Tools & Platforms
The uproar over Vogue’s AI-generated ad isn’t just about fashion

Sarah Murray recalls the first time she saw an artificial model in fashion: It was 2023, and a beautiful young woman of color donned a Levi’s denim overall dress. Murray, a commercial model herself, said it made her feel sad and exhausted.
The iconic denim company had teamed up with the AI studio Lalaland.ai to create “diverse” digital fashion models for more inclusive ads. For an industry that has failed for years to employ diverse human models, the backlash was swift, with New York Magazine calling the decision “artificial diversity.”
“Modeling as a profession is already challenging enough without having to compete with now new digital standards of perfection that can be achieved with AI,” Murray told TechCrunch.
Two years later, her worries have compounded. Brands continue to experiment with AI-generated models, to the consternation of many fashion lovers. The latest uproar came after Vogue’s July print edition featured a Guess ad with a typical model for the brand: thin yet voluptuous, glossy blond tresses, pouty rose lips. She exemplified North American beauty standards, but there was one problem — she was AI generated.
The internet buzzed for days, in large part because the AI-generated beauty showed up in Vogue, the fashion bible that dictates what is and is not acceptable in the industry. The AI-generated model was featured in an advertisement, not a Vogue editorial spread. And Vogue told TechCrunch the ad met its advertising standards.
To many, an ad versus an editorial is a distinction without a difference.
TechCrunch spoke to fashion models, experts, and technologists to get a sense of where the industry is headed now that Vogue seems to have put a stamp of approval on technology that’s poised to dramatically change the fashion industry.
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They said the Guess ad drama highlights questions arising within creative industries being touched by AI’s silicon fingers: When high-quality creative work can be done by AI in a fraction of the time and cost, what’s the point of humans? And in the world of fashion, what happens to the humans — the models, photographers, stylists, and set designers — performing those jobs?
“It’s just so much cheaper”
Sinead Bovell, a model and founder of the WAYE organization who wrote about CGI models for Vogue five years ago, told TechCrunch that “e-commerce models” are most under threat of automation.
E-commerce models are the ones who pose for advertisements or display clothes and accessories for online shoppers. Compared to high-fashion models, whose striking, often unattainable looks are featured in editorial spreads and on runways, they’re more realistic and relatable.
“E-commerce is where most models make their bread and butter,” Bovell said. “It’s not necessarily the path to model fame or model prestige, but it is the path for financial security.”
That fact is running in direct contrast to the pressure many brands feel to automate such shoots. Paul Mouginot, an art technologist who has worked with luxury brands, said it’s simply expensive to work with live models, especially when it comes to photographing them in countless garments, shoes, and accessories.
“AI now lets you start with a flat-lay product shoot, place it on a photorealistic virtual model, and even position that model in a coherent setting, producing images that look like genuine fashion editorials,” he told TechCrunch.
Brands, in some ways, have been doing this for a while, he said. Mouginot, who is French, cited the French retailer Veepee as an example of a company that has used virtual mannequins to sell clothes since at least 2013. Other notable brands like H&M, Mango, and Calvin Klein have also resorted to AI models.
Amy Odell, a fashion writer and author of a recently published biography on Gwyneth Paltrow, put it more simply: “It’s just so much cheaper for [brands] to use AI models now. Brands need a lot of content, and it just adds up. So if they can save money on their print ad or their TikTok feed, they will.”
PJ Pereira, co-founder of AI ad firm Silverside AI, said it really comes down to scale. Every conversation he’s had with fashion brands circles around the fact that the entire marketing system was built for a world where brands produced just four big pieces of content per year. Social media and e-commerce has changed that, and now they need anywhere from 400 to 400,000 pieces; it’s too expensive for brands, especially small ones, to keep up.
“There’s no way to scale from four to 400 or 400,000 with just process tweaks,” he added. “You need a new system. People get angry. They assume this is about taking money away from artists and models. But that’s not what I’ve seen.”
From “diverse” models to AI avatars
Murray, a commercial model, understands the cost benefits of using AI models, but only to an extent.

She lamented that brands like Levi’s claim AI is only meant to supplement human talent, not take away.
“If those [brands] ever had the opportunity to stand in line at an open casting call, they would know about the endless amounts of models, including myself, that would dream of opportunities to work with their brands,” she said. “They would never need to supplement with anything fake.”
She thinks such a shift will impact “non-traditional” — think, diverse — commercial models, such as herself. That was the main problem with the Levi’s ad. Rather than hiring diverse talent, it artificially generated it.
Bovell calls this “robot cultural appropriation,” or the idea that brands can just generate certain, especially diverse, identities to tell a brand story, even if the person who created the technology isn’t of that same identity.
And though Pereira argues that it’s unrealistic to shoot every garment on every type of model, that hasn’t calmed the fears many diverse models have about what’s to come.
“We already see an unprecedented use of certain terms in our contracts that we worry indicate that we are possibly signing away our rights for a brand to use our face and anything recognizable as ourselves to train their future AI systems,” Murray said.
Some see generating likenesses of models as a way forward in the AI era. Sara Ziff, a former model and founder of the Model Alliance, is working to pass the Fashion Workers Act, which would require brands to get a model’s clear consent and provide compensation for using their digital replicas. Mouginot said this lets models appear at several shoots on the same day and possibly generate additional income.
That’s “precious when a sought-after model is already traveling constantly,” he continued. But at the same time, whenever an avatar is hired, human labor is replaced. “What few players gain can mean fewer opportunities for many others.”
If anything, Bovell said the bar is now higher for models looking to compete with the distinctive and the digitized. She suggested that models use their platforms to build their personal brands, differentiate themselves, and work on new revenue streams like podcasting or brand endorsements.
“Start to take those opportunities to tell your unique human story,” she said. “AI will never have a unique human story.”
That sort of entrepreneurial mindset is becoming table stakes across industries — from journalism to coding — as AI creates the conditions for the most self-directed learners to rise.
Room for another view

Mouginot sees a world where some platforms stop working with human models altogether, though he also believes humans share a desire for the “sensual reality of objects, for a touch of imperfection and for human connection.”
“Many breakthrough models succeed precisely because of a distinctive trait, teeth, gaze, attitude, that is slightly imperfect by strict standards yet utterly charming,” he said. “Such nuances are hard to erode in zeros and ones.”
This is where startup and creative studio Artcare thrives, according to Sandrine Decorde, the firm’s CEO and co-founder. She refers to her team as “AI artisans,” creative people who use tools like Flux from Black Forest Labs to fine-tune AI-generated models that have that touch of unique humanity.
Much of the work Decorde’s firm does today involves producing AI-generated babies and children for brands. Employing minors in the fashion industry has historically been a gray area rife with exploitation and abuse. Ethically, Decorde argues, bringing generative AI to children’s fashion makes sense, particularly when the market demand is so high.
“It’s like sewing; it’s very delicate,” she told TechCrunch, referring to creating AI-generated models. “The more time we spend on our datasets and image refinements, the better and more consistent our models are.”

Part of the work is building out a library of distinctive artifacts. Decorde noted that many AI-generated models — like the ones created by Seraphinne Vallora, the agency behind Vogue’s Guess ad — are too homogenous. Their lips are too perfect and symmetrical. Their jawlines are all the same.
“Imagery needs to make an impact,” Decorde said, noting that many fashion brands like to work exclusively with certain models, a desire that has spilled over into AI-generated models. “A model embodies a fashion brand.”
Pereira added that his firm combats homogeneity in AI “with intention” and warned that as more content gets made by more people who aren’t intentional, all of the output feeds back into computer models, amplifying bias.
“Just like you would cast for a wide range of models, you have to prompt for that,” he said. “You need to train [models] with a wide range of appearances. Because if you don’t, the AI will reflect whatever biases it was trained on.”
An AI future is promised, but uncertain
The usage of AI modeling technology in fashion is mostly still in its experimental phase, Claudia Wagner, founder of modeling booking platform Ubooker, told TechCrunch. She and her team saw the Guess ad and said it was interesting technically, but it wasn’t impactful or new.

“It feels like another example of a brand using AI to be part of the current narrative,” she told TechCrunch. “We’re all in a phase of testing and exploring what AI can add — but the real value will come when it’s used with purpose, not just for visibility.”
Brands are getting visibility from using AI — and the Guess ad is the latest example. Pereira said his firm recently tested a fully AI-generated product video on TikTok that got more than a million views with mostly negative comments.
“But if you look past the comments, you see that there’s a silent majority — almost 20x engagement — that vastly outnumber the criticism,” he continued. “The click-through rate was 30x the number of complaints, and the product saw a steep hike in sales.”
He, like Wagner, doesn’t think AI models are going away anytime soon. If anything, the process of using AI will be integrated into the creative workflow.
“Some brands feel good about using fully artificial models,” Pereira said. “Others prefer starting with real people and licensing their likeness to build synthetic shoots. And some brands simply don’t want to do it — they worry their audiences won’t accept it.”
Wagner said what is becoming evident is that human talent remains central, especially when authenticity and identity are part of a brand’s story. That’s especially true for luxury heritage brands, which are usually slow to adopt new technologies.
Though Decorde noted many high-fashion brands are quietly experimenting with AI, Mouginot said many are still trying to define their AI policies and are avoiding fully AI-generated people at the moment. It’s one reason why Vogue’s inclusion of an AI model was such a shock.
Bovell pondered if the ad was Vogue’s way of testing how the world would react to merging high fashion with AI.
So far the reaction hasn’t been great. It’s unclear if the magazine thinks it ride out the backlash.
“What Vogue does matters,” Odell said. “If Vogue ends up doing editorials with AI models, I think that’s going to make it okay. In the same way the industry was really resistant to Kim Kardashian and then Vogue featured her. Then it was okay.”
Tools & Platforms
Why our business is going AI-in-the-loop instead of human-in-the-loop

True story: I had to threaten Replit AI’s brain that I would report it’s clever but dumb suggestions to the AI police for lying.
I also told ChatGPT image creation department how deeply disappointed I was that it could not, after 24 hrs of iterations, render the same high-quality image twice without changing an item on the image or misspelling. All learnings and part of the journey.
We need to remain flexible and open to new tools and approaches, and simultaneously be laser focused. It’s a contradiction, but once you start down this road, you will understand. Experimentation is a must. But it’s also important to ignore the noise and constant hype and CAPS.
How our business’ tech stack evolves
A few years ago, we started with ChatGPT and a few spreadsheets. Today, our technology arsenal spans fifteen AI platforms, from Claude and Perplexity to specialised tools like RollHQ for project management and Synthesia for AI video materials. Yet the most important lesson we’ve learned isn’t about the technology itself. It’s about the critical space between human judgment and machine capability.
The data tells a compelling story about where business stands today: McKinsey reports that 72 percent of organizations have adopted AI for at least one business function, yet only one percent believe they’ve reached maturity in their implementation. Meanwhile, 90 percent of professionals using AI report working faster, with 80 percent saying it improves their work quality.
This gap between widespread adoption and true excellence defines the challenge facing every service organisation today, including our own.
Our journey began like many others, experimenting with generative AI for document drafting and research. We quickly discovered that quality was low and simply adding tools wasn’t enough. What mattered was creating a framework that put human expertise at the center while leveraging AI’s processing power. This led us to develop what we call our “human creating the loop” approach, an evolution beyond the traditional human-in-the-loop model. It has become more about AI-in-the-loop for us than the other way round.
The distinction matters.
Human-in-the-loop suggests people checking machine outputs. Human creating the loop means professionals actively designing how AI integrates into workflows, setting boundaries, and maintaining creative control. Every client deliverable, every strategic recommendation, every customer interaction flows through experienced consultants who understand context, nuance, and the subtleties that define quality service delivery.
Our evolving tech stack
Our technology portfolio has grown strategically, with each tool selected for specific capabilities.
Each undergoes regular evaluation against key metrics, with fact-checking accuracy being paramount. We’ve found that combining multiple tools for fact checking and verification, especially Perplexity’s cited sources with Claude’s analytical capabilities, dramatically improves reliability.
The professional services landscape particularly demonstrates why human judgment remains irreplaceable. AI can analyse patterns, generate reports, and flag potential issues instantly. But understanding whether a client concern requires immediate attention or strategic patience, whether to propose bold changes or incremental improvements; these decisions require wisdom that comes from experience, not algorithms.
That’s also leaving aside the constant habit of AI generalising, making things up and often blatantly lying.
For organisations beginning their AI journey, start with clear boundaries rather than broad adoption.
Investment in training will be crucial.
Research shows that 70 percent of AI implementation obstacles are people and process-related, not technical. Create internal champions who understand both the technology and your industry’s unique requirements.
Document what works and what doesn’t. Share learnings across teams. Address resistance directly by demonstrating how AI enhances rather than replaces human expertise.
The data supports this approach. Organisations with high AI-maturity report three times higher return on investment than those just beginning. But maturity doesn’t mean maximum automation. It means thoughtful integration that amplifies human capabilities.
Looking ahead, organisations that thrive will be those that view AI as an opportunity to elevate human creativity rather than replace it.
Alexander PR’s AI policy framework
Our approach to AI centres on human-led service delivery, as outlined in our core policy pillars:
- Oversight: Human-Led PR
We use AI selectively to improve efficiency, accuracy, and impact. Every output is reviewed, adjusted, and approved by experienced APR consultants – our approach to AI centres on AI-in-the-loop assurance and adherence to APR’s professional standards.
- Confidentiality
We treat client confidentiality and data security as paramount. No sensitive client information is ever entered into public or third-party AI platforms without explicit permission.
- Transparency
We are upfront with clients and stakeholders about when, how, and why we use AI to support our human-led services. Where appropriate, this includes clearly disclosing the role AI plays in research, content development, and our range of communications outputs.
- Objectivity
We regularly audit AI use to guard against bias and uphold fair, inclusive, and accurate communication. Outputs are verified against trusted sources to ensure factual integrity.
- Compliance
We adhere to all applicable privacy laws, industry ethical standards, and our own company values. Our approach to AI governance is continuously updated as technology and regulation evolve.
- Education
Our team stays up to date on emerging AI tools and risks. An internal working group regularly reviews best practices and ensures responsible and optimal use of evolving technologies.
This framework is a living document that adapts as technology and regulations evolve. The six pillars provide structure while allowing flexibility for innovation. We’ve learned transparency builds trust. Clients appreciate knowing when AI assists in their projects, understanding it means more human time for strategic thinking.
Most importantly, we’ve recognised our policy must balance innovation with responsibility. As new tools emerge and capabilities expand, we evaluate them against our core principle: does this enhance our ability to deliver exceptional service while maintaining the trust our clients place in us?
The answer guides every decision, ensuring our AI adoption serves our mission rather than defining it.
For more on our approach and regular updates on all things AI reputation, head to Alexander PR’s website or subscribe to the AI Rep Brief newsletter.
Tools & Platforms
A Scalable Blueprint for Tech-Enhanced ROI

In the high-stakes arena of general merchandise retail, Walmart has emerged as a trailblazer, leveraging artificial intelligence not just as a buzzword but as a strategic engine for scalable returns. From 2023 to 2025, the company has systematically embedded AI into its DNA, creating a blueprint for how retailers can achieve operational efficiency, cost savings, and customer loyalty in an era of razor-thin margins. For investors, this isn’t just a story of technological innovation—it’s a masterclass in how to turn AI into a profit center.
