Foundation Models for Advertising: Why They Matter for Brands

In every wave of technology, advertising is both the first mover and the latecomer. The first because brands are always willing to try something new if it gives them attention. The latecomer because the industry tends to mistake novelty for infrastructure. We saw this with the early internet. Banner ads and “hits” passed for strategy. We saw it with social: “likes” were confused with sales. Now we are seeing it again with AI. Agencies produce clever one-off campaigns with generative tools, but few are asking the deeper question: what does it mean to build foundation models for advertising?

A foundation model is, in essence, a general-purpose system trained on vast amounts of data that can then be fine-tuned for specific tasks. OpenAI’s GPT-4, Anthropic’s Claude, or Google’s Gemini are all foundation models for text. They can be adapted to write code, summarize legal contracts, or mimic Shakespeare because they’ve been trained on such a broad base of language. In the same way, image and video foundation models learn not just pixels but patterns: how a chair looks from every angle, how a human face changes when it smiles, how a bottle of shampoo glistens under studio lights.

For advertising, the potential is obvious. The industry spends billions of dollars a year creating short-form video and image content to show products in ways that resonate with consumers. A traditional campaign might take weeks of storyboarding, casting, shooting, editing, and localization. A foundation model for advertising can collapse much of that into hours. The catch is that the output depends entirely on the input. If the model has been trained on scraped internet content.  Much of it Western, generic, or unlicensed, it will produce results that are misaligned with the brand’s values, inconsistent across executions, and often legally risky.

This is why purpose-built foundation models for advertising matter. Instead of training on whatever content is available, they are trained on datasets that reflect specific brand categories, consumer behaviors, and cultural nuances. They learn not just what “a dress” looks like but what a North Indian bridal saree looks like. Not just “a dinner” but a Malay family gathering during Hari Raya. They also learn the grammar of advertising itself, the camera angles, the lighting, the pacing, so the outputs look like ads, not generic videos.

The next step is pre-training and constraint. Pre-training means the model has already absorbed the category knowledge before a brand ever touches it. A shampoo brand doesn’t need to teach the model what hair looks like in motion; the model already knows. Constraints mean the model is limited to a brand’s specific guidelines: its colour palette, its approved product shots, its licensed music. This combination, pre-trained and constrained, gives brands three advantages.

First, speed. If the model already knows what shampoo, skin tone, or fabric drape should look like, generating variations takes hours, not days. Campaigns that once needed a month of production can be iterated in real time.

Second, consistency. Traditional creative work often suffers from drift: different agencies, markets, and freelancers interpret the brand in slightly different ways. A constrained model acts like a guardrail, ensuring that every execution adheres to brand standards. The blue is the right blue. The logo is always in the right place.

Third, cost and scale. Advertising used to be about four big campaigns a year. Now brands need hundreds of assets across social, e-commerce, and retail channels. Producing that volume by hand is unsustainable. A pre-trained, constrained model can generate 200 ad variations with the same accuracy as 20. The economics shift from linear to exponential.

There is a parallel here to the history of stock photography. In the 1980s, every ad required custom shoots. Then Getty and Shutterstock built structured libraries of images that agencies could license quickly. That infrastructure lowered costs and accelerated campaigns. But it also created sameness: everyone had access to the same stock. Foundation models for advertising are a new kind of infrastructure, but instead of static images, they generate dynamic, tailored video at scale. And when they are trained on unique, licensed datasets, they avoid the trap of homogenization.

For brands, this is not just about cheaper ads. It’s about accuracy and trust. Consumers notice when a model looks unrealistic, when a cultural detail feels off, when an image seems borrowed rather than authentic. In times when brand equity is fragile and social media outrage is a constant risk, the cost of getting it wrong is higher than the cost of doing it right. Foundation models built for advertising are, at their core, tools for risk reduction as much as for efficiency.

The ad industry, as usual, has a choice. It can keep playing with wrappers and prompt hacks, producing clever stunts that win awards but do not scale. Or it can invest in the foundations: models trained on the right data, constrained by brand guidelines, and designed for the realities of modern consumer engagement. The former is noise. The latter is the infrastructure on which the next decade of advertising will run.

Eventually, the difference will be stark. Brands that rely on generic tools will sound and look the same as everyone else. Brands that build or partner on foundation models for advertising will own not just their creative but the underlying system that generates it.

In a world where every channel demands content, that control is not optional. It’s the moat.