Generative AI has revolutionized visual creativity, but how well do these tools stack up when tasked with creating authentic vintage/retro advertisements? Using a consistent style theme, I tested five popular AI tools — MidJourney, Krea, Ideogram, Freepik, and Leonardo AI — to see which delivers the best results. From iconic Victorian lithographs to 50s retro-futurism, this post explores their strengths, weaknesses, and whether AI can truly capture the essence of a bygone era.
The charm of vintage/retro advertisements lies in their intricate details, nostalgic appeal, and artistic diversity spanning decades. From Victorian lithographs and roaring 20s trade cards to mid-century catalogs and 50s retro-futurism, the style demands not only artistic flair but technical precision. To evaluate how well GenAI tools perform, I tested five leading platforms, applying the same prompts and measuring their output against 18 key criteria.
Can GenAI image tools create compelling visuals that feel authentic to their respective eras? And beyond aesthetics, how do they handle prompt accuracy, customization, style consistency, user experience, text and typographic? Here’s what I discovered.
This comparison does not take into account how well different style themes correspond to the selected tools or the specific model versions used during the evaluation. The text prompts in this study were specifically crafted to evaluate how well each tool handles graphic design, text rendering, context accuracy, and adherence to the selected time period and style (vintage/retro ads).
It is also worth noting that, at the time this study was conducted, tools such as MidJourney were on the verge of a major update to version 7, which could significantly impact future outcomes. The results presented here are context-specific and focused on the particular requirements of this case. The outcome of this evaluation would undoubtedly differ if the focus were shifted to other creative styles, such as illustrations, paintings, photography, or other media. As such, the scores provided here are not universally applicable and may vary depending on the intended use case or style.
Used Model: Leonardo Phoenix
For me, this tool is a bit of a revisit. I first tried Leonardo AI back in early 2023 alongside other GenAI image tools that were emerging at the time. Back then, I felt the results had a somewhat generic tone, seemingly optimized for broader audience appeal. Last week, I decided it was time to give Leonardo AI another try. A lot has happened since I last used it, and I’m glad I did. Previously, I found the user interface to be more complicated and cluttered. Now, everything has changed—Leonardo has a much cleaner, straightforward, and intuitive UI. Gone are the overwhelming options, replaced with a sleek interface that integrates seamlessly with a growing suite of tools designed for workflow-related tasks.
Since my last experience, I noticed that Leonardo’s image quality has undergone a major facelift. I recall the earlier versions had overly high-contrast, colorful outputs with a lack of subtle greyscale tones, which made them feel somewhat cheap. Now, the contrast feels much more balanced, with a richer middle-grey register that adds depth and sophistication to the visuals. One feature that really sets Leonardo AI apart is its knack for infusing creativity into the generated images. Often, I found Leonardo would add additional text elements or visual details that were not explicitly mentioned in the text prompts. This suggests that the tool not only understands the context but also goes a step further, adding coherence and thematic consistency that align perfectly with the intended time period. These subtle, creative enhancements are a HUGE win and really elevate the overall results.
Another notable improvement is the image composition. Despite the inclusion of intricate details, the layouts are now far better balanced and more visually stable, which helps anchor the overall composition. The results feel polished and harmonious, even when the images are packed with content. While the improvements are significant, there are still a few minor shortcomings. For instance, the text in the images can sometimes appear duplicated or incorrect. However, this is still a giant step forward compared to my earlier experiences with the tool.
Leonardo AI has come a long way. With its refined UI, improved image quality, and creative flair, it now stands as a highly competitive GenAI image tool. The enhancements in contextual understanding, composition, and workflow integration make it a tool worth revisiting—especially if you haven’t tried it in a while.
Tool: ★★★★☆
Strong Performer
Attempts-to-First-Quality-Image ★★★☆☆
1/12
Speed-to-Render ★★☆☆☆
~45s
Ease-of-Use: ★★★☆☆
Straightforward
Image Management ★★★☆☆
Galleries
Best Quality Image ★★★★☆
High-Quality
Cost ★★☆☆☆
Moderate Cost
Composition & Variation ★★★☆☆
Flexible
Additional Creativity ★★★★☆
Unique Ideas
Customization ★★★☆☆
Flexible Options
Follow Prompt Instructions ★★★★☆
Very Accurate
Censorship Guidelines ★★★☆☆
Rarely Blocks
Media & Technique Versatility ★★★★☆
Handles Many Styles
Follow Image References ★★★★☆
Good Resemblance
Style Ref Consistency ★★★★☆
Uniform
Character Ref Consistency ★★★☆☆
Erratic
Text Accuracy ★★★★☆
Minor Text Issues
Overall Effort ★★★☆☆
Minimal Effort
Cost/Value ★★★★☆
Great Value
Overall Rate
Used Model: Flux
I’ve been using the Krea tool for a while now, primarily for its upscaling functionality. However, recently Krea has introduced an entire suite of new tools and functionalities—and it is AMAZING! Over the past couple of weeks, this has become my go-to tool for generating AI images. It’s fast—REALLY fast—and consistently delivers fantastic results on the first attempts almost every time. Krea feels like an image harvester machine, and its real power lies in the ability to tweak outputs using a variety of pre-trained styles. These include both your own styles and shared styles from the vibrant Krea community.
