News
As AI Upends Creative Pricing, Indie Agencies Confront a New Reality
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Generative AI heralds rapid efficiencies—but brings strategic upheaval for small agencies balancing margins, client expectations, and creative judgment.
Independent creative agencies have long navigated a tightrope: delivering bold, imaginative work while contending with slim margins. Today, they’re wrestling with generative AI—not just as a tool, but as a disruptor of business models. Efficiency gains promised by AI force a reckoning: how to bill when production gets cheaper, yet client expectations balloon even faster?
Margin Rescued or Client-Entitlement Reinforced?
For many indie shops, generative AI has offered a lifeline. Tools like AI-enabled storyboarding, image synthesis, and video generation promise cost reductions and faster delivery. But while agencies see AI as a margin-restoring weapon—crucial amid years of pricing pressure—clients increasingly demand that efficiencies translate into lower invoices.
Lucinda Peniston-Baines of Observatory International puts it plainly: agencies view AI as a means to regain eroded margins—but clients, freshly attuned to AI’s power, expect savings “now,” on their receipts.
Big Brands Outpace the Indies—With Clients Watching
Major advertisers aren’t waiting. Unilever, Kimberly-Clark, and Yum! Brands have revamped creative production using tools such as Pencil Pro, generating hundreds of assets across markets—a capability well beyond most agencies’ or smaller brands’ reach. These deep-pocketed players set a new standard—one that trickles pressure down the supply chain.
Agencies warn this dynamic doesn’t portend an immediate shift toward in-house creative by smaller brands, but procurement teams are already asking: why pay the same when AI makes it cheaper?
“Clients will always look for value,” says Jonathan Healey of agency IDHL, while Swapnil Patel of Attention Arc adds, “With all the AI headlines there’s a clear expectation that we’re using these tools to help grow their business.”
When Clients Channel AI—Creativity Undermined or Elevated?
The tension is sudden and real. In pitches, buyers may casually whip out AI-generated taglines mid-meeting. Mike Hayward, CCO of Copacino Fujikado, shares that clients now challenge agencies with ChatGPT lines in real time—a shift that blurs value lines.
At Copacino Fujikado, AI has transformed a recent project: generating video and still assets featuring 26 models across various locations at just 26% of traditional live-shoot costs. The cost benefits were significant, and faster delivery has become the norm. Still, not all clients are comfortable with AI-generated assets stepping into traditionally human-crafted territory.
Healey notes some agencies lean AI usage into behind-the-scenes tasks—storyboarding and concept ideation—continuing only when clients accept AI-generated outcomes.
Pricing Models at a Crossroads
1. Hourly with Hidden Efficiency
Copacino Fujikado continues to bill hourly—but layers in AI efficiency indirectly. Faster production and in-house control mean more assets per hour without raising client bills. As Hayward puts it: “We now build in the efficiencies that AI provides… clients directly benefit from those savings.”
2. Output-Based & Productized Pricing
Agencies like Uncharted are shifting toward hybrid models—mixing output-based with performance-based billing. They don’t just deliver work; they get paid more when work hits the agreed business objectives. CEO Hattie Matthews clarifies, “We’re not there to deliver things—we’re there to create value and impact.”
This trend isn’t brand-new. Luquire adopted productized pricing in 2019 to stay competitive. Their experience underscores that AI simply accelerates a pricing evolution already underway.
3. Performance vs. Predictability
Still, there are concerns. Healey warns output-based models risk prioritizing what’s measurable—not what’s meaningful—diluting creative impact. Kiosk co-founder Munir Haddad points out clients may prefer predictable, “known” costs rather than performance-linked, potentially volatile pricing.
Meanwhile, Elite Media uses both hourly and output-based pricing on a case-by-case basis. Though some projects adapt hybrid frameworks, the default remains hourly for longer relationships.
AI’s Work Remains Imperfect—Creative Judgment Still Critical
Despite the hype, AI-generated assets often require significant human craftsmanship. Matthews warns bluntly: “AI tools are not that good yet… You have to patch together the bits that are good. It’s still a very human process.” Agencies expect better tools soon, but for now, human oversight remains essential.
