On August 7, OpenAI debuted GPT-5 with the fanfare of a pharmaceutical company launching a drug that works in real patients exactly the way it did in mice. CEO Sam Altman stood on stage and declared it would make us feel “useless relative to the AI,” which may not have been what anyone wanted to hear while sipping their morning coffee and contemplating whether they’d be rendered obsolete.

Here’s what OpenAI offers for pharma marketers: A unified AI model that combines the quick wit of a copywriter with the methodical reasoning of a regulatory affairs ace, and is able to score 94.6% on math olympiad problems and 46.2% on health benchmarks. Never mind, of course, that pharmaceutical marketers need it to score 100% on “doesn’t claim our drug cures cancer when it only treats mild eczema.”

But here’s the non-flashy reality of GPT-5: It’s both more and less revolutionary than advertised, and that paradox is what makes it fascinating for life sciences marketing. It’s as if your creative agency, your MLR team and that one intern who excels at Excel inhabited the same brain, and that brain had a really good router that decided who should answer each question.

Using it is akin to hiring a brilliant but unreliable consultant who works 24/7, never asks for a raise and occasionally suggests that your cardiovascular drug might also cure the summertime blues. Is this valuable? Absolutely. Is it trustworthy? Only with attentive human supervision.

Early adopters in and around pharma are bullish on its potential. “GPT-5 feels like having a teammate who can generate 50 campaign ideas, translate clinical data into patient-friendly language and still laugh with you about filling out the same five forms just to confirm your insurance,” says Geetha Parachuru, director, digital solutions at Gilead. “It’s not replacing us—it’s amplifying our ability to move fast, stay empathetic, and build trust in a system that’s anything but simple.”

The technical specs are impressive. There’s a 400,000-token context window (that’s around 300,000 words, or just a bit more than the average Complete Response Letter from the Food and Drug Administration). It comes in three model sizes: GPT-5, GPT-5 mini and GPT-5 nano. GPT-5 also promises a 45% to 80% reduction in hallucinations compared to GPT-4—which, of course, still falls far short of the complete elimination of hallucinations that pharma demands.

The real breakthrough isn’t the raw intelligence, but rather the routing system. GPT-5 automatically decides whether a query needs quick, surface-level processing or deep, contemplative reasoning. It’s like having an AI that knows when to share a quick tagline versus when to write a definitive disease-state-education piece.

Here, in fact, is where things get interesting for content creators. OpenAI explicitly trained GPT-5 to be less agreeable. In targeted tests, sycophantic responses dropped from 14.5% to under 6%.

This is simultaneously the best and worst news for pharmaceutical marketers. On one hand, their AI won’t announce that a minimally compliant claim about transforming patient lives is “so inspiring!!!” when it’s actually generic marketing fluff. On the other, that same AI might tell them that their carefully crafted patient journey narrative sounds like it was written by someone who’s never met an actual patient. The robot has opinions now, and they’re not always flattering.

So what does GPT-5 mean for pharmaceutical content generation? It’s genuinely good at generating first drafts of educational content that don’t sound as if they were written by a medical textbook that gained sentience. It can create multiple variations of messaging for A/B testing (think 50 versions of a headline without repeating “breakthrough” multiple times) and translate complex mechanism-of-action descriptions into patient-friendly language (without dumbing it down to “medicine makes body happy”). It can analyze competitor messaging and identify white space opportunities.

There are still plenty of blind spots, however. Under no circumstances can GPT-5 be trusted to navigate the complex world of MLR without human supervision. It still can’t quite grasp why claims about “superior efficacy” will ultimately result in the receipt of a Warning Letter, nor can it generate content that passes the would-an-actual-doctor-say-this? Test.

Then there’s the aforementioned issues around hallucinations. GPT-5 may be significantly less likely to hallucinate than previous models, with responses that are “~45% less likely to contain a factual error than GPT-4o” when web search is enabled. This seems impressive until you realize that, in pharma marketing, a 55% improvement in accuracy still means you’re likely to find yourself in FDA’s crosshairs.

