When people talk about AI in creative industries, the conversation usually goes in one of two directions.
Either AI is treated like a miracle button that will make every photo, video, ad, and campaign instantly cheaper and faster. Or it is treated like a threat that will replace photographers, designers, editors, creative directors, and eventually every human involved in the creative process.
After speaking with Sofiia Shvets, former CEO of Claid AI, I think both views are too simplistic.
AI will absolutely change creative work. It will automate repetitive tasks. It will reduce the cost of experimentation. It will help brands produce more content, faster. But it will not remove the need for taste, direction, storytelling, judgment, and brand vision.
In fact, the more AI tools become available, the more important those human skills become.
As Sofiia said during our conversation on AI Inside San Francisco, there is a lot of speculation that AI will take creative jobs and leave us with “just AI and no creators.” She disagrees with that. Her view is clear: creators will be the winners of this revolution.
I agree.
But not every creator will win equally. The people who only know how to execute repetitive tasks will feel pressure. The people who understand visual language, brand positioning, storytelling, audience behavior, and workflow design will become much more powerful.
That is the real shift.
The Hidden Problem Behind Product Content
Before meeting Sofiia in person, I had already used Claid AI without knowing the founder behind it.
A friend asked me for help with product photography. She needed clean product visuals, background removal, and new image variations, but I did not have time to do a full production workflow. So I started testing AI product photography tools and ended up using Claid AI.
A few weeks later, I was at an event in San Francisco, sitting next to Sofiia. We started talking, and she explained that she was building a company focused on AI product photos and videos for e-commerce brands. I realized I had used her product before meeting her.
That is one of the things I love about San Francisco. You go to an event, start a random conversation, and suddenly you are talking to the person behind a tool you already used.
Claid AI works on a very real business problem: brands need more product content than ever, but traditional content production is slow, expensive, and difficult to scale.
For e-commerce, a product image is not just a nice asset. It directly affects trust, perception, and conversion. A product photo needs to show quality. It needs to show details. It needs to explain how the product is used. It needs to communicate the lifestyle, the audience, and the context around the product.
As Sofiia put it, consumers buy with their eyes.
That sounds simple, but producing those visuals is not simple.
A traditional product shoot requires a studio, lighting, a camera, lenses, backgrounds, props, models, styling, retouching, and post-production. If the product is reflective, transparent, textured, or needs to be shown on a person, the complexity increases fast.
I have done product photography before, and people who have never been behind the camera often underestimate how much work goes into a “simple” product image.
Even something like a glass can become complicated because of reflections, lighting, transparency, and cleanup. Clothing adds another layer. Fashion often requires models, different body types, different ethnicities, styling, fit, and retouching. Kids’ clothing can be even harder because hiring child models introduces logistical and legal constraints.
AI does not make all of that disappear, but it creates a new way to expand from a good reference image into many possible campaign directions.
Garbage In, Garbage Out Still Applies
One of the most important points Sofiia made is that AI does not remove the need for good input.
Her recommendation is practical: invest once in strong product reference photos. Get clean, high-quality images of your product with good lighting. Then use AI to expand those images into different themes, environments, formats, and campaign ideas.
This is where a lot of brands misunderstand AI.
They think AI can magically transform weak assets into premium campaign visuals. Sometimes it can help, but the old machine learning rule still applies: garbage in, garbage out.
If the product photo is bad, the model has less reliable information to work with. If the lighting is poor, if the product is unclear, if the shape is distorted, the output will suffer.
That is why AI is not replacing the production mindset. It is changing where the production investment happens.
Instead of spending a massive budget on every campaign variation, brands can invest in strong source assets and then use AI to test different creative directions at scale.
That is a huge advantage.
You can test a Halloween theme, a luxury editorial theme, a lifestyle shot, a fashion marketplace image, a product-in-hand scene, or a video variation before committing to a full production campaign.
This changes the economics of creative testing.
Why Brands Need So Much More Content Now
One of the biggest reasons AI content tools are exploding is not just because they are impressive. It is because the demand for content has become unreasonable.
