Walid Boulanouar recently shared something that caught my attention: "the west is not leading ai video anymore." He pointed to what he called a coordinated push from China, "dropping kling 3.0 and now seedance 2.0," while many Western teams are still debating "release cycles for sora 3 and Veo 4 ( maybe)." He also described the results as "insane in cinematic realism" and said it feels like "the nano banana moment for ai video is here," noting that it is already available in tools like Higgsfield and Freepik.

That short post packs a big claim: the center of gravity for AI video is shifting, and the shift is visible in product cadence, distribution, and the quality bar creators can now hit. I want to expand on what Walid is getting at, because the implications are practical for anyone building products, creating content, or running a creative team.

The point Walid is making (and why it matters)

Walid is not just cheering for new models. He is calling out an execution gap.

"china is on a coordinated offensive" while "we are still debating release cycles"

Whether or not you agree with the geographic framing, the underlying pattern is real: when multiple teams iterate quickly, ship frequently, and push distribution through widely used tools, they can change the market before slower ecosystems finish their internal debates.

AI video is especially sensitive to this because:

The quality threshold is obvious. Anyone can see a jump in motion consistency, lighting, texture, or camera control.
Creators switch fast. If a tool produces better shots today, loyalty is thin.
Distribution compounds. If a model lands inside creator workflows (templates, stock media marketplaces, editing suites), adoption accelerates.

So Walid is effectively saying: this is not a research race only. It is a shipping race.

What "cinematic realism" actually signals in 2026

When Walid says the leap is "insane" in cinematic realism, he is pointing at a bundle of capabilities that collectively feel like a step-change:

1) Coherent motion and fewer "video tells"

Early generative video often gave itself away with jittery motion, melting objects, or identity drift between frames. The new wave is reducing those errors. The result is not perfect, but it crosses a psychological line where viewers stop looking for mistakes and start focusing on the story.

2) Better camera grammar

Cinematic realism is not just sharp frames. It is camera behavior: dolly moves, rack focus, lens choices, and plausible depth. When models get better at that grammar, prompts become closer to directing than to debugging.

3) Consistency across shots

The real creative bottleneck is not generating one great clip. It is generating multiple clips that match style, character, wardrobe, and lighting. Improvements here turn AI video from a novelty into a pipeline component.

This is why the "nano banana moment" metaphor resonates: a moment when capability becomes meme-able and broadly usable, not just impressive in a lab demo.

Why fast release cycles beat perfect release cycles

Walid contrasts rapid drops (Kling 3.0, Seedance 2.0) with the West "still debating" the next releases. The meta-lesson is not that speed always wins. It is that speed plus feedback wins.

In generative media, model improvement is tightly coupled to:

Data flywheels (what users generate, what they like, what they remix)
Prompt patterns (what people actually ask for, not what researchers assume)
Tool context (how the model is used inside real workflows)

If you ship more often, you collect more real-world signal. If you integrate into places where creators already live, you increase that signal again.

The competitive moat in AI video is not only model weights. It is iteration speed, UX, and distribution.

Distribution is the underrated battlefield

Walid notes the tools are "already available in higgsfield and freepik." That matters more than it might sound.

A model that is technically strong but hard to access often loses to a slightly weaker model that is:

Embedded in popular platforms
Wrapped in templates and presets
Priced for experimentation (or free tiers)
Supported by creator education and examples

When AI video appears inside marketplaces and creative suites, it becomes less of a "new product" and more of a default option. That is how adoption suddenly feels inevitable.

What builders and creative teams should do right now

Walid ends with a very engineer-forward stance: "we bought pretty much every creative tool on the market for the whole team. as engineers today, there is no excuse to not build something great." I agree with the spirit, and I would make it more actionable.

1) Treat AI video as a stack, not a single tool

Instead of asking "Which model is best?" map the workflow:

Ideation (scripts, storyboards)
Shot generation (text-to-video, image-to-video)
Control (reference frames, motion guidance, camera constraints)
Editing (cutting, color, sound)
Delivery (formats, variants, localization)

Often the win comes from stitching tools together cleanly, not from obsessing over one model.

2) Run a weekly benchmark with your own prompts

Public demos can be misleading. Create a small internal test set:

5 product shots (your actual product or brand style)
5 character shots (consistent identity)
5 motion shots (complex movement)
5 cinematic shots (lighting and camera)

Re-run the same tests every week across the tools you are considering. Track what improves, what breaks, and what becomes usable for production.

3) Optimize for time-to-first-usable-clip

In production, the metric that matters is not "best possible output." It is how quickly a non-expert can get something good enough to ship. Measure:

Minutes to a usable clip
Number of iterations required
Cost per usable second
Failure rate (clips that must be thrown away)

4) Build lightweight pipelines, not heavy platforms

Walid mentions building with automation tooling like n8n and agentic workflows. The pattern that works is:

Automate ingestion of briefs
Generate multiple shot candidates
Route to human review
Auto-export variants

Start with a thin layer that connects tools and enforces process. Avoid spending months building an internal studio before you know what the team will actually use.

5) Invest in creative direction, not just prompts

As realism rises, differentiation shifts back to taste:

Strong references and moodboards
Clear shot lists
Consistent art direction
Good editing and sound

The teams that win will be the ones that combine model capability with creative judgment.

If the "race" framing is true, what should the West do?

Walid is basically sounding an alarm. If you want a pragmatic response (not a geopolitical argument), it looks like this:

Shorten feedback loops: ship, learn, iterate
Lower friction: put models inside workflows where creators already are
Compete on product: controls, reliability, and collaboration features
Support creators: examples, templates, and education at scale

The takeaway is not "panic." It is "move." AI video is becoming a normal part of creative production, and the teams that treat it as such will outpace the teams still waiting for a perfect next release.

This blog post expands on a viral LinkedIn post by Walid Boulanouar, get one engineer with swarm of agents | aiCTO ay automate & humanoidz | building with n8n, a2a, cursor & ☕ | advisor | first ai agents talent recruiter. View the original LinkedIn post →