Major AI companies are turning to big-budget advertising to attract mainstream users, but many are struggling to create distinctive brands that stand out from the competition.
Rahulsing Rajput, a Marketing Analytics Professional and advertising enthusiast who works at a large financial institution, explains why smaller players are finding success with creative distribution partnerships rather than trying to match the ad spend of giants like OpenAI and Google.
As the rise of AI-generated ads is rewriting the rules of production, the AI industry could soon behave like any other consumer brand, using its own data to refine marketing.
Now that tech-savvy early adopters are largely captured, major AI companies are turning their attention to the general public. Using traditional tactics like out-of-home advertising to reach them, the fight to be number one is turning into a fierce competition. Their success now depends on mastering classic marketing disciplines like building brand recall, securing clever distribution, and stretching creative budgets.
For an expert's take, we spoke with Rahulsing Rajput, a Senior Lead at a large tier 1 financial institution. With a background that includes systems engineering at Tata Consultancy Services and advanced studies at MICA and JBIMS, Rajput has deep insight into how technology meets the mainstream. In his view, the challenge now is turning that functional storytelling into a memorable brand.
"Stories are great connectors because many normal people won't understand what GPUs or algorithms you are using. But, they'll understand through stories," Rajput says. Speaking in a personal capacity, he notes that while AI companies are finally spending big on advertising, many are failing a fundamental marketing test. For these companies, the pivot to the mainstream means learning to speak a more human language.
Musical chairs: Right now, advertising for AI companies is a sea of generic sameness, Rajput explains. While that raises category awareness, it does little to build a competitive moat for the company footing the bill. "It's a race for all AI companies to grab market share and drive adoption, because in the end, most users will only use two to three AI tools. All the AI companies have recognized they need to show these new users what AI can do for them, and that's why they have started spending money on traditional advertising."
Brand blind test: However, according to System1 analysis, OpenAI’s recent ads scored low on fluency, Rajput points out. For him, it's a measure of how quickly an audience can identify the brand. "You could have easily put a logo for Claude or Gemini at the end of that OpenAI ad, and it would have still made sense because you are showing a generic function. You search for a recipe and you get a recipe, so it could be an ad for OpenAI, Gemini, or anyone else," he remarks.
The solution is building distinctiveness, Rajput says. Brands need immediate recall so viewers know instantly if it's an OpenAI ad or a Gemini ad. But that's only possible with consistent communication. Now, the economics of ad production are being fundamentally rewritten, he continues. It's creating a self-sustaining feedback loop in which AI-generated ads are then used to sell AI. Here, Rajput acknowledges that tools like Sora are improving at an "exponential pace," even though the technology is still in a "nascent stage."
Dimes on the dollar: That improvement, from the "six fingers" era of image generation to near-photorealism, is forcing a new calculation for advertisers. "The cost difference is huge. If you want to shoot an ad traditionally, it can be 10x to 100x more than what you will create using AI. But there's a trade-off in terms of quality control that traditional advertising offers. Advertisers have to pick and choose how much output quality they want for their resources, and based on that, decide when to use AI versus a traditional shoot," he says.
Distribution over dollars: These massive ad budgets and AI-driven efficiencies can give incumbents a competitive edge. "OpenAI and Gemini have been forever integrated with Google phones and Android phones. Perplexity was a comparatively late entrant, but they took a different route, partnering with India's second-biggest telecom company, Airtel, and giving one year of free services of their premium offering. Because of that, they became the fastest-growing AI in India with almost 600% adoption growth."
Go where they aren't: To compete, many smaller startups are taking his advice by finding alternative paths that don't require matching budgets. "Rather than going all out in advertising, smaller AI companies can tie up with influencers if they have specific use cases. For a hypothetical fashion AI, that could mean targeting influencers by demonstrating an algorithm specifically upgraded to suit Indian skin tones," he envisions.
Ultimately, this marks a clear maturation point for the industry. The strategies being deployed today are set to determine tomorrow's winners, Rajput concludes. But the next 12 months will reveal which leaders have learned their lessons. As companies face pricing games like OpenAI's economical 'ChatGPT Go' plans in India, the real test will begin. "We're all anticipating where this space will be a year from now, when most of the premium offers will have ended. They'll have more data, like any other traditional FMCG advertiser. They will use that data to understand what happened with their ads, learn from it, and refine their communication to improve throughput. That's what I'm really interested to see."