Sofiia mentioned that general TikTok recommendations can push brands toward three to five new creatives per day.
That means around 90 to 150 creative assets per month.
Most of them will fail.
That is not a bad thing. That is how creative testing works. You produce variations, test hooks, angles, formats, product contexts, and messages, then look for the winning cohort.
The problem is that traditional production was not built for this speed.
Five years ago, many brands could survive with fewer assets and slower refresh cycles. Today, social platforms, short-form video, paid media, and e-commerce marketplaces reward constant testing and constant creative freshness.
Meta and TikTok increasingly want more creative inputs so their algorithms can test and find audiences. Targeting is becoming less manual, and the creative itself becomes one of the main levers.
That means brands need to give the platform more options.
Not one hero image.
Not one campaign video.
Dozens or hundreds of variations.
For small brands, this is a budget problem. For marketplaces, it is a scale problem. For creative teams, it is a workflow problem.
This is where AI becomes useful, not as a replacement for creative strategy, but as a way to multiply execution after the strategy is clear.
AI Is Becoming a Workflow, Not Just a Tool
One of the most interesting parts of the conversation was the move from single-output AI tools to multi-step workflows.
A basic AI workflow might remove a background or generate a new product scene.
A more advanced workflow can analyze a product image, understand that it is a white T-shirt, read a brief requesting a male model between certain ages, generate a prompt, create images, then turn those images into videos.
As Sofiia explained, multi-step workflows that used to require many manual steps can now run in 20 or 30 seconds, automatically. A brand could upload files, walk away, and have generated assets ready the next morning.
That is where AI starts to become operationally powerful.
It is not just about making one cool image.
It is about building a content pipeline.
For companies with thousands of products, this matters a lot. You cannot manually generate product visuals one by one forever. You need APIs, batch processing, agents, workflow logic, review steps, and quality control.
This is also where human judgment becomes more important, not less.
If you automate a bad creative direction, you just generate bad content faster.
The human role shifts toward setting the vision, defining the brand rules, reviewing outputs, deciding what is good enough, and knowing where automation should stop.
The Creative Jobs Question
Every conversation about AI and creativity eventually arrives at the same question:
Will AI replace creative jobs?
Sofiia’s answer was balanced. Some parts of creative work will be automated. Simple editing tasks, background removal, object cleanup, color correction, and repetitive production work are already being automated because they follow patterns.
But she does not believe this means creators disappear.
I agree with that, but I think the distinction is important.
AI will replace tasks before it replaces roles.
If someone’s entire value is based on repetitive execution, that person is exposed. But if someone understands taste, visual direction, brand strategy, storytelling, audience psychology, and how to use tools to produce a specific outcome, AI becomes leverage.
Sofiia gave a great example from her own team. Her designer produces the best AI-generated creatives because she has a visual eye. She knows what to ask for. She knows when the output is good. She knows how to guide the system.
That is the part many people miss.
The tool does not replace taste.
The tool amplifies taste.
If you are building a brand, AI cannot decide whether the brand should feel extravagant, minimal, premium, playful, raw, futuristic, or low key. It can generate options, but the direction still needs to come from someone.
Without human vision, AI will create randomness.
With human vision, AI becomes production power.
Not Everything Should Be Automated
A very important part of the conversation was the human loop.
Once automation becomes easy, the temptation is to automate everything. But not everything should be automated.
Some tasks are too nuanced. Sofiia mentioned wrinkles in clothing. A wrinkle depends on fabric, lighting, background, fit, and the creative intention of the image. You could try to build an AI system to handle every wrinkle variation, but sometimes a skilled retoucher can fix it in three minutes with better judgment.
That example matters because it shows a bigger principle.
The question is not “Can AI do this?”
The better question is “Should AI do this, or is human judgment still faster, better, or more appropriate?”
In production, the best workflows will not be fully manual or fully automated. They will be hybrid.
AI handles the repetitive and scalable parts. Humans handle judgment, direction, authenticity, and final decisions.
That is where strong creative teams will win.
The Return of Raw and Authentic Content
Another idea I found very interesting was the possibility of two content trends happening at the same time.