Christine Downton from Observatory International emphasizes this point: the true value lies not in speed or cost alone, but in the outcome—its quality, insight, and creativity.
AI’s Influence Extends Beyond Pricing
AI’s impact reaches every facet of agency operations:
Strategic Evolution – Agencies must also compete on AI literacy and ethical use—not just technical efficiency. They need to pitch not only what AI can do, but how human oversight makes it better.
Positioning & Brand Trust – Transparency about AI’s use, responsible practices, and creative ownership should become pillars of brand trust.
Agency Identity Realigned – The core identity of indies—nimble, imaginative, personal—must evolve around human-AI collaboration, not pure automation.
The Path Forward: Embracing Hybrid Models and Creative Value
Strategic Tiering
Agencies ought to recognize that utility pricing (AI-assisted production) and creative value pricing (human insight) aren’t interchangeable. A dual-tier model—differentiating production from creative strategy—provides clarity and fairness.
Transparent Client Collaboration
Openness about AI workflows—even offering choices between human-only or hybrid processes—can build trust. Some clients may prefer speed and cost; others may want a premium “human-first” label.
AI as Co-Creative Partner
Reframing AI as a co-creative assistant—not a replacement—helps agencies align technology with their creative DNA. The narrative shifts: AI amplifies, but doesn’t define.
Outcome-Driven Partnerships
Moving client relationships toward outcome-based goals—like engagement, conversions, or brand lift—allows value to be measured by impact rather than assets produced. However, blending this model with predictability remains an art.
Agile Experimentation
Adopting AI is not a binary choice but a progressive journey. Agencies can pilot internal efficiency first (e.g., AI storyboarding), then selectively roll out client-facing AI assets, tracking reactions and refining offerings.
Looking Ahead—A Tectonic Shift with Human Anchors
As agencies ask: Should they bill less because AI made it cheaper? The better question might be: How should they bill more, because AI made it better?
1. AI will redefine margin expectations – Clients will expect cost adjustment, but a strategic approach can preserve creative value.
2. Pricing innovation becomes a competitive advantage – Agencies that thoughtfully embrace hybrid pricing—balancing speed, cost, and impact—may gain an edge.
3. Creative excellence remains non-negotiable – At its core, every agency must assert that while AI helps produce, it cannot replace the human spark.
Conclusion: Redefining Rules for a Human-AI Future
As generative AI matures, independent agencies find themselves at a crossroads: commoditization or reinvention. Maintaining the tightrope—delivering efficient service without compromising creativity—means reimagining pricing, embedding transparency, and emphasizing what remains exclusively human.
In this evolving landscape, those that excel will be agencies that treat AI not as a shortcut, but as a tool in a broader creative toolkit—one that elevates judgment, narrative, and impact. And in doing so, they redefine not just how they work, but why it matters.
AI Model
How to Prompt Nano Banana Pro: A Guide to Creating High-Quality Images with Google’s AI
Why Nano Banana Pro Matters
Nano Banana Pro is Google DeepMind’s most advanced image generation model, built on the powerful Gemini 3 Pro architecture. It delivers high-resolution outputs (up to 4K), understands complex prompts with layered context, and performs exceptionally well when generating realistic lighting, textures, and dynamic scenes. It also supports image referencing — letting you upload photos or designs to guide the visual consistency.
In short, it’s not just a toy — it’s a tool for designers, marketers, illustrators, and creatives who want to build professional-grade images fast. But to unlock its full potential, you need to learn how to prompt it properly.
Prompting Basics: Clarity Beats Cleverness
The secret to powerful results isn’t trickery — it’s clarity. Nano Banana Pro doesn’t need keyword spam or obscure syntax. It needs you to be specific and structured.
Here are the key rules to follow:
- Be descriptive, not vague: Instead of “a cat,” write something like “a ginger British shorthair cat sitting on a marble countertop under soft morning light.