What’s worth noting is the range of models Krea offers. The main model, Flux, is optimized for Krea, but it also includes: Flux 1.1 Pro, Flux 1.1 Pro Ultra, Ideogram 2.0, Ideogram 2.0 Ultra. For video generation, Krea also integrates several models, such as: Luma, Hailuo AI, Runway, Kling Standard, Kling Pro, and Kling 1.5. So, this incredible app combines nearly all the essential graphical tools you might need in a single platform.
When it comes to the results, I had a hard time selecting which images to showcase because I generated so many great ones with Krea. As you can see from the selected visuals, I gravitate toward line art illustrations and typographical creativity. This preference may have slightly impacted the correctness and coherence of the themes for certain time periods, but the results were just too good to ignore!
That said, there is definitely an opportunity for improvement in Krea’s text rendering. While the tool delivers solid outcomes, text accuracy still needs some polishing. Fortunately, this is easily fixable with Photoshop for those final refinements.
Tool: ★★★★★
Emerging Tool
Attempts-to-First-Quality-Image ★★★★★
1/4
Speed-to-Render ★★★★★
<10s
Ease-of-Use:★★★★☆
Straightforward
Image Management ★★★★☆
Good Organization
Best Quality Image ★★★★★
Stunning Output
Cost ★★★★★
Very Cheap
Composition & Variation ★★★☆☆
Balanced
Additional Creativity ★★★☆☆
Inspiring
Customization ★★★★☆
Many Settings
Follow Prompt Instructions ★★★★☆
Accurate Results
Censorship Guidelines ★★★★☆
Rarely Restrictions
Media & Technique Versatility ★★★★☆
Handles Many Styles
Follow Image References ★★★★☆
Good Resemblance
Style Ref Consistency ★★★★★
Consistent
Character Ref Consistency ★★★★★
Perfect
Text Accuracy ★★★☆☆
Mixed Quality
Overall Effort ★★★★★
Effortless
Cost/Value ★★★★★
Excellent Value
Overall Rate
Used Model: Ideogram 2.0 Turbo
Ideogram sometimes surprises. Even though this tool has lower ratings compared to others in this evaluation, it distinguishes itself in several key areas of quality. While it may offer fewer variations in rendered outputs and often requires multiple remixes to achieve the desired result, when it works—it truly excels.
You often need to process the final image through an upscaler app for that extra push in resolution. However, one of Ideogram’s strongest advantages is its, image composition which tends to be cleaner and less cluttered compared to other tools.
Another notable strength is text accuracy. The text is approximately 90% correct, with excellent typographical execution, often adding angled or dynamic headlines. In terms of image quality, Ideogram has a distinctive touch—a subtle milky filter in its color tones. The tool leans toward rich middle greyscales, with less emphasis on stark black and white contrasts. This makes it particularly well-suited for post-production retouching work. The end result is a very clean image compare to the other tools. And is for that reason why this tools is part of this evaluation.
Tool: ★★★★☆
Good at certain tasks
Attempts-to-First-Quality-Image ★★☆☆☆
1/20
Speed-to-Render ★★★★☆
<30s
Ease-of-Use ★★★★☆
Straightforward
Image Management ★★☆☆☆
Limited
Best Quality Image ★★★☆☆
Good Quality
Cost ★★★☆☆
Affordable
Composition & Variation ★★★★☆
Good Balance Compositions
Additional Creativity ★★★☆☆
Inspiring
Customization ★☆☆☆☆
Barely Adjustable
Follow Prompt Instructions ★★★★☆
Accurate Results
Censorship Guidelines ★★★☆☆
Seldom Restrictions
Media & Technique Versatility ★☆☆☆☆
Good at 1–3 Styles
Follow Image References ★★☆☆☆
Acceptable Resemblance
Style Ref Consistency ★★★☆☆
Erratic
Character Ref Consistency ★☆☆☆☆
Not Applicable
Text Accuracy ★★★★★
Clear Text
Overall Effort ★★★★☆
Effortless
Cost/Value ★★☆☆☆
Bring Specific Value
Overall Rate
Used Model: Mystic v.2.5
FreePik is the newest tool in this evaluation trial, and I must say it took me by surprise. I recently discovered this fantastic platform, and I didn’t see it coming. At this point, I’m still in a learning phase, exploring its capabilities and figuring out what it has to offer. What initially piqued my interest was its training module for characters and styles. This feature seems incredibly accurate and powerful, making it a standout function for personalized image creation. That said, I’m still in an exploratory mode, uncovering FreePik’s full potential.