On one side, we will have extremely polished AI-generated content. Sharp, fast, scalable, cinematic, and personalized.
On the other side, we may see a stronger return to raw, imperfect, human-made content.
Film cameras are already cool again. Imperfect photos feel more real. Unpolished videos can feel more trustworthy. “No AI used” may become a creative signal in some contexts.
That makes sense.
When everything becomes easy to generate, authenticity becomes more valuable.
The future of content may not be only AI-generated perfection. It may be a split between highly generated content and deliberately human, raw, imperfect media.
Both will have value.
The key is knowing which one fits the message.
Democratizing Content Creation
One of the most powerful ideas in this episode was democratization.
For Sofiia, democratizing content creation means more people can become creators. People are limited more by ideas than by access to expensive equipment, studios, or technical skills.
That connects deeply with how I see AI in video and content production.
Many people have strong ideas in their minds but never had the tools, money, or technical background to express them visually. They did not have a camera. They did not have a studio. They did not have editing skills. They did not have a production team.
AI changes that.
It gives more people the ability to express a creative vision.
That does not mean every output will be good. It means more people can enter the creative process.
This is similar to what happened with digital cameras and smartphones. More people could take photos, but not everyone became a photographer. Access increased, but taste still mattered.
AI will do something similar for visual storytelling.
It will make creation more accessible. But the best work will still come from people with strong ideas, good judgment, and a clear point of view.
The Trust Problem: Deepfakes, Watermarking, and Compensation
Of course, generative AI also creates serious problems.
Sofiia talked about trust, deepfakes, transparency, and artist compensation. Funny AI memes are one thing. A generated video of a real person saying something they never said is another.
As AI content becomes more realistic, platforms will need better ways to label generated or modified content. Invisible watermarking, disclosure systems, and content provenance will become more important.
There is also the issue of artists’ work being used to train or influence models. Sofiia believes that creators should be compensated when their work contributes to model outputs, potentially through licensing models similar to how music royalties work.
This is complicated technically, but the principle is important.
If AI is going to reshape creative industries, trust and fairness cannot be treated as side topics.
They are part of the foundation.
Advice for Beginners
For people who are new to AI image tools, Sofiia recommends starting simple.
Use text-to-image tools. Try Midjourney. Look at community examples. Study prompts. Click on images you like, see how prompts are structured, reuse them, change a few words, and observe how the model reacts.
This is one of the best ways to learn.
AI prompting is not just writing a sentence. It is learning how a model responds to structure, detail, style, references, and constraints.
You learn by testing.
For more advanced users, Sofiia recommends testing different models and thinking in workflows. There is no universal model that is best for everything. Some models are better for product images. Some are better for art. Some are better for video. Some are better for control.
Tools like Krea, Freepik, and workflow-based systems help users compare models and build more complex generation pipelines. For highly technical users, ComfyUI gives deeper control, but it also requires more technical knowledge and compute.
Her advice is practical: do not jump into the most technical setup before mastering the tools already available online.
That is good advice.
Most people do not need the most complex workflow first. They need to understand the creative process, the models, the limitations, and the outputs.
My Biggest Takeaway
The biggest takeaway from this conversation is that AI is not removing creative work.
It is changing what creative work means.
The repetitive parts will become faster and cheaper. The strategic parts will become more important. The gap between people who just execute and people who can direct, judge, and tell stories will become more visible.
For brands, AI means more experimentation.
For creators, AI means more leverage.
For agencies, AI means workflows need to be redesigned.
For consumers, AI means more content, more personalization, and more need for trust.
And for everyone in the creative industry, the best advice may be the one Sofiia gave at the end:
Read less hype. Test more for yourself.
That is probably the most useful mindset right now.
Not panic.
Not blind excitement.
Curiosity, experimentation, and taste.
That is where the opportunity is.
Watch the Full Episode
You can watch the full conversation with Sofiia Shvets, CEO of Claid AI, on AI Inside San Francisco.
In the episode, we discuss AI product photography, AI video workflows, e-commerce content at scale, creative jobs, deepfakes, human judgment, and the future of content creation.