The AI Arsenal: From “Super Agents” to Digital Twins
Walmart’s AI playbook is as diverse as it is precise. At the heart of its transformation are four “super agents” designed to streamline interactions across the retail value chain:
– Sparky (for shoppers): This AI agent anticipates customer needs by analyzing household behaviors, seasonal trends, and purchase history. It doesn’t just recommend products—it crafts personalized shopping baskets and automates reordering, reducing the “mental load” on consumers.
– Marty (for sellers and suppliers): By consolidating vendor onboarding, inventory coordination, and promotional planning, Marty cuts administrative overhead and accelerates decision-making.
– Associate Agent (for employees): This tool acts as a one-stop shop for store associates, handling payroll, time-off requests, and real-time sales insights. It even learns from user interactions, becoming more intuitive over time.
– Developer Agent (for systems): Accelerating software development by automating routine coding tasks, this agent ensures Walmart’s tech stack evolves at breakneck speed.
But the real magic lies in Walmart’s use of digital twin technology. By creating virtual replicas of its stores, powered by spatial AI, the company can predict and resolve issues like refrigeration failures up to two weeks in advance. This has already slashed emergency alerts by 30% and maintenance costs by 19% in the U.S. Imagine the ripple effect of such proactive problem-solving across 5,500 stores.
Logistics and Delivery: AI’s Invisible Hand
Walmart’s Dynamic Delivery algorithm is another crown jewel. By analyzing traffic, weather, and historical data, it predicts delivery windows with 93% accuracy, enabling same-day delivery to 93% of U.S. households. This isn’t just convenience—it’s a 25% year-over-year boost in digital sales and a 35% surge in Walmart+ memberships. Meanwhile, the Load Planner and Pallet Builder systems optimize trailer loading and route planning, saving $75 million annually in logistics costs.
The financials tell a compelling story. Walmart’s AI-driven advertising platform, Walmart Connect, grew 46% globally in Q2 2025, tapping into the high-margin potential of data-driven marketing. With 27.3 million Walmart+ members, the company is uniquely positioned to monetize customer data without sacrificing privacy—a critical edge in an age where trust is currency.
Why This Matters for Investors
Walmart’s approach to AI is surgical. Unlike companies that dabble in flashy tech, Walmart has focused on solving real-world retail challenges—inventory accuracy, labor efficiency, and customer retention. The results? A 26% year-over-year earnings per share (EPS) growth projection by 2027 and a P/E ratio that’s more attractive than Amazon’s despite stronger e-commerce margins.
The company’s capital allocation is equally impressive. A $520 million investment in Symbotic’s AI-powered robotics and a $19 billion annual capex in the U.S. signal long-term commitment. These aren’t just expenses—they’re investments in infrastructure that will compound value as AI adoption scales.
The Road Ahead: A Retail Renaissance
Walmart’s AI-led transformation isn’t just about today—it’s about redefining the future of retail. The company is already testing agentic AI systems that can autonomously manage complex tasks, from dynamic pricing to in-store navigation. With a proprietary large language model (Wallaby) trained on decades of retail data, Walmart’s predictive capabilities are unmatched.
For investors, the key takeaway is clear: Walmart is not just keeping up with the AI revolution—it’s leading it. While competitors like Amazon and Target are still figuring out how to integrate AI into their operations, Walmart is already reaping the rewards of a disciplined, data-driven strategy.
Final Call to Action
The numbers don’t lie. Walmart’s AI initiatives have delivered $75 million in annual savings, 46% growth in high-margin advertising, and a 1.2–1.5 percentage point boost in operating margins by 2027. For those seeking exposure to the next phase of retail innovation, Walmart offers a rare combination of scale, execution, and profitability.
In a sector where margins are under constant pressure, Walmart’s AI-driven efficiency is a moat worth betting on. This isn’t just a stock—it’s a glimpse into the future of retail, where technology isn’t just a cost center but a catalyst for exponential returns.
Bottom line: Buy Walmart. The AI revolution is here, and Walmart is the blueprint.
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