One thing that really stands out with FreePik is the cleanliness of the generated images. The tool consistently delivers visuals with a well-balanced design composition, which makes a strong first impression. The creativity in text layout and typography is another impressive feature. For example, in images like “Electro-Knit” and “Zap-Zap,” the text feels like carefully designed logotypes, adding a unique edge to the visuals. This is something I haven’t seen in other tools and makes FreePik stand out. The image “Static Silhouette Sculpture” also showcases a recognizable font and logo style that feels true to the time period.
Similar to Leonardo AI, FreePik seems to have a remarkable ability to understand context. It occasionally adds text elements that were not explicitly mentioned in the text prompts, which contributes to a more coherent and polished final result. Another strength is the tool’s ability to produce good variations in design composition across renders. Most of the images have correct text, though there were a few exceptions where the results fell short. These occasional inaccuracies, however, are minor and don’t detract significantly from the overall experience.
So far, my overall impression of FreePik is nothing short of FANTASTIC. The clean compositions, innovative typography, and contextual understanding are incredibly promising. The minor text issues I’ve encountered are likely to improve over time as the tool evolves. FreePik has certainly earned its place in this evaluation and is a tool I look forward to exploring further.
Tool: ★★★★☆
Emerging Tool
Attempts-to-First-Quality-Image ★★★★☆
1/8
Speed-to-Render ★★★☆☆
<60
Ease-of-Use ★★★★☆
Straightforward
Image Management ★★★☆☆
Basic Management
Best Quality Image ★★★★☆
High-Quality
Cost ★★★★☆
Low Cost
Composition & Variation ★★★☆☆
Balanced
Additional Creativity ★★★★☆
Unique Ideas
Customization ★★★★☆
Many Settings
Follow Prompt Instructions ★★★★★
Very Accurate
Censorship Guidelines ★★★★☆
Rarely Restrictions
Media & Technique Versatility ★★★★☆
Excels in All Styles
Follow Image References ★★★★☆
Good Resemblance
Style Ref Consistency ★★★★★
Consistent
Character Ref Consistency ★★★★★
Perfect
Text Accuracy ★★★★★
Clear Text
Overall Effort ★★★★☆
Effortless
Cost/Value ★★★★★
Excellent Value
Overall Rate
Used Model: MidJourney v.6.1
MidJourney has been my main GenAI image tool for almost two years, and while it excels in many areas, it does have some weaknesses. Graphic design isn’t MidJourney’s strongest suit, which might stem from the type of material it has been trained on. In this particular case, it seems as though the closest associated image references were old postcards. In other words, it doesn’t appear to have been specifically trained on vintage advertising graphics.
The word “Vintage” likely triggered the yellow tones resembling aged paper that appear in all the images. While some of the painted visuals are remarkably accurate in capturing the style of the time period, MidJourney’s biggest drawback is its poor support for text and typography. Every image contains garbled text, looking as though someone scribbled random notes over them.
Another recurring issue I’ve noticed for a long time is the appearance of small image artifact details sprinkled across the visuals. This often means the images need to go through a cleansing process in Photoshop to become usable. Most annoying of all, some of the text prompts I used didn’t pass the censorship guidelines. Apparently, there was something about “getting electrocuted in a bathtub” that triggered the filters—quite a surprise when working on vintage-themed ads! That said, I truly love MidJourney’s strengths in other areas. However, in this particular case, it falls short.
Tool: ★★★★☆
Industry Leader
Attempts-to-First-Quality-Image ★★☆☆☆
1/20
Speed-to-Render ★★☆☆☆
Fast ~30s, Relax <3 min
Ease-of-Use: ★★★☆☆
Slight Learning Curve
Image Management ★★★★☆
Excellent Galleries
Best Quality Image ★★★★☆
High-Quality
Cost ★★★☆☆
High Cost
Composition & Variation ★★★☆☆
Balanced
Additional Creativity ★★★☆☆
Inspiring
Customization ★★★★★
Many Settings
Follow Prompt Instructions ★★★☆☆
Misses Subtleties
Censorship Guidelines ★☆☆☆☆
Over sensetive blocks
Media & Technique Versatility ★★★★☆
Excels in All Styles
Follow Image References ★★★★☆
Good Resemblance
Style Ref Consistency ★★★★★
Consistent
Character Ref Consistency ★★☆☆☆
Inconsistent
Text Accuracy ★☆☆☆☆
Garbled Text
Overall Effort ★★★★☆
Effortless
Cost/Value ★★★★☆
Great Value
Overall Rate
Generative AI tools are pushing creative boundaries, but how well do they handle the nostalgic charm of vintage-retro advertising? In this evaluation, I tested Leonardo AI, Krea, Ideogram, FreePik and MidJourney to see how they perform when tasked with recreating the timeless aesthetics of retro ads across decades. From clean compositions and typographical creativity to text accuracy and contextual understanding, each tool brought its own strengths and challenges to the table.
While no tool achieved perfection, each offered unique capabilities that cater to specific creative needs. This evaluation highlights the growing potential of AI tools in handling design-intensive tasks and retro themes, while also pointing to areas for future improvement.