As a result, some users are likely to find themselves frustrated by the reality that GPT-5 is simultaneously the most accurate AI model ever created and nowhere near accurate enough for unsupervised content generation. It’s like having a brilliant colleague who gets things right 95% of the time, but the wrongness of that last 5% could cost you your job and your company its  reputation (and possibly a few billion dollars in lawsuits).

About that potential frustration, Monique Landrum, omnichannel and digital marketing lead in the U.S. immunology patient value unit at UCB, believes the overall adoption curve will dictate pharma’s path forward. “As users become more accustomed to receiving answers rather than sources, how should we adapt to this new behavior?,” she asks. “We’re already seeing platforms experiment with selling ad space alongside AI-generated results. Could this represent the next wave of pharma ad spend?”

Then there’s the alchemizing effect GPT-5 could have on the client/agency relationship. It’s not a leap to suggest that it could replace 30% of what agencies currently do.

All those hours spent on initial concepting and copywriting, as well as preliminary competitive analyses? That’s GPT-5 turf now. The model can generate 20 campaign concepts in the time it takes to send a Slack about scheduling a brainstorming meeting.

But what the AI evangelists miss is that pharmaceutical marketing isn’t really about content generation. It’s about navigating a regulatory minefield while trying to tell a compelling story about a molecule that took 15 years and $2 billion to develop. It’s about understanding that your HCP audience has seen every marketing trick in the book and your patient audience has likely been burned before by marketing promises.

GPT-5 can write beautiful prose about your drug’s mechanism of action. It cannot sit in a room with a patient who’s been failed by three previous treatments and understand what message will actually connect. Not yet, anyway.

Your Pragmatic Implementation Playbook

So if you’re a pharmaceutical marketer wondering how to use GPT-5 without ending up in a compliance nightmare, your playbook begins with a list of dos and don’ts. You should consider it for initial ideation sprints (ask GPT-5 to generate 100 concepts, then throw out the 95 lesser ones and polish the remaining five the old-fashioned way; plain language summaries (to transform clinical trial results into patient-comprehensible content, subject to medical review; personalization at scale (create tailored content for different physician specialties without starting from scratch each time); and competitive intelligence synthesis (analyze patterns across competitor materials to identify what everyone’s doing that nobody should be…and vice versa).

You should stay far away from GPT-5 when creating final copy that won’t be reviewed later by humans (this should be obvious); anything involving specific clinical claims (the AI doesn’t understand the difference between statistical and clinical significance); patient testimonials or case studies (it will create captivating fiction that sounds disturbingly real); and regulatory response documents (unless you covet the opportunity to explain to the FDA why your response was written by a robot).

Indeed, the real story of GPT-5 isn’t that it’s revolutionary or that it’s overhyped. It’s that it can be both, depending on how you use it, just like the models that came before it.

GPT-5 is a tool that makes good marketers better and mediocre marketers slightly less mediocre. It can generate infinite variations of content, but it can’t tell you which variation is compliant and which will actually move the needle on script lift.

As Box CEO Aaron Levie noted, while previous models failed at complex reasoning within long documents, GPT-5 represents a “complete breakthrough…The model is able to retain way more of the information that it’s looking at, and then use a much higher level of reasoning and logic capabilities to be able to make decisions.” For pharmaceutical marketers drowning in clinical trial data and medical literature, this could prove game-changing.

But here’s the issue: The pharmaceutical industry has historically been a few years behind other industries in adopting new technology, and there’s a good reason for that. Companies aren’t selling software or sneakers; they’re selling molecules that alter human biology. The stakes are different, and no amount of transformer architecture improvements can change that fundamental reality.

The marketers who win in the GPT-5 era won’t be the ones who use it most, but the ones who use it most intelligently. They’ll view it as a tool for augmentation, not automation. They’ll recognize that, in an industry where a single wrong word can trigger a Warning Letter, having an AI that’s 95% accurate is impressive and terrifying in equal parts.

Welcome to the future of pharmaceutical marketing… which looks like the present, but faster and with better first drafts. And honestly, that might be exactly what pharma marketers need.

[Disclaimer: Ahmed Elsayyad has an investment stake in OpenAI.]

Has your marketing team experimented with GPT-5? Drop us a note at [email protected], join the conversation on X (@KinaraBio) and subscribe on the website to receive Kinara content.