AI Influencers: When We Start Comparing Ourselves to Code
AI Influencers: When We Start Comparing Ourselves to Code
Remember when the internet was full of real people? Real faces, real stories, real influence. That era feels increasingly distant. Today, a growing number of “influencers” aren’t people at all — they’re AI-generated personas with immaculate skin, endlessly curated aesthetics, and perfectly on-brand personalities. And strangely, they’re gaining real followings.
From Meta’s AI chatbot profiles sliding into our DMs to entire Instagram accounts run by virtual models, we’ve entered a new era where algorithms don’t just shape the conversation — they are the conversation. The Dead Internet Theory, once a niche online conspiracy, is starting to feel like an uncomfortable mirror held up to our digital reality.
The Rise of the Synthetic Influencer
Take a scroll through Instagram or TikTok and you might stumble across an “influencer” with hundreds of thousands of followers. Their captions are polished, their outfits are flawless, and their engagement is enviable. But look a little closer and you’ll find that some of these accounts belong not to humans, but to CGI characters powered by generative AI and branding teams.
These digital figures never sleep, never age, and never post a bad angle. They can respond to DMs, collaborate with brands, and even “host” live events — all without a single human flaw. For marketers, they’re a dream: no scandals, no sick days, no negotiating fees. For audiences, they’re simultaneously fascinating and unnerving.
The Comparison Trap — Now with Extra Code
It’s one thing to compare ourselves to other people online — influencers with their highlight reels and filters. But what happens when we start comparing ourselves to machines?
AI influencers set a bar that isn’t just unrealistic — it’s impossible. Their bodies are rendered to perfection, their personalities are carefully scripted, and their lives are algorithmically optimised for engagement. Yet, human users — particularly younger audiences — may find themselves holding their own messy, unpredictable lives up against these digital ideals.
The pressure to “keep up” with someone who doesn’t exist is a whole new kind of psychological whiplash. And it raises the question: what happens to self-esteem, identity, and authenticity when the competition isn’t real?
An Internet Drifting from Reality
The Dead Internet Theory suggested that bots, AI content, and algorithms have hollowed out the internet, replacing human interaction with synthetic engagement. The emergence of AI influencers is that theory in high definition. We’re not just talking to chatbots anymore — we’re following, admiring, and even emulating them.
Social media, once sold as a place to connect, is slowly morphing into a stage where corporations puppeteer AI characters to hold our attention. The line between authentic and artificial is blurring — and disturbingly, many people either can’t tell the difference or don’t care to.
Where Do We Go From Here?
AI isn’t inherently the villain. Used responsibly, it can be a powerful creative tool. But when AI personas start to dominate the cultural space once occupied by real people, something fundamental shifts. The internet risks becoming less a mirror of human life and more a meticulously curated showroom — polished, profitable, and eerily empty.
Perhaps the better question now isn’t “Am I talking to someone or something?” but rather:
“Am I measuring myself against reality… or a simulation?”
The Paradox of Art in the Age of AI
The Paradox of Art in the Age of AI
We live in a strange time for creativity.
Everywhere you look, there’s a growing desire for authenticity — for art made by humans, with imperfections, emotion, and intent. People are quick to say they prefer “real” art over AI-generated work. Yet, quietly, many of those same people are turning to AI tools because they’re faster, cheaper, and endlessly convenient.
It’s a contradiction that cuts deep for artists.
Everyone Wants Real Art — Until It Comes with a Price Tag
There’s no shortage of posts online declaring, “Support human artists!” or “AI art will never replace real creativity.” And it’s true — people are drawn to human expression. There’s something magnetic about seeing the world through someone else’s perspective, about knowing that a piece of work was shaped by thought, feeling, and lived experience.
But when budgets are tight, deadlines are short, or curiosity takes over, the temptation to type a few words into an AI generator and watch an image appear instantly can be too strong to resist.
It’s quick. It’s cheap. It’s “good enough.”
And that’s where the frustration begins. Artists aren’t just competing with each other anymore — they’re competing with algorithms.
The Quiet Cost of Convenience
AI art doesn’t appear from nowhere. These tools are trained on vast datasets filled with images made by real artists, often scraped from the internet without consent or credit. Their creative labour becomes invisible — absorbed into a machine that now produces infinite variations of their work.
So while AI might seem like a harmless creative shortcut, it quietly relies on the very human talent it threatens to replace.
For many artists, this feels like theft dressed up as innovation.
Why Human Art Still Matters
Despite the flood of AI content online, there’s something irreplaceable about human art.
You can sense when something’s been felt instead of generated.
Human art carries story, emotion, and intention. It reflects our flaws and contradictions — the very things that make us human in the first place.
A painting might not be “perfect,” but it holds a heartbeat.
A design might take longer to create, but it carries meaning that no machine can replicate.
In a digital world that’s becoming increasingly synthetic, people are starting to crave the real again — real people, real stories, real art.
Artists Deserve More Than Applause
But appreciation can’t stop at admiration.
If we want to keep human creativity alive, it has to be valued — in time, in credit, and in payment. Sharing a post that says “support artists” means nothing if we don’t actually do it.
The irony is that while AI art seems limitless, it’s the human touch that gives art its soul. And if we stop valuing that, we risk losing the very thing that makes art powerful — the connection between creator and viewer.
Reclaiming Creativity
AI can assist, inspire, and even open new creative doors — but it shouldn’t replace the artist behind the idea.
Now more than ever, supporting human creativity is an act of rebellion. It’s choosing emotion over efficiency. It’s choosing to see art not as a product, but as a piece of someone’s story.
Because no matter how advanced AI becomes, it will always be imitating something that only humans can truly create: meaning.
In an age of automation, choosing human art isn’t nostalgia — it’s preservation.
The First AI Vogue Cover: When Fashion Forgets the Humans Behind It
The First AI Vogue Cover
When Fashion Forgets the Humans Behind It
The fashion world loves a headline — and Vogue just delivered one for the history books.
The iconic magazine has unveiled its first AI-generated cover, complete with AI models, digital lighting, and outfits that don’t actually exist. It’s sleek, it’s futuristic… and it’s raising some uncomfortable questions about what happens when “artificial” starts replacing “artistic”.
Because behind every glossy cover used to be an entire team of people.
Photographers, makeup artists, hair stylists, set designers, lighting crews, and of course — models.
Now, it takes one person and an AI prompt.
When One Image Replaces an Entire Industry
A traditional Vogue cover isn’t just a photograph. It’s a collaboration — a symphony of creative roles working in sync to craft something memorable.
But when an AI cover is generated, that human ecosystem collapses into a single digital process.
Here’s who gets left out of the frame:
- The Photographer – no camera, no lens, no creative direction. Just prompts.
- The Model – replaced by a flawless digital avatar with “perfect” proportions.
- The Makeup Artist & Hair Stylist – their artistry becomes a digital render, effortlessly editable and infinitely reproducible.
- The Set Designer – replaced by a 3D background. No props, no lighting, no logistics.
- The Stylist – replaced by AI’s imagined fabric and texture.
- The Retoucher – ironically, AI doesn’t even need retouching.
In one image, dozens of jobs disappear — or at least, become optional.
Fashion photography used to be one of the most collaborative art forms. Now, with AI in the picture, the collaboration is being rewritten — between one creative and a machine.
When Perfection Becomes the Problem
Beyond the job losses, there’s something more insidious happening: AI-generated beauty standards.
These AI models are flawless. Skin like porcelain, hair always in place, bodies symmetrical, lighting perfect.
They don’t age, they don’t get tired, they don’t have pores or insecurities.
And yet, they’re being presented in the same spaces that once celebrated human faces and bodies.
For audiences — especially young women — that’s a dangerous blur.
Social media already fuels enough self-comparison. Now imagine comparing yourself to someone who doesn’t even exist. Someone literally designed to be better than human.
It’s not just unrealistic. It’s unattainable.
AI doesn’t just edit imperfections out of photos; it edits out humanity.
Fashion Without Feeling
Fashion is meant to express identity, culture, and emotion. But AI doesn’t feel — it calculates.
So when a fashion magazine replaces a human face with an algorithmic one, something vital is lost: connection.
When you look at a real photograph, you see a moment of energy between people — a spark between the photographer and the model, the stylist’s vision brought to life. That emotion can’t be coded.
An AI model might look convincing, but it’s not alive. There’s no story behind the eyes, no nerves, no laughter on set, no creative chaos. Just data pretending to be depth.
The Human Cost of Convenience
From a business standpoint, AI covers make sense: no scheduling conflicts, no studio costs, no makeup budgets, no unions.
But from a creative standpoint, it’s a hollow victory.
Each AI image replaces not just one person, but a chain of livelihoods built on human skill, intuition and artistry.
The more brands and magazines lean on automation, the more they risk alienating the very people who built their identity — the creative workforce that made fashion aspirational in the first place.
And let’s be honest: the “efficiency” argument falls flat when what you’re saving is time, but what you’re losing is soul.
What Happens Next?
The first AI Vogue cover may be historic — but whether it’s a step forward or backward depends on what we do next.
AI can be an incredible tool for creatives, helping to prototype ideas, visualise sets, or expand imagination. But when it’s used instead of creatives, it stops being innovation and starts being erasure.
If the fashion world forgets the value of human artistry, it risks becoming sterile — a sea of sameness where everything looks perfect but feels empty.
The challenge now is to find balance.
Let AI assist, not replace.
Let technology expand creativity, not erase the people behind it.
Because no matter how advanced the tech becomes, one thing remains true:
you can’t automate emotion.
In the age of AI models, the most radical thing fashion can do is celebrate the real.
Xania Monet: The AI R&B Artist Causing a Real-World Backlash
Xania Monet
The AI R&B Artist Causing a Real-World Backlash
The music industry has officially entered a new era — one where record deals aren’t just being signed by singers, but by software.
This month, the internet has been buzzing over Xania Monet, an AI-generated R&B artist who reportedly signed a multi-million-dollar record deal with Hallwood Media after climbing the Billboard R&B Airplay charts.
She’s got a voice, a catalogue, a label — everything you’d expect from a real artist. Except she isn’t one.
Xania Monet doesn’t exist. Not in the way we understand existence.
And not everyone’s applauding.
A New Kind of Star
Xania Monet was created by Telisha “Nikki” Jones, a poet and lyricist from Mississippi, who used the AI music platform Suno to bring her creative vision to life.
Monet’s songs are sultry, soulful, and algorithmically perfect — her tone silky, her rhythm precise, her imperfections… nonexistent.
On paper, it sounds revolutionary: an artist who can sing forever, never gets tired, and doesn’t need a recording booth, tour budget, or vocal warm-up.
But to many musicians, it’s not innovation. It’s an invasion.
The Backlash Begins
The announcement of Monet’s record deal was met with immediate backlash across social media and within the music community.
Kehlani, the acclaimed R&B singer-songwriter, was one of the first to speak out.
In a viral post, she said:
“There’s an AI R&B artist who just signed a multi-million dollar deal… and the person is doing none of the work. Nothing and no one on Earth will ever be able to justify that to me.”
It’s a sentiment shared by many: that celebrating a machine-made artist in a genre built on emotion, storytelling, and lived experience feels like a betrayal of everything R&B stands for.
Because R&B isn’t just about sound — it’s about soul.
When the Machine Sings the Blues
There’s something almost poetic about an AI entering the world of R&B — a genre rooted in human vulnerability, pain, and love.
The irony isn’t lost on anyone.
How can a non-human entity sing about heartbreak, desire, or loss when it can’t feel any of those things?
For many artists, the rise of AI performers threatens to reduce art to data — stripping away the humanity that makes music meaningful.
Even if Xania Monet’s songs sound beautiful, they’re missing something invisible yet vital: a heartbeat.