Step into the electrifying world of Sugar Hippie Rush, a group that channels the pioneering spirit of Giovanni Giorgio Moroder, the “Father of Disco.” Known for his groundbreaking use of synthesizers, Moroder shaped genres like Euro disco, synth-pop, and electronic dance music, influencing everything from Italo disco to techno. Inspired by his innovative use of analog synths and early drum machines, Sugar Hippie Rush reimagines the lush, futuristic soundscapes of the 80s with a fresh, modern twist. Their tracks are a pulsating journey through shimmering arpeggios, funk-infused grooves, and neon-lit nostalgia.
Sugar Hippie Rush is a fictional creation, brought to life through a collaboration between myself and the GenAI tool; Udio 130 v.1.50 (on a 48kHz sample rate). This project serves as an exploration of how AI-driven music tools can craft compelling narratives and powerful sonic worlds. I created Sugar Hippie Rush not just for the listening experience but as a means to evaluate the quality and performance of GenAI music tools. I try to apply a light-weighted, generalized variant of the 5-point music analysis method to examining how well the AI captures the distinctive elements that define each genre and style. The evaluation result is addressed for the collection and not for the individual tracks.
Sound Design: Sugar Hippie Rush leans heavily into a retro-futuristic sound palette, blending synths that glisten with a kind of lo-fi warmth alongside percussive elements that feel modern but lightly dusted in nostalgia. The kicks are punchy, without overpowering, and the hi-hats have this almost shuffled texture that gives a laid-back groove. The synths often evoke a whimsical, slightly warped cassette quality—as if someone found an old treasure and gave it a new spin for the dancefloor.
From a technical perspective, the synths seem to be crafted using classic analog emulation plugins, perhaps something akin to the Juno-60 or Prophet emulations, giving them that rich, vintage feel. The subtle tape distortion effect used throughout adds to the retro warmth, while sidechain compression on the pads creates a breathing effect that keeps the energy consistent. Overall, the sound design balances playful innovation with a comforting vintage touch, making it feel simultaneously fresh and familiar.
Genre Adherence: Sticking to what feels like a blend of nu-disco and funk-inspired house, Sugar Hippie Rush confidently taps into the characteristics of these genres. The tracks exhibit a steady four-on-the-floor beat typical of disco-house, while their sonic embellishments bring in the quirkiness of nu-disco. There’s a consistent use of filtered breakdowns, a groove-centric bassline, and shimmering synth chords that make it irresistibly danceable.
Technically, the BPM hovers around 120-125, typical for nu-disco, giving it that relaxed but danceable tempo. The use of filtered sweeps and high-pass filters during transitions helps build tension and release, a classic technique in disco-house. This adherence isn’t rigid, though; there are moments when the artist plays with genre conventions—adding slightly detuned leads or unexpected vocal chops—giving the music a playful edge.
Emotional Impact: The music here is made to be enjoyed—pure and simple. It embodies a carefree spirit that evokes the bliss of a sunny day spent with friends, dancing, or simply lounging in a park. There’s an infectious energy that doesn’t take itself too seriously, which makes it feel like a sincere invitation to let loose. Some tracks in the set lean into a more euphoric feeling, almost uplifting you with wide synth pads that swell, while others exude a breezy relaxation—perfect for a lazy afternoon.
The emotional impact is enhanced by the use of dynamics—like the volume swells on synth pads that create a lifting sensation, and the use of reverb to add depth without making the sound too distant. The instrumentation choices, like bright plucked guitar samples or shimmering bell synths, further enhance the carefree, positive atmosphere.
Melodic Variation: In terms of melodic variation, Sugar Hippie Rush keeps it straightforward but effective. Rather than overcomplicating, the artist relies on catchy motifs and playful loops that subtly evolve throughout the tracks. This minimalist approach works in the favor of this genre—giving the bassline room to shine, while synth leads and chord stabs change just enough to keep the ear interested without overwhelming the groove.
Melodically, the tracks often revolve around major keys or pentatonic scales, which lends to the positive, almost childlike charm of the music. The use of modulation, such as shifting up a whole step during a final chorus, adds a sense of climax without needing overly complex changes. The intervals used are typically thirds and fifths, which keeps the harmonies consonant and pleasing to the ear.
Arrangement Complexity: The arrangement across the tracks maintains a strong focus on flow. There’s a thoughtful progression in how layers come in and fade out—building tension before a classic disco-style drop or giving space for breakdowns that let the track breathe. Unlike some of the highly intricate arrangements found in more experimental genres, the complexity here is subtle and functional.
The typical arrangement structure follows the intro-verse-chorus-breakdown-chorus-outro pattern, which is easy for listeners to follow and effective for dance music. Sections usually last around 16 or 32 bars, giving enough time to build a groove before introducing new elements. Automation on effects like filters and reverb helps to keep sections dynamic and engaging without the need for drastic compositional changes.
Expressive Nuance: One of the standout aspects of Sugar Hippie Rush is the way they use expressive nuances. These aren’t about showy vocal performances or flashy solos—instead, it’s the smaller, detailed touches that make the difference. Like the occasional filter sweeps that add a touch of psychedelic flavor, the playful use of stereo panning that gives a ‘movement’ sensation, and even the way reverb tails are used to create a sense of space that isn’t too polished—adding to the warm, inviting feel.
Technically, the use of subtle LFOs (low-frequency oscillators) on parameters like filter cutoff adds a gentle movement to the synth lines, making them feel more alive. The panning automation on certain percussion elements gives the impression of sound moving around the listener, which adds to the sense of immersion. These details bring a subtle but noticeable depth, transforming tracks from simple loops into experiences with texture and character.
Sugar Hippie Rush is a celebration of groove, nostalgia, and fun—crafted for those moments when you just want to smile, dance, and let the music wash over you. The careful attention to sound design, genre adherence, and emotional texture makes this set both a throwback and a refreshing burst of originality. It’s a perfect listen for anyone seeking an authentic nu-disco experience with enough nuance to keep you discovering new details on each playthrough.
Best Sound Design Moment: The warm, slightly detuned synth pads during the breakdowns that evoke a sense of nostalgia.
Most Uplifting Track Element: The modulation shift during the final chorus, which adds an uplifting sense of climax and joy.
Grooviest Bassline: The funk-inspired bassline in track three that carries the whole rhythm with a smooth, infectious energy.
Sugar Hippie Rush doesn’t try to reinvent the wheel—it just makes sure the wheel is spinning in a way that invites everyone to join in for the ride.
Listen to the tracks & decide for yourself: How close is AI to capturing the essence of sound?
This text was created based on a discussion about Sugar Hippie Rush using generative AI tools. The insights shared here were developed collaboratively through a dialogue, incorporating audio examples. The conversation took place between me(Michael Käppi) and with ChatGPT-4o with Canvas, leveraging its canvas feature to analyze and explore these topics interactively, resulting in this post.
Generative AI has opened up new opportunities for creativity, making content creation accessible to more people than ever before. However, with this accessibility comes a challenge: the risk of creativity becoming diluted and artistic voices becoming homogenized. This post is a speculative exploration of how a hybrid AI ecosystem—balancing centralized and decentralized models—could protect and enhance creative diversity. Imagine a world where creativity itself is the product, and artists can monetize their unique AI models, creating a new economy of personalized, AI-driven expression.
Generative AI has made it possible for anyone to create art, music, and writing easily. This is empowering, but it also introduces the risk of creative dilution. When more and more content is produced by people who may not have professional experience, the AI models trained on this content can lose their originality and depth. Centralized AI systems, which often rely on undifferentiated data, may further reinforce popular trends instead of encouraging diverse and unique artistic voices.
“As generative AI becomes more widely used, its reliance on vast, often undifferentiated datasets can lead to a dilution of creativity. This can result in homogenized outputs, lacking the richness and distinctiveness that define true artistry.”
A possible solution is a hybrid AI ecosystem that integrates both centralized and decentralized models. Centralized models provide consistency and scalability, but they may do so at the expense of creative uniqueness. Decentralized models, on the other hand, allow individual creators to cultivate AI tools that reflect their specific styles and nuances. Imagine a system where centralized models handle broader tasks, while decentralized models allow individual creators to curate and develop unique versions that reflect their personal artistic vision.
A decentralized approach could give power back to creators, allowing them to develop and refine their own AI models. This means that artists could better preserve their creative intent and potentially enhance it over time. Instead of simply using AI as a tool, it could become a creative partner, co-creating with the artist and blending artistic vision with advanced technology.
AI swarms—groups of specialized AI agents working together—could play an important role in this hybrid ecosystem. For example, one AI agent could focus on color, another on texture, and another on composition. These specialized agents could adapt to user preferences in real-time, creating richer and more nuanced creative outputs. SynthAI could also help artists refine their models by synthesizing user interactions and offering actionable insights for continuous improvement.
A hybrid AI ecosystem that adapts based on context offers a promising path forward for more dynamic, context-sensitive solutions. This approach could also lead to new business models, such as companies that manage and curate personalized AI models. Similar to an AI app store, these models could be bought, customized, and shared, offering more than just functional tools—they provide curated creative intelligence shaped by individual artists.
An AI marketplace could allow artists to offer specialized models for subscription, enabling others to build and customize their own creative tools. This system would ensure that artists are rewarded directly for their work, fostering a thriving ecosystem of high-quality, specialized models. Just like platforms such as app stores or Spotify, artists could list their models, set subscription fees, and earn royalties, creating a continuous feedback loop for quality improvement. Additionally, user-friendly platforms could make AI model creation accessible even for those without technical expertise, similar to other SaaS solutions.
This vision could empower content creators to have greater control over their creative process and prevent the dilution of their work. For example, musicians who currently struggle to make a living from streaming services could use decentralized AI models to create unique AI-driven tools that reflect their own sound and style. These models could then be offered for subscription, allowing artists to maintain control over their creativity while being compensated fairly. This concept extends to all kinds of artists, turning creativity into a service where each model embodies an artist’s signature style. This could foster micro-cultures centered around shared AI models, leading to a cultural explosion of diversity instead of homogenization.
By involving artists directly in the evolution of their models, a hybrid approach could effectively address the problem of dilution. Personal models, unlike centralized ones trained on random content, would be curated with high artistic intent and quality. As users provide feedback, each iteration of the model could enhance its unique artistic vision, creating a continuous cycle of improvement.
The hybrid model directly tackles the dilution effect by ensuring artists are actively involved in the evolution of their models. Unlike centralized models trained on random content, these personal models are curated with high artistic intent, retaining originality and quality. With feedback from users, each iteration of the model enhances its distinct artistic vision, creating a continuous cycle of quality improvement.
Managing intellectual property in this new creative ecosystem will require strong frameworks to ensure that artists retain the rights to their models and receive proper credit. While making these models accessible is valuable, unrestricted customization could risk diluting the original quality. Proper boundaries and clear credits will help mitigate this. Additionally, the technical aspects of AI model creation could be intimidating for many artists. User-friendly tools will be essential to make this vision a reality, allowing artists to engage in AI creativity without needing advanced programming skills.
This speculative vision suggests a future where AI-enabled creativity is both personalized and collaborative. By balancing centralized and decentralized approaches, rewarding creativity through subscriptions, and fostering new artistic partnerships, we can counteract the risks of dilution and keep creativity at the forefront. Instead of homogenized content, we could build an ecosystem where AI amplifies human creativity, supporting new ideas, diverse voices, and richer artistic expressions.
The next step is to explore the tools and infrastructure needed to make this vision a reality. How can we empower artists to create, share, and protect their models? Let’s continue the conversation and work towards a creative, decentralized future together.
This text was created based on a discussion about Creativity as the Product – Addressing the Dilution Dilemma using generative AI tools. The insights shared here were developed collaboratively through a dialogue, incorporating work-related examples and iterative refinement. The conversation took place between me(Michael Käppi) and with ChatGPT-4o with Canvas, leveraging its canvas feature to explore these topics interactively, resulting in this post that captures both practical and conceptual shifts in creative work.
As I explore the rise of generative AI, I’ve come to realize it’s triggering a cognitive revolution in how I create content. It challenges traditional workflows and demands a complete shift in my approach to creative work—it’s not just about learning new tools, but rethinking my entire mindset.
What we’re witnessing in content creation is a shift happening at the cognitive level—a fundamental change in how we approach the creative process, not merely a new tool or technique. Generative AI is both powerful and disruptive, and it challenges traditional workflows that often require mastery across multiple domains—be it sound engineering, scriptwriting, or UX design.
Instead of needing deep knowledge in every production step, generative AI demands that we condense and articulate the core intent up front. Imagine having to strip a complex idea down to its bare essentials, like reducing a movie to just a logline that tells you everything you need to know. It’s a lot like first principles thinking—breaking down a concept to its most fundamental components and then building it back up.
For example, in a recent project, I had to describe a tool in just three or four sentences—a task that forced me to:
The AI then took that core insight and turned it into a tangible workflow, visualized and ready to iterate on. It’s a different mental discipline compared to the traditional approach, where creators may get lost in the details of every production step. With generative AI, you keep your focus sharp—on vision and intent—so that the creative energy stays alive, without the burden of executing every technical task yourself.
To better transition to specific examples of how this cognitive shift manifests, let’s explore how workflows and tool mastery come into play. Using generative AI isn’t about just learning a new tool; it’s about creating a workflow that maximizes each tool’s unique strengths. For example, I use Midjourney and Ideogram for initial image generation, and then Krea and Magnific AI for refining and upscaling. Each tool serves a distinct purpose, and being able to curate a toolkit—knowing when to use which tool and why—is essential for achieving high-quality outcomes. The tools are evolving quickly, and so must we. The real power of AI isn’t in the tools themselves; it’s in your ability to guide them—shaping, refining, and combining different parts into a cohesive, impactful whole.
On iterative explorations; Take my tarot card project, for example. I wanted to create a visually consistent yet individually unique deck. DALL-E 3 had the exact visual style I was after, so even though it was slower compared to Midjourney, I chose it because it matched my vision. Each card had its own unique composition and symbolism, and to achieve this, every text prompt had to be meticulously prepared, tested, and iterated upon. This often meant generating 50-150 images to get each card just right. This project was a classic case of balancing speed versus artistry. Yes, Midjourney would have been faster, but DALL-E 3 provided the specific quality I was looking for. Sometimes, it’s not about how quickly you can finish a project; it’s about staying true to the vision and putting in the time to get it right.
