of those creators will produce a feature-length film using AI tools that secures distribution from a major streaming platform. Animation strikes me as the most likely format, embracing AI’s current aesthetic quirks as deliberate choices rather than technical limitations.

Distribution beyond YouTube will be controversial. Questions about training data, IP rights, creative labour, and what constitutes filmmaking will intensify. Traditional animators and filmmakers will rightly point to the years of craft development that AI-assisted production assimilates or bypasses.

But it will demonstrate something undeniable: the tools have matured enough for one creatively skilled person to produce broadcast-quality long-form content.

That’s it for this week. See you back here next week for my final batch of predictions, which will be focused on media and AI industry developments.