- Layer your descriptions: Include details about the subject, setting, atmosphere, materials, lighting, style, and mood.
- State your format: Tell the model if you want a photo, digital painting, cinematic frame, 3D render, infographic, comic panel, etc.
- Use reference images: Nano Banana Pro supports multiple uploads — useful for matching styles, poses, faces, characters, or branding.
This is how professionals prompt: not by hacking the system, but by being precise about what they want.
Crafting Prompts by Use Case
📸 Realistic Photography
Want a product photo, fashion portrait, or cinematic still? Then your prompt should include lens type, lighting style, subject age, composition, and color grading.
Example:
Professional studio portrait of a 35-year-old woman in natural light, soft cinematic lighting, shallow depth of field, 85mm lens look, natural skin tones, soft shadows, clean background, editorial style.
Another example:
A 3/4 view of a red sports car parked in a luxury driveway at golden hour, realistic reflections, soft shadows, DSLR-style image, bokeh background.
These prompt structures help the model replicate not just the subject but the feel of a professionally shot image.
🎨 Illustration, Comic Art, and 3D Concepts
If you want stylized work — like a retro comic, anime-style character, or matte painting — the style must be part of the prompt.
Example:
Comic-style wide cinematic illustration, bold black outlines, flat vibrant colors, halftone dot shading, a heroic female astronaut on Mars with a pink sky, dramatic lighting, wide aspect ratio.
More styles to try:
- Fantasy concept art, a medieval knight riding a dragon above stormy mountains, painted in the style of Frank Frazetta, high detail, dramatic lighting.
- Cyberpunk anime character in a rain-soaked Tokyo alley, glowing neon lights, futuristic fashion, overhead perspective, digital painting.
Tip: Reference known artistic styles (e.g., Art Nouveau, Impressionism, Pixar, Studio Ghibli) to guide the tone.
🔄 Editing Existing Images
Nano Banana Pro can also transform existing images by changing backgrounds, lighting, or adding/removing objects.
Examples:
Replace the background with a rainy city street at night, reflect soft blue and orange lights on the subject, keep original pose and composition, cinematic tone.
Add a glowing book in the subject’s hands, soft magical light cast on their face, night-time indoor setting.
Best practices:
- Use clear “before/after” language.
- Indicate what must stay unchanged.
- Specify the mood or lighting effect you want added.
Common Mistakes to Avoid
- Too generic: A prompt like “a girl standing” tells the model almost nothing. Who is she? Where is she? What’s the style?
- Keyword stuffing: Don’t use outdated tricks like “masterpiece, ultra-detailed, trending on ArtStation.” They’re mostly ignored.
- Ignoring context: Don’t forget to describe how elements relate (e.g. “holding a glowing orb” vs. “glowing orb floating behind her”).
- Unclear intent for text/logos: If you want branded material, say exactly what the logo or label should look like, and where.
Prompt Templates You Can Use Right Now
Try adapting these for your needs:
- “Cinematic 4K photo of a mountain climber reaching the summit at sunrise, orange glow on snowy peaks, lens flare, dramatic sky.”
- “Retro-futuristic 3D render of a diner on Mars, neon signs, dusty surface, stars in the background, warm ambient light.”
- “Isometric vector-style infographic showing renewable energy sources, solar, wind, hydro, with icons and labels.”
- “Realistic photo of a smartwatch product on a floating glass platform, minimalistic white background, soft shadows.”
These prompts are short but rich in visual instruction — and that’s the key to strong output.
Going Further: Advanced Prompting Tips
- Use cinematic language: Words like “soft light,” “overhead shot,” “close-up,” “medium angle,” “shallow depth of field” guide the AI like a film director.
- Test with reference images: Upload an image of your brand, product, or character to maintain continuity.
- Iterate: If your first image isn’t right, adjust one or two variables (e.g., lighting, background, subject age) and regenerate.
- Define aspect ratios: Use “cinematic,” “vertical portrait,” “square crop” if you need a specific format.
- Stay natural: Write prompts like you’re briefing a professional illustrator or photographer.