To better illustrate the differences between traditional and generative AI-driven content creation workflows, consider the following visual representation:
Building on these practical workflows, it’s crucial to understand how our entire mindset around creativity needs to adjust to accommodate these new processes. And it’s indeed a 180-degree shift from traditional approaches:
Having established the importance of maintaining the core vision, let’s look at how we adapt our cognitive approach and practical methods to fit this new creative paradigm. The biggest challenge with generative AI lies at the cognitive level—changing how we think and how we approach creativity. This shift can feel unsettling, especially for those of us used to traditional workflows that are more linear and detail-heavy. Instead of taking a step-by-step journey, AI encourages an outcome-first approach:
Once we recognize the shift in mindset, another key aspect is maintaining the integrity of the core idea throughout complex creative projects. One of the biggest challenges in traditional creative processes—especially when working on complex projects like music production or UX design—is maintaining the core narrative, the guiding vision that ties everything together. It’s easy to lose sight of your original idea when you’re switching hats constantly—jumping from producing to mixing, or from designing to coding, each requiring different mental skillsets.
Generative AI helps maintain this objectivity. It keeps your core vision intact while handling some of the execution details. It’s like having a creative assistant who handles the repetitive bits, allowing you to keep your focus on the big picture and on how each piece fits within that larger vision. You can iterate quickly, test variations, and see a version of your outcome much sooner—without getting bogged down in every small decision. AI keeps the core narrative visible, allowing you to refine without losing your place in the creative journey.
Beyond adapting processes, leveraging synthesis and context becomes vital—these are the superpowers that humans bring to AI-driven creativity. In this new landscape, synthesis and understanding context are emerging as crucial creative skills. It’s not enough to just know how to use tools; you need to connect the dots—to see patterns, to create meaning, and to bring everything together cohesively. Generative AI can generate components, but it’s the human ability to synthesize that adds depth and significance.
In my own work, synthesis often begins with a blank canvas—gathering insights, grouping them, trying out different arrangements until something clicks. Whether it’s visual styles, interview findings, or symbolic concepts, it’s about constantly arranging and rearranging elements until the story makes sense. This is also where context comes in—understanding the context helps determine how each piece should be arranged for maximum impact. Generative AI can offer initial ideas, but it’s the synthesis and contextual understanding that make the real magic happen.
The essence of generative AI lies in collaboration—where AI augments human creativity rather than replacing it. This partnership allows creatives to shift their focus from repetitive, technical execution to high-level ideation and exploration. By collaborating with AI, we are effectively extending our creative toolkit to include not only traditional tools and techniques but also intelligent systems capable of generating, iterating, and refining content.
This collaboration isn’t a one-way street; it’s about active participation. AI provides the rapid generation of ideas, but it’s our role as creatives to direct, filter, and shape these ideas to meet our vision. The magic lies in how we interact with what the AI generates—by enhancing, reimagining, and synthesizing it into something uniquely human.
Generative AI opens new doors for creativity by offering multiple perspectives at lightning speed, thereby expanding the scope of what’s possible. We can test different styles, experiment with iterations, and refine ideas without the constraint of traditional linear workflows. This approach accelerates the creative process and encourages a more fluid, experimental mindset.
Ultimately, collaborative creativity is about embracing unpredictability. With AI by our side, we have to learn to value the unexpected. The creative journey is no longer strictly under our control, but that unpredictability brings new opportunities for inspiration. The future of creativity is not AI versus human—it’s AI and human working in synergy. Together, we are capable of pushing the boundaries of imagination, innovating in ways that were previously unimaginable.
This shift isn’t just about new tools—it’s about breaking old habits and adopting a completely new way of thinking. The traditional ways we’ve learned to approach creativity are being upended, and this is both an internal and external journey. For many, finding this new way forward will be challenging, as it requires us to not only learn new tools but also to rewire our brains to adapt to a new reality.
The cognitive shift demands new skill sets—ones that focus less on linear processes and more on intent, adaptability, and AI collaboration. It’s about embracing ambiguity, leaning into iteration, and redefining what it means to create. The new creative toolbox is full of unexpected and evolving tools, but the biggest change lies within our own capacity to think differently, to adapt, and to thrive in a reality where AI plays an active role in creativity.
This text was created based on a discussion about the creative shift in content creation using generative AI tools. The insights shared here were developed collaboratively through a dialogue, incorporating work-related examples and iterative refinement. The conversation took place between me(Michael Käppi) and with ChatGPT-4o with Canvas, leveraging its canvas feature to explore these topics interactively, resulting in this post that captures both practical and conceptual shifts in creative work.
DeepState Empire brings a haunting, dystopian edge to the world of metal music. With deep, pulsing beats, pushy guitars and an eerie soundscape, their tracks confront themes of societal control, disillusionment, and the echoes of voices lost in the digital age. Each track invites you to explore a different dimension of a world slipping into the “idiocracy” they narrate.
DeepState Empire is a fictional creation, brought to life through a collaboration between myself and the GenAI tool; Udio 130 v.1.50 (on a 48kHz sample rate). This project serves as an exploration of how AI-driven music tools can craft compelling narratives and powerful sonic worlds. I created DeepState Empire not just for the listening experience but as a means to evaluate the quality and performance of GenAI music tools. I try to apply a light-weighted, generalized variant of the 5-point music analysis method to examining how well the AI captures the distinctive elements that define each genre and style. The evaluation result is addressed for the collection and not for the individual tracks.
