AI Video Is Finally Profitable: Lessons for Singapore

AI-generated video used to be a punchline: extra fingers, rubbery faces, and movement that screamed “fake.” That phase is ending fast—and the money is starting to show.

A recent case out of China makes the point better than any hype deck. Kuaishou’s video generator, Kling AI, reportedly hit an annualised revenue run rate of US$240 million (December 2025), while its monthly active users jumped 110% from 3 million in December to 7.7 million in January. Paying users also spiked (reported at 350% month-over-month in January). These aren’t “cool demo” numbers. They’re business model numbers.

For this AI Business Tools Singapore series, that’s the signal worth paying attention to: the AI value is shifting from foundation models to products people actually pay for—especially products that shorten production time and increase marketing throughput.

The real shift: AI monetisation is happening in the product layer

AI monetisation isn’t primarily showing up in whoever trains the biggest model. It’s showing up in tools that:

• Solve a specific workflow (video ads, product explainers, social content)
• Reduce cost per asset
• Increase testing velocity (more variants, faster cycles)
• Plug into distribution (social platforms, creator ecosystems, ad pipelines)

Kling’s story is basically “focus beats breadth.” Kuaishou didn’t try to win the war of a hundred general-purpose models. It used its short-video DNA and visual data advantage to build a paid AI video product that customers can justify on a budget.

A number that should make Singapore business owners sit up: Kling reportedly gets meaningful revenue from overseas markets, including the US. That implies two things:

1. AI video is becoming a global commodity product—users will pay if the output is “good enough.”
2. Distribution + packaging matters as much as model quality. A decent product with a clear job-to-be-done can out-earn a better model with messy onboarding and no pricing story.

Why “good enough” AI video wins in marketing

Here’s the uncomfortable truth: most marketing doesn’t require cinema-grade craft. It requires clarity, speed, and iteration.

In practice, “good enough” means:

• The viewer understands the offer in the first 2 seconds
• The visual is believable enough to not distract
• The message matches the channel (TikTok/Reels/Shorts vs LinkedIn)
• The brand can produce ten variations, not one “perfect” video

That’s why AI video is starting to print revenue: it changes the unit economics.

The new unit economics: from “one hero video” to “a library of experiments”

Most SMEs in Singapore still treat video like a rare event: plan, shoot, edit, approve, publish. The cycle is long, so you publish fewer assets, and you learn slowly.

AI video flips that. You can run a content program where you produce:

• 20 hooks (openings)
• 10 demonstrations
• 10 offers
• 5 audience-specific versions (new parents, busy execs, students, expats, etc.)

…and you let performance data decide what gets scaled.

If your production bottleneck disappears, your marketing becomes an optimisation problem—not a creative scheduling problem.

What Singapore businesses can copy (and what they shouldn’t)

Singapore businesses don’t need to copy China’s apps to copy China’s playbook. The playbook is about focus, buyers with budgets, and measurable outcomes.

Copy this: sell to customers who already pay for outcomes

One standout detail from the Kling case: professional and enterprise clients reportedly account for 70% of revenue (as cited in coverage). That’s a strategic choice.

If you’re building or buying AI marketing capability in Singapore, prioritise use cases where budgets already exist:

• Performance marketing teams (creative testing)
• E-commerce teams (product video variants)
• Real estate teams (listing videos at scale)
• Training/L&D teams (microlearning clips)
• Recruitment teams (employer branding content)

The lesson: don’t start with “let’s make something viral.” Start with “what workflow costs us time and money every week?”

Don’t copy this: chasing novelty as a growth plan

AI moves fast, and novelty is cheap. Viral spikes don’t guarantee durable demand.

If your AI content plan depends on a trend format that will be dead in 30 days, you’re building on sand. The businesses that win with AI video build repeatable pipelines:

1. A stable set of brand-safe templates
2. A consistent process for reviewing output
3. A measurement framework for testing creative
4. A distribution calendar that doesn’t collapse when a trend fades

Practical AI video use cases that work in Singapore (2026 edition)

If you want AI video to drive leads—not likes—these are the use cases I’d bet on.

1) Paid social creative testing (the fastest ROI)

Answer first: AI video is most profitable when it increases your testing velocity.

How it looks:

• Generate 10–30 video variants per product/service
• Test hooks and offers with small budgets
• Scale the top 10–20% performers

What to measure:

• Cost per lead (CPL)
• Hook rate (3-second view rate)
• Hold rate (25% / 50% watch)
• Click-through rate (CTR)
• Conversion rate (CVR)

A common Singapore SME pattern is to run ads with 2–3 creatives for months. AI video lets you refresh weekly without a full shoot.

2) E-commerce product demos at scale

Answer first: AI video helps e-commerce teams turn catalog data into shoppable stories.

Ideas that work:

• “3 benefits in 7 seconds” product clips
• UGC-style demonstrations (with proper disclosure where needed)
• Seasonal promos (CNY, Hari Raya, Great Singapore Sale) without re-shooting everything

This is especially relevant in February 2026: many brands are transitioning from CNY campaigns into Q2 planning. AI video makes it realistic to produce post-CNY clearance creatives and Q2 testing creatives quickly.

3) Service businesses: explainers that drive enquiries

Answer first: AI video makes intangible services easier to sell by visualising outcomes.

Good fits:

• Clinics (process walkthroughs, post-care instructions)
• Tuition centres (method highlights, parent FAQs)
• B2B consultancies (before/after case scenarios)
• Renovation firms (concept walkthroughs)

The rule: keep it simple. One offer, one problem, one next step.

4) Internal enablement: sales and onboarding content

Answer first: AI video isn’t only for external marketing; it can cut internal training time.

Examples:

• Sales teams: product updates in 60-second clips
• Ops teams: SOP visual guides
• HR teams: onboarding sequences

This matters because internal content often has the worst production ROI (high effort, low polish expectations). AI video fits perfectly.

The “hard parts” you can’t ignore: deepfakes, copyright, and trust

As AI video becomes more realistic, the risks stop being theoretical.

Deepfake risk isn’t just politics—it’s customer operations

Answer first: If your brand can generate realistic video, scammers can impersonate your brand too.

Singapore businesses should prepare for:

• Impersonation of executives (“CEO message” fraud)
• Fake promo videos and phishing offers
• Synthetic customer testimonials

What to do now (practical guardrails):

1. Set a policy: what you will/won’t generate (real people, public figures, competitor brands).
2. Add approval gates for any video featuring a person’s likeness.
3. Maintain a brand asset registry (logos, official accounts, watermark standards where appropriate).
4. Create a public verification habit: pin official channels; standardise promo mechanics.

Copyright and IP: the silent business risk

Answer first: If your AI video “remixes” well-known characters or styles, your legal risk climbs immediately.

If you’re using AI video tools for marketing in Singapore, keep these operational rules:

• Use licensed brand assets only
• Prefer your own product imagery and original footage as references
• Avoid “in the style of” prompts tied to identifiable studios/artists
• Keep a record of prompts and source materials for campaigns

If a tool makes it easy to generate Barbie/Pikachu-like remixes, that’s a sign to tighten governance—not a sign to post faster.

A simple rollout plan for SMEs: from pilot to lead engine in 30 days

Answer first: The fastest way to succeed with AI video is to start with one funnel, one product, one metric.

Here’s what works if you want leads (not experiments that never ship):

Week 1: Pick one offer and define “success”

• Choose a single lead-generating offer (consultation, audit, demo, trial)
• Define one KPI: CPL or cost per qualified lead
• Decide your channels: Meta, TikTok, YouTube Shorts, LinkedIn

Week 2: Build a repeatable creative system

• Create 5–8 templates (hook + problem + proof + offer)
• Generate 20–40 short variants (6–15 seconds)
• Write a checklist for brand safety and claims

Week 3: Launch tests and measure properly

• Run small-budget A/B tests
• Kill losers quickly
• Document why winners worked (hook, pacing, claim, visual)

Week 4: Scale winners and operationalise

• Produce “winner expansions” (same hook, new proof; same offer, new audience)
• Build a weekly content cadence
• Assign ownership: who generates, who approves, who publishes

If you do this well, you don’t just get cheaper videos. You get a marketing system that learns faster than your competitors.

Where this is going next (and why Singapore should move early)

AI video used to look fake. Now it’s credible enough to sell products, win attention, and—based on Kling’s reported numbers—generate serious revenue.

The opportunity for Singapore businesses is straightforward: treat AI video as a production line for marketing experiments, not as a novelty tool. The companies that win in 2026 will be the ones that can publish, test, and refine faster while keeping brand trust intact.

If you’re building your stack for the rest of the year, ask one practical question: Which part of your marketing pipeline is slow because video is expensive—and what would happen if that constraint disappeared?

Source referenced: https://www.channelnewsasia.com/commentary/china-ai-videos-kuaishou-bytedance-profit-5906516