Final Thoughts
Nano Banana Pro is one of the most powerful visual AI tools available — but it’s only as good as your prompts. Whether you’re an art director, a solo founder, or a content creator, learning to prompt well is the fastest way to unlock its full creative range.
Focus on clarity, visual language, and style specificity. Add references when needed. Think like a photographer, art director, or storyteller. The better your brief, the better the image.
Want more? Ask for our expanded prompt pack: 50+ ready-made formulas across categories like product design, sci-fi art, fantasy scenes, infographics, editorial portraits, and more.
AI Model
Qwen vs. ChatGPT — Which AI Assistant is Better — and For What
Why This Comparison Matters Now
Qwen, the large language model developed by Alibaba Cloud, has recently been gaining significant attention. The release of Qwen 2.5-Max and its successors has sparked comparisons across benchmarks covering reasoning, coding, long-context handling, and multimodal tasks. Meanwhile, ChatGPT continues to dominate as the default choice for many users who prioritize conversational quality, creative tasks, and ease of use. Comparing the two is increasingly important for anyone deciding where to invest their time, money, or infrastructure in 2025.
Let’s explore how Qwen and ChatGPT compare across major performance categories — and which model might suit your needs better.
Where Qwen Shines: Power, Context, and Flexibility
One of Qwen’s strongest features is its ability to handle long-context reasoning and document-heavy workflows. With larger context windows than many competitors, Qwen is particularly adept at analyzing long reports, writing consistent long-form content, summarizing legal or technical material, and managing multi-layered input without losing coherence. It’s a powerful tool for users who need depth.
Qwen also excels in structured logic and code-related tasks. In independent evaluations, it has shown impressive results in mathematical reasoning, data extraction, and code generation. For developers and technical users looking for an AI assistant to support real engineering workflows — rather than simply explain code snippets — Qwen is a highly capable alternative to established incumbents.
Multimodal and multilingual flexibility is another area where Qwen stands out. It supports text, image input, and multiple languages, enabling it to serve as a true assistant across varied communication and media formats. That’s particularly useful for global users or teams operating in bilingual or multilingual environments.
Finally, the open-source accessibility of Qwen is a major advantage. While not every version is fully open, many variants are freely available and can be run locally or fine-tuned. For users prioritizing data control, customization, or cost-efficiency, that’s a serious point in Qwen’s favor.
Where ChatGPT Excels: Conversation, Creativity, and Ecosystem
ChatGPT continues to lead when it comes to polish and user experience. Its conversational flow is smooth, stylistically natural, and often feels more human than any other model on the market. That’s invaluable for creative writing, ideation, storytelling, or any application that requires tone, style, and nuance. It’s also why many casual users prefer ChatGPT over more technical models.
ChatGPT’s integration with live data, APIs, and tools (depending on the version) provides a dynamic and extensible platform for users who need real-time insights or app-level functionality. If you’re looking for an assistant that can browse the web, generate code, search documentation, or plug into third-party services, ChatGPT is often the more mature choice.
Consistency, reliability, and safety mechanisms also remain a strength. For teams or individuals who don’t want to think about model drift, hallucination tuning, or backend parameters, ChatGPT offers a plug-and-play solution that’s hard to beat. It’s a tool that just works — and that simplicity matters more than benchmark scores for a wide audience.
The scale and maturity of ChatGPT’s ecosystem also give it a clear edge. From community guides to business integrations, apps, and workflows — it’s supported nearly everywhere, and that makes it easy to adopt regardless of your skill level.
Limitations and Trade-offs
That said, Qwen and ChatGPT each come with their own trade-offs.
Qwen, while powerful, sometimes lacks the fluency or stylistic finesse that makes ChatGPT feel so natural. It can hallucinate in edge cases, and while some versions are open-source, the most powerful iterations may still depend on Alibaba’s infrastructure, limiting portability for privacy-centric users.
ChatGPT, for its part, is a closed model, with cost barriers and fewer customization options. It also has a more constrained context window in some versions, making it less ideal for ultra-long documents or advanced reasoning across large data structures.
Which Model Should You Use?
If your work involves processing long documents, building tools, working with code, or requiring multilingual support — and you value the ability to run models locally or integrate them deeply — Qwen is an excellent fit. Its performance is strong, and it offers more technical freedom for advanced users.
If your needs are creative, conversational, or content-driven — and you want something intuitive, responsive, and polished out of the box — ChatGPT is still the best experience available today. It’s perfect for brainstorming, writing, email generation, and any task where clarity, creativity, and tone matter.
For enterprise teams, researchers, and power users — using both might be the optimal solution. Qwen can handle the heavy lifting in development and data, while ChatGPT takes care of interaction, presentation, and ideation.
Final Verdict
There’s no absolute winner in the Qwen vs. ChatGPT debate — only better fits for different tasks. Qwen brings muscle, flexibility, and context awareness. ChatGPT delivers fluency, elegance, and seamless usability.
In the AI race of 2025, the smartest move isn’t to pick a side — it’s to pick the right tool for the job.
News
Alibaba’s AI Coup: Qwen App Hits 10 Million Downloads in One Week — And the AI Wars Just Escalated
A Meteoric Debut for Qwen
Alibaba’s freshly launched Qwen app has crossed 10 million downloads in just its first seven days — a staggering adoption rate that places it among the fastest-growing AI applications globally. The explosive start signals more than consumer interest. It marks Alibaba’s transition from infrastructure giant to serious AI contender in the public arena.
Qwen Isn’t Just Another Chatbot
At the core of Qwen’s early success is its engine: the Qwen model family, developed in-house by Alibaba. These large language models (LLMs) are multimodal — capable of processing not just text, but also images, audio, and potentially video. Unlike other AI tools that remain sandboxed in niche applications, Qwen is designed as a true all-in-one assistant.
From drafting documents and summarizing reports to answering questions and managing multimedia tasks, Qwen is built to be useful — not just entertaining. And critically, it launched with a free-access model, eliminating the subscription paywall that often hinders adoption in early-stage AI apps.
From E-Commerce to Everyday AI
This launch represents a clear strategic pivot for Alibaba. Historically known for e-commerce dominance and its powerful cloud infrastructure (via Alibaba Cloud), the company is now positioning itself as a top-tier player in the AI space — not just at the backend, but at the consumer-facing layer.
Qwen is not just a product — it’s a platform play. It ties into Alibaba’s cloud resources, shopping ecosystem, productivity tools, and eventually, financial services. By releasing it as a standalone, viral consumer app, Alibaba is laying the groundwork for a much bigger AI ecosystem play.
Global AI Ambitions, Starting in Asia
While Qwen’s initial rollout is concentrated in China and Southeast Asia, there are clear signs Alibaba intends to push the app globally. With Western alternatives like ChatGPT, Claude, and Gemini facing geopolitical and regulatory barriers in some regions, Qwen could capitalize on being both regionally accessible and locally optimized.
Additionally, the app’s early traction reflects strong demand for AI solutions tailored to regional languages, customs, and ecosystems. As Chinese tech continues to look outward, Qwen may become a cultural as well as a technological export — one capable of competing head-to-head with the biggest names in global AI.
The Next Phase: Monetization and Market Power
Crossing 10 million downloads in a week is only the first milestone. The real test lies in retention, monetization, and integration. Alibaba will now focus on converting casual users into power users, offering advanced features, integrating payments, cloud-based services, and potentially leveraging the app to strengthen its broader commercial footprint.
There is already speculation that Qwen could evolve into the “WeChat of AI” — a super-assistant that combines messaging, productivity, shopping, and finance in a single intelligent interface. If that vision materializes, Alibaba may have just positioned itself as the most powerful AI consumer company outside the West.
Final Thought
The Qwen launch is not just about downloads. It’s about direction. Alibaba has made its move — not with hype or vague roadmaps, but with a working, useful, and widely adopted AI assistant. The global AI race is officially more competitive than ever.
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