Sound Design: In general, Udio truly excelled in creating rich, atmospheric stereo soundscapes. Each track varies in the sound changes, character, space & volume. One some of the songs the panning’s and stereo width was used effectively between song parts and instruments, which created a richer variation of the arrangement and composition. Especially needed as this gener is kind of a challenge when it comes to mixing, due the number of instruments fighting over the same frequency span. In that aspect, I’m still stunned that the AI can create this kind of results. The guitar sound on some of the songs is truly amazing. “Idiocracy” was created as a remix of an older track and apparently, somehow, the second takes get lesser sound quality. This has been my experience, lately.
Genre Adherence: Udio recognized the typical variations between parts associated with this type of geners; half-time tempo & double-kicks switches, strong bridges and other attributes. Not so much long guitar solo and occasionally there where some guitar riffing intros that had a couple of hickups. Not so perfect, but somewhat a milder hickup could actually be realifing and add a bit of authentism to the whole experience. Sometimes I wonder if the common listener even hears this?
Emotional Impact: For the most of the songs, they had strong, dynamical voices and used them dramaturgically togheter with the lyrics. The lyrics that was prompted here was meant to be more emotional to give better push opportunities for the song dynamics. This song collection contains a lot of good voice takes, as this gener is very demanding on delivery power & dynamic vocals to be true worthy.
Melodic Variation: In this collection I can say that it took like 500 songs to find good enough material. Udio feels a little weak on this part compared to others. But they “rule” on all other parts when it comes to this music gener.
Arrangement Complexity: Some structural elements still fels repetitive, with limited experimentation in rhythm and transitions, resulting in tracks that could feel predictable over time. But when it comes to variations on a song, then Udio is the best tool so far. Sometimes you get result that you really expected at all. “Idiocracy” had strong elements of Eurovision influences in choruses as the bridge parts mixed with Whitesnake-like verse riffs. “As we all keep falling” strongly reminds me of a Nickelback sound and the use of stereo-widening vocals.
Expressive Nuance: It feels like Udio has a better understanding on what’s important. They favor the importance of vocals as it is louder than other compatitors. This probably increases dynamics and balances the mixes better so the limiter/clipper doesn’t cracks up the output. But one big issue that I can see on the current Udio model version is that it has problems with the timing on vocals. Sometimes it feels that coming late between verses and chorus parts and it in general unsynchronized. Something you could fixed with stems afterwards. I think this is the biggest challenge for Udio just now.
The exploration of DeepState Empire has really giving me a glimpse into how well AI can emulate the core elements of genre and style. Worth mention is that the aim of this music album was to have conceptual approach. The creation of lyrics, logotype, together with expressive images that took some time to complete. This because I wanted to show how powerful ALL PARTS ARE for the whole experience as it makes the package complete. This work was for pure fun & pleasure, as I learned a lot doing it!
ArtPopies revives the soul of early 70s art pop and progressive rock, echoing the experimental spirit of legends like David Bowie, Genesis, and Yes. Their music explores cosmic themes, emotional introspection, and transcendent journeys that bridge the personal with the surreal. With layered instrumentation, ethereal melodies, and lyrics that hint at the mysteries of existence, ArtPopies pulls listeners into a world that feels both timeless and otherworldly.
ArtPopies is a fictional creation, brought to life through a collaboration between myself and AI. This project is a musical experiment designed to test the boundaries of AI-driven music tools. By drawing inspiration from the evocative sounds & themes of early art pop & progressive rock, I created ArtPopies not only as an homage to this genre but also to evaluate the quality and performance of GenAI music tools. Each track is analyzed using a 5-point technique, assessing how well the AI captures the distinct qualities that define art pop & progressive rock. AI tool: Udio v.1.5 (48kHz Sample Rate)
Genre Adherence: The AI captures the essence of 70s art pop and progressive rock remarkably well, incorporating lush melodies and complex arrangements that nod to the genre’s iconic structure.
Atmosphere & Soundscape: Tracks like “Cosmic haze” excel in creating a cosmic, immersive soundscape that transports listeners beyond the ordinary. I was kind of perplexed that it brought the icons key changes, from major to minor between verse and pre-chorus as some odd back/offbeats.
Melodic Depth: High scores in melodic intricacy and emotional impact, especially in conveying the introspective and cosmic themes characteristic of classic art pop and prog rock.
Dynamic Variation: The AI could benefit from more varied dynamics to evoke the ebb and flow typical of progressive rock compositions. It was hard to get good reslults but in general I’m really supprised that it actually pulled this off.
Expressive Detail: Certain subtle elements, like the expressive nuances of analog instruments, are less pronounced, which slightly diminishes the authenticity.
Lyrical Ambiguity: While the lyrics evoke themes of cosmic exploration, they sometimes lack the layered ambiguity and poetic depth that define the genre’s most iconic tracks.
ArtPopies offers a look at how GenAI can interpret the foundational elements of a genre while leaving room for growth in expressive depth. Through this project, tested against the 5-point technique, we can see both the potential and the limitations of AI in recreating the essence of an era.