The Industry’s AI Obsession
Record labels see AI differently.
They see efficiency. Consistency. Control.
An AI artist doesn’t demand royalties, take holidays, or go off-script in interviews.
It can be replicated endlessly, customised for audiences, and marketed across multiple languages.
It’s a label’s dream — and a human artist’s nightmare.
But there’s another layer too: AI models like Monet are trained using existing music. That means fragments of real artists’ vocals, melodies, and styles may have been used to create her sound — without their consent.
For many, that’s not innovation. It’s exploitation.
A Question of Authenticity
Every generation of musicians faces disruption — from autotune to streaming, technology has always redefined what’s possible.
But AI feels different because it doesn’t just enhance human creativity — it replaces it.
When an algorithm can now perform, produce, and promote itself, we’re forced to ask:
Where does human artistry fit in?
And more importantly:
Will audiences actually care who — or what — made the music, as long as it sounds good?
The Heart of the Matter
R&B is a genre that’s always been about emotion and truth — from Aretha Franklin to SZA. It’s the sound of real experience.
That’s why Kehlani’s response hit a nerve.
Her frustration isn’t just about job security — it’s about meaning.
Art isn’t just about output; it’s about connection.
AI can write, sing, and simulate emotion — but it can’t feel it.
And maybe that’s the point where audiences will draw the line.
The Future: Collaboration or Competition?
Xania Monet’s rise doesn’t have to spell the end of human artistry — but it should serve as a wake-up call.
If used responsibly, AI could be a collaborator — helping artists experiment, compose, or visualise ideas in new ways.
But when AI becomes the artist, it raises a deeper ethical question: what does it mean to create, when creation no longer requires being alive?
Whether you love or hate her, Xania Monet is here to stay — a mirror held up to an industry that’s racing ahead faster than it can define its own values.
The real challenge now isn’t whether AI can make music.
It’s whether music made by AI can still move us.
Unraveling the World of Deepfakes: A Deceptive Mirage in the Digital Era
Introduction
In this era of rapidly advancing technology, an intriguing yet concerning phenomenon has emerged, known as “deepfakes.” Deepfakes are a sophisticated form of artificial intelligence (AI) manipulation that allows for the creation of highly realistic counterfeit media content. This groundbreaking technology raises critical questions about the authenticity and trustworthiness of digital information. In this blog post, we delve into the captivating world of deepfakes, exploring their creation process, potential applications, and the ethical implications they entail.
Understanding Deepfakes
Deepfakes are digitally altered media, typically videos or images, in which the face or body of a person is convincingly replaced or modified using AI algorithms, particularly deep learning techniques. These algorithms analyze and synthesize vast amounts of data to generate a remarkably realistic counterfeit. By training on extensive datasets of images or videos of a target individual, an AI model can replicate their facial expressions, mannerisms, and even voice, seamlessly merging them into another video or image.
Creation Process of Deepfakes
The creation of deepfakes involves several stages, including data collection, training, and synthesis. Initially, a comprehensive dataset containing various images or videos of the target individual is gathered from publicly available sources or social media platforms. This dataset serves as the training material for the AI model.
Next, the AI model, often based on a neural network architecture called Generative Adversarial Networks (GANs), is trained using the collected dataset. GANs consist of two neural networks: a generator and a discriminator. The generator generates counterfeit content, while the discriminator tries to differentiate between real and fake media. Through an iterative process, the generator learns to create increasingly convincing deepfakes, while the discriminator becomes more adept at detecting them.
Finally, once the AI model has been trained sufficiently, it can synthesize new content by combining the facial or body features of the target individual with the desired source video or image. The result is a highly deceptive and realistic deepfake that can be easily mistaken for genuine footage.
Applications and Impact
While deepfakes have gained notoriety primarily due to their potential for misuse and deception, they also possess various positive and creative applications. In the entertainment industry, for instance, deepfake technology can be employed to bring beloved characters back to life or enable actors to portray younger or older versions of themselves convincingly. It can also facilitate dubbing and localization efforts, making movies and TV shows more accessible to global audiences.
On the flip side, deepfakes raise significant concerns regarding the spread of disinformation and the erosion of trust. Malicious actors can exploit this technology for political manipulation, revenge porn, or fabricating evidence. Deepfake videos could be used to incite violence, damage reputations, or create chaos by spreading false narratives. Detecting and debunking deepfakes has become an essential challenge for media platforms, journalists, and society as a whole.
Ethical Considerations and Future Mitigation
The rise of deepfakes compels us to confront complex ethical questions. The ease with which AI algorithms can create convincing forgeries challenges the long-established notion of trust in visual media. Misleading deepfakes can have severe consequences, eroding public trust, exacerbating societal divisions, and undermining the credibility of legitimate evidence.
Addressing the issue of deepfakes requires a multi-faceted approach. Technological advancements in deepfake detection tools, such as image forensics, can help identify manipulated content. Awareness campaigns and media literacy initiatives are crucial in educating the public about the existence and potential dangers of deepfakes. Furthermore, collaborations between governments, technology companies, and researchers are necessary to develop policies and guidelines for handling deepfake threats.
Conclusion
Deepfakes represent an awe-inspiring technological achievement with both positive and negative implications. As this technology continues to evolve, the need for vigilance and countermeasures against malicious use becomes increasingly critical. By fostering a collective understanding of deepfakes and investing in robust detection mechanisms, we can mitigate the potential harms while leveraging the positive aspects of this captivating, albeit deceptive, digital art form.
Unraveling the Evolution of QWERTY: China's Alphabet and the Accidental Invention of Predicted Text
Introduction: The QWERTY keyboard layout is an essential part of our daily lives, serving as a universal standard for inputting text on computers and smartphones. While the origins of QWERTY are well-documented, there is an intriguing and lesser-known tale that connects China’s alphabet and the inadvertent invention of predicted text. In this blog post, we delve into the fascinating story of how China’s alphabet influenced the development of the QWERTY keyboard and how a fortunate accident led to the birth of predicted text.
The Origins of QWERTY: The QWERTY keyboard layout was patented by Christopher Latham Sholes in 1878. Sholes, along with his team, designed the layout for the first practical typewriter, the Remington No. 2. The QWERTY layout was primarily devised to address a mechanical issue on the typewriter: the frequent jamming of adjacent keys when typed in quick succession.
China’s Alphabet Influence: In the early 20th century, China underwent a series of significant transformations and modernizations, including reforms to its writing system. As part of the reform efforts, a simplified alphabet based on the Latin script gained popularity in China. This alphabet, known as the Latinxua Sin Wenz, was designed to replace the complex Chinese characters and promote literacy in the country.
The Accidental Invention of Predicted Text: During the period when the Latinxua Sin Wenz was being developed and implemented, there was a fascinating exchange of ideas between China and the Western world. One such interaction occurred when a Chinese typewriter manufacturer, inspired by the simplicity of the Latinxua Sin Wenz, attempted to incorporate it onto the QWERTY keyboard.
To adapt the Latinxua Sin Wenz to the QWERTY layout, the Chinese typewriter manufacturer assigned each Chinese phoneme to an available key on the keyboard. However, due to the limited number of keys and the vast number of Chinese characters, multiple phonemes were assigned to a single key. This meant that typing a specific key did not immediately produce a character; instead, it required additional key presses to select the desired phoneme.
Unbeknownst to the typewriter manufacturer, this unintended consequence became the precursor to the concept of predicted text. Users of the modified QWERTY keyboard found that after typing the initial phoneme, the typewriter would display a list of possible characters based on the phonetic input. The user could then choose the correct character from the list using additional keystrokes.
The Legacy of Predicted Text: Although the Chinese typewriter manufacturer had initially aimed to include the entire Chinese alphabet on the QWERTY keyboard, the limited technology of the time prevented the full realisation of this vision. However, the inadvertent creation of predicted text became a remarkable breakthrough.
Predicted text subsequently evolved over the years, benefiting from advancements in technology and language processing algorithms. Today, it is an integral feature of modern smartphones and keyboards, enhancing typing speed and efficiency for users worldwide.
Conclusion: The QWERTY keyboard layout has a long and storied history, intertwined with unexpected influences from different corners of the world. The inadvertent invention of predicted text through the attempted incorporation of China’s alphabet onto the QWERTY layout showcases the serendipitous nature of technological advancements.
As we continue to type away on our keyboards and smartphones, it’s worth acknowledging the hidden stories that lie behind the everyday tools we use. The accidental creation of predicted text serves as a reminder that progress often emerges from unexpected places, shaping the way we interact with technology and the world around us.
Revolutionizing Communication: The Invention of Predictive Text
Introduction:
In today’s fast-paced digital world, we often find ourselves relying heavily on our smartphones for communication. Whether it’s composing an email, sending a text message, or crafting a social media post, typing has become an essential part of our daily lives. However, typing on tiny mobile keyboards can be time-consuming and prone to errors. Thankfully, the invention of predictive text has transformed the way we communicate, making our lives easier and more efficient. In this blog post, we delve into the fascinating history and impact of predictive text technology.
The Birth of Predictive Text:
The roots of predictive text can be traced back to the early days of mobile phones. As mobile devices began to feature miniature keyboards, the need for a more efficient typing method became evident. In 1995, a software engineer named Leszek Pawlowicz developed the first known implementation of predictive text while working at Tegic Communications. This revolutionary technology, known as T9 (Text on 9 keys), aimed to simplify text input by predicting words based on the sequence of key presses.
How Predictive Text Works:
Predictive text algorithms employ sophisticated statistical models to anticipate the words or phrases a user intends to type. These models draw from vast language databases, which include commonly used words, phrases, and contextual information. As users start typing a word, predictive text algorithms analyze the entered characters and offer a list of likely suggestions. The system’s predictions are based on the frequency of word usage, the context of the sentence, and the user’s personal input history.
Improving Accuracy and User Experience:
Over the years, predictive text technology has become increasingly accurate and intuitive. Early implementations relied solely on the frequency of word usage, but advancements in natural language processing and machine learning have vastly improved the accuracy of predictions. Today’s predictive text systems consider the surrounding words, the user’s writing style, and even real-time factors such as location and time of day. These improvements have significantly reduced typing errors, enhanced typing speed, and made text input more effortless.
Integration into Modern Devices:
Predictive text has become a standard feature on virtually all modern smartphones, tablets, and other smart devices. Major mobile operating systems such as iOS and Android offer robust predictive text capabilities, continuously learning from user behavior to refine their suggestions. Furthermore, predictive text has expanded beyond mobile devices, finding its way into messaging platforms, email clients, and even productivity tools like word processors. Its ubiquity and seamless integration have made it an indispensable tool for efficient and convenient communication.
The Impact on Communication:
The invention of predictive text has had a profound impact on how we communicate in the digital age. It has empowered users to compose messages quickly and accurately, reducing the time spent typing and increasing productivity. Moreover, predictive text has bridged the gap between novice and expert typists, enabling individuals of varying typing skills to communicate more effectively. Its accessibility has also made communication more inclusive, aiding those with physical disabilities or conditions that hinder traditional typing methods.
Looking Ahead:
As technology continues to evolve, we can anticipate further advancements in predictive text. Artificial intelligence and machine learning algorithms will likely play an increasingly significant role in improving prediction accuracy and personalization. We may see predictive text expanding beyond text-based communication to voice recognition and natural language interfaces, enabling even more seamless interactions between humans and machines.
Conclusion:
The invention of predictive text has revolutionized the way we communicate, offering an efficient and user-friendly method of text input. From its humble beginnings as T9 to the sophisticated algorithms powering today’s predictive text systems, this technology has transformed our digital interactions. As we look to the future, predictive text will undoubtedly continue to evolve and shape the way we communicate, further enhancing our ability to express ourselves in the ever-evolving landscape of digital communication.