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The launch of GPT-5 was supposed to be the moment generative AI went from impressive to indispensable. Instead, it has been a week of glitches, conflicting opinions, and hurried damage control. OpenAI’s most ambitious model yet was billed as more creative, contextually aware, and capable than anything that had come before. What users got was a mix of brilliance and bewilderment.
In the first 48 hours, social media lit up with screenshots of GPT-5 producing nuanced ad copy, pitch decks that looked like they had been through a seasoned strategist, and long-form content that read like it had been ghostwritten by a team of award-winning editors. Those posts were followed, often in the same threads, by examples of the model hallucinating numbers, inventing references, or abruptly changing tone halfway through a project. One creative director described it as “having the best copywriter in the world who occasionally decides to write surrealist poetry in the middle of a client brief.”
Industry reactions split almost instantly. Developers and AI optimists praised GPT-5’s leap in coherence over long stretches of text, improved ability to maintain context across sessions, and its multimodal processing that allows marketers to feed it everything from product photos to moodboards for instant campaign drafts.
Others were less charitable, noting that the instability in early outputs made it a risky replacement for tried-and-tested systems. Several agency leaders expressed concerns over the reliability of factual claims, especially in compliance-heavy sectors like finance and healthcare.
The announcement
Jacob Joseph, VP – Data Science at CleverTap, says GPT-5 is a measured but meaningful leap for marketers, not just more powerful but more grounded. He points out that for the first time, there is a model that can adapt to the complexity of real marketing workflows, from summarising long-form assets to reasoning across customer data and interpreting campaign videos and performance.
He further said that with up to six times fewer factual errors compared to previous versions, marketers can trust GPT-5 to understand goals, analyse context, and execute with nuance, whether they are building personalised journeys, testing narratives, or scaling creative production.
The pressure mounted quickly enough for OpenAI CEO Sam Altman to intervene publicly. In a series of posts and interviews, he acknowledged the uneven rollout and promised to restore access to GPT-4o, the model many agencies and creators still rely on for consistent results. “We hear you,” he told users, “and we will ensure you can use GPT-4 alongside GPT-5 until stability is where it needs to be.” The reassurance was not just a customer relations exercise. For marketers with ongoing campaigns built on GPT-4 outputs, it was a lifeline.
With the immediate fire-fighting underway, attention has shifted to what GPT-5 can actually do when it works as intended. The early verdict is that its creative range is broader, its capacity for brand-specific tone mimicry is more precise, and its personalisation capabilities are far more granular.
Marketers can now feed it detailed audience segments and see ad copy emerge that reflects not just demographic data but inferred preferences, past engagement patterns, and even mood cues from prior interactions.
Another area where GPT-5 shows promise is in producing long-form thought leadership. For brands investing in content marketing, the model’s ability to maintain narrative threads and logical flow across several thousand words means less time spent stitching together multiple drafts.
The multimodal abilities also change the speed at which campaigns can be developed. A creative team can upload a product image, a rough value proposition, and a competitor ad, then ask GPT-5 to produce a set of differentiated positioning statements. In early trials, this process compressed what was previously a two-day brainstorming sprint into a two-hour exercise. The danger, as some strategists point out, is that faster does not automatically mean better, and without rigorous brand checks there is a risk of homogenised or off-brand messaging.
The mixed reception has not slowed curiosity. Agencies are already restructuring workflows to blend GPT-5 into ideation while keeping GPT-4 as the production workhorse. This dual-model strategy allows teams to exploit GPT-5’s creative edge while relying on GPT-4’s steadiness for client-facing material. Some marketers are even building internal “AI switchboards” to route tasks automatically to the model best suited for them based on complexity and risk profile.
The marketing industry has seen enough AI hype cycles to know that adoption is rarely linear. GPT-5’s arrival is a reminder that innovation and reliability do not always debut together. The capability gains are real, but so are the operational headaches. As stability improves, the lure of richer personalisation, faster creative development, and deeper brand mimicry will be hard to ignore. For now, the smartest approach appears to be one of guarded experimentation, pairing GPT-5’s ambition with the proven dependability of GPT-4o.
The launch drama may fade, but its lesson will linger. The future of AI in marketing is not just about bigger models or flashier capabilities. It is about trust—both in the technology and in the brands using it. GPT-5 has opened the next chapter. Whether it becomes the standard or just another step along the way will depend as much on how marketers adapt as on how quickly OpenAI can deliver on Altman’s promise.
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