The Great AI Wrapper Charade in Biotech

In boardrooms and biotech conferences alike, one phrase has become inescapable: “our AI-powered platform.” Startups in drug discovery, clinical decision support, and diagnostics proudly tout proprietary artificial intelligence as their secret sauce. Yet peel back the glossy pitch decks, and many of these supposed AI pioneers are simply piggybacking on someone else’s algorithms.

Their “cutting-edge AI” often amounts to nothing more than a well-dressed wrapper around a generic GPT-like service. It is a bit like claiming to be a master chef after merely plating take-out food. Everyone, it seems, insists they have cooked up an AI revolution – but too often the real work was done in another kitchen.

A Surge of Generative Hype and Me-Too Startups

The frenzy around artificial intelligence, especially generative AI, has reached fever pitch. Investment has poured in at staggering rates, and biotech has been no exception. In the first half of 2025 alone, global venture funding for generative AI startups hit roughly $50 billion – already more than the entire previous year. Healthcare and life sciences startups have jumped on this bandwagon en masse.

A recent analysis mapped 87 health care and biotech companies offering generative AI products across some 20 different application areas. These range from AI “scribes” that automate medical paperwork to drug discovery platforms promising to conjure molecules by algorithm.

Nowhere is the hype more evident than in drug discovery. Last year four of the five largest generative-AI funding deals in life sciences went to drug discovery startups, including ventures claiming AI-driven molecule design. Clinical decision support and diagnostic AI are hot on their heels; nearly half of health-system AI funding has gone toward imaging, decision support, and diagnostic tools in recent years.

The surge reflects high hopes that AI can streamline R&D, assist doctors, and improve outcomes. It also reflects a herd mentality: no biotech startup today wants to be caught without an “AI” story to tell.

Yet behind this gold rush lies a gimmick. With generative AI tools now readily accessible via API, an alarming number of new startups are effectively reselling someone else’s technology. As one commentator dryly quipped in a Q&A about these companies’ competitive edge: “there’s this API call, right, and we make it for you.” His verdict on such a business model? “That’s not a moat. That’s not even a shallow puddle.” 

In other words, wrapping a public AI model and calling it your own creates no defensible advantage. It’s artificial intelligence in name, but outsourced intelligence in practice. And like any fad, this one has produced a glut of indistinguishable offerings. The result is a proliferation of me-too startups, each layering a pretty interface and marketing spin over the same few underlying AI models.

The GPT in a Lab Coat: Wrappers Unmasked

At the core of many “AI-powered” startups is not a breakthrough algorithm tuned to biomedical complexity, but a foundational dependency on someone else’s AI. They take a powerful general model – say OpenAI’s GPT-4 or Meta’s Llama 2 – and simply fine-tune prompts or add a thin veneer of domain flavor.

The heavy lifting (natural language understanding, image recognition, molecular generation) is being done by an AI developed outside the company. The startup’s contribution is often little more than glue code and a user interface.

This phenomenon spans across biotech subfields:

Drug discovery startups frequently rely on public models or open science. Some quietly use DeepMind’s AlphaFold for protein folding insights, or repurpose generative models from academia to design molecules – contributions available to any researcher with an internet connection. Others apply GPT-based text analysis to scour research literature for drug targets, essentially doing with ChatGPT what a diligent grad student might do with PubMed.

If every company has equal access to the same pretrained models and databases, calling the output “our proprietary AI” rings hollow. Little wonder skeptics note that lofty terms like “foundation model” often serve as “a catch-all phrase to signal ‘we did something similar to ChatGPT’. In many cases, the only “innovation” is preferring not to invest in original R&D.

Clinical decision support tools – apps that help doctors diagnose or plan treatments – have rapidly emerged by piggybacking on large language models. After GPT-4 famously passed medical exams, startups rushed out virtual health assistants that sound like a doctor. Under the hood, however, a number of these are just calling the same general AI everyone else uses. For example, one much-hyped medical documentation AI scribe achieved impressive accuracy in transcribing and coding patient visits by using OpenAI’s fine-tuned language model as its engine.

The startup’s “technical breakthrough” was essentially leveraging OpenAI’s latest upgrade, then layering some specialty-specific tweaks. Indeed, big health systems are partnering with third-party AI providers for custom solutions – a pragmatic move, but one that means many “new” clinical AI products rely on the identical brains provided by a handful of tech giants. The result? Competing physician-support AIs often differ more in branding and user interface than in underlying capability.

Diagnostics is another arena flooded with AI claims. Dozens of tools promise to interpret X-rays, pathology slides or blood tests with superhuman savvy. Yet developing a novel medical AI from scratch is arduous – it demands curated data, specialized models and validation in clinical trials. Far simpler is to take a general image recognition model or GPT-like text model and apply it to medical data. 

Many consumer health chatbots, for instance, simply wrap a generic LLM like ChatGPT and answer patient questions with eloquent general knowledge. Even some diagnostic startups explicitly brand themselves as “ChatGPT for X.” (One recent entrant called BloodGPT does exactly what its name implies – aiming to turn lab results into plain English explanations – proudly embracing the ChatGPT-for-blood label.)

The danger is that a generic AI may sound confident on medical matters while lacking the rigorous accuracy of a truly specialized system. A clever demo does not equal a clinically vetted model. As the founder of BloodGPT points out, an error rate of even 5% in healthcare “could cause real harm,” which is why his team claims to have built their system from scratch with traceability and validation – a deliberate contrast to “consumer-grade” AI that merely puts a glossy wrapper on a general model.

In short, a great many biotech AI offerings today are AI middlemen. They operate by taking a general-purpose model (often created by OpenAI, Google, Meta, or an academic lab), and repackaging it for a niche use. This is not to say such repurposing has zero value – integration and ease-of-use matter, especially in settings like hospitals where user experience counts.

But calling it proprietary “innovation” is stretching the truth. At worst, it verges on tech arbitrage: present a well-known AI’s capabilities as if it were your own cutting-edge invention. It brings to mind The Wizard of Oz – lots of smoke and theatrics, but behind the curtain it’s the same old wizardry that everyone else has access to.

Why It Matters: Hype, Moats, and Medical Integrity

The prevalence of AI-wrappers is more than a semantic quibble; it has real implications for investors, customers, and the progress of healthcare AI itself. First, business sustainability is in question. A startup whose product is essentially an API call to a third-party model has no durable moat. The moment the underlying AI provider offers a similar feature directly, the startup’s raison d’être evaporates.

We have seen this movie before: an “AI email assistant” built on GPT-4 can vanish when OpenAI adds the same email drafting capability to its own app. As a venture capitalist put it bluntly, betting on a company that is “just a wrapper” around GPT is betting on fragile ground. Dozens of nearly identical competitors can spring up overnight (and indeed they have).

The only survivors will be those who either build truly proprietary assets – unique data, custom models – or integrate AI so deeply into workflows that customers become dependent on the broader solution, not just the AI’s answers. In the long run, genuine algorithmic innovation or comprehensive service integration must replace the thin wrapper model.

Second, in the medical context, accuracy and trust are paramount. If multiple healthcare startups are all leaning on the same general AI brain, there’s a risk of homogeneous failure modes. A limitation or bias in the base model could be replicated across many tools. And when everyone claims their AI is special, it becomes harder for clinicians and patients to discern which tools have been rigorously validated.

The history of medicine is littered with snake oil and overhyped cures – a proliferation of copycat “AI doctors” could erode trust in the genuinely useful AI solutions.

Regulators like the FDA are already grappling with how to evaluate learning algorithms; they will not be amused if firms obfuscate the true source of their AI logic. Transparency about whether an algorithm is home-grown or a tweaked version of GPT-4 is not just an academic detail – it’s essential for accountability, especially if an AI’s advice could affect a patient’s life.

Finally, there’s the matter of industry focus. If talent and capital are pouring into startups whose main capability is prompt-crafting on top of someone else’s model, that might be diverting resources from tackling harder, domain-specific challenges. As one researcher mused amid the foundation-model buzz: are we adopting these models for real utility, or simply “chasing the allure of cutting-edge technology”?

The concern is that the hype around general AI in biotech could become a distraction – a fashionable veneer that doesn’t truly advance science. We’ve seen how initial euphoria for AI in drug discovery has given way to sober reality checks; for example, some deep learning methods failed to outperform much simpler models on certain biomedical tasks,

Complexity for its own sake, or for marketing, does not equal better outcomes. As the dust settles, the biotech AI companies left standing will likely be those that delivered provable improvements – whether via novel algorithms or just excellent implementation – not those that merely rode the hype.

Spotting a GPT-in-Disguise: A Field Guide

Given the proliferation of these AI wrappers, how can one discern a genuinely innovative AI biotech from a mere GPT-in-disguise? Whether you are an investor, a partner hospital, or simply an interested observer, here are a few tell-tale signs and questions to consider:

Vague vs. Specific Tech Description

Listen to how the company describes its AI. Is it all buzzwords and no details? Claiming “a state-of-the-art neural network” without explaining what makes it special is a red flag. Truly innovative firms can usually point to a proprietary model architecture, a unique dataset, or published research. If a startup’s explanation of its “secret AI” sounds like it came from a generic AI textbook, be skeptical.

Talent and Team Composition

Examine the team’s credentials. Building new AI models requires expertise. Does the company roster include experienced machine-learning researchers, computational biologists, or data scientists with relevant publications? Or does it look more like a group of product managers and MBAs who had an idea to apply ChatGPT to health care? A strong research team doesn’t guarantee originality, but a lack of one almost guarantees a dependency on external AI tech.

Evidence of Training and Data 

Inquire about the data and training process. A genuine AI model for drug discovery or diagnosis would need substantial domain-specific data (e.g. molecular libraries, patient records, images) and a training regimen. If a startup never mentions the painstaking work of data collection, cleaning, and model training, it may not have done that work at all. Conversely, if they boast about fine-tuning GPT-4 on a small custom dataset, you know the backbone is still GPT-4, not their own creation.

Performance Claims and Benchmarks

Look for independent validation. Are there peer-reviewed studies, FDA approvals, or at least concrete benchmark tests of the AI’s performance? A company that built something real will be eager to prove its merit. If all you see are anecdotal success stories and cherry-picked examples, suspect that the “AI” might be a generic model whose well-known limitations are being soft-pedaled.

Dependency Tell-Tales

Observe the product behavior and requirements. Does the tool require an internet connection and ping an external API when performing its AI functions? If yes, it could be relying on a cloud AI service (though to be fair, even proprietary models might be cloud-hosted). Also, note if the output style or knowledge base of the tool uncannily resembles that of ChatGPT or another popular model. For instance, if a clinical chatbot politely refuses certain requests or has knowledge cut off around 2021, you might just be conversing with a repackaged GPT.

Partnerships and Licenses

Read the fine print. Many startups openly announce partnerships with OpenAI, Google, or AI vendors – which is not inherently bad (it often improves their product). But it tells you whose engine is under the hood. If an “AI diagnostics” firm says it licensed a model from a big AI lab, then the core IP isn’t really theirs. On the other hand, if a company has filed patents for its algorithms or consistently publishes novel findings, that signals in-house innovation.

No single one of these indicators is definitive, but together they can paint a picture. The key is to distinguish true technical differentiation from cosmetic repackaging. Just as early internet companies had to prove they weren’t merely “a website with a thin idea”, today’s AI startups must prove they are more than a prompt on top of someone else’s network.

The Road Ahead: From Wrappers to Real Solutions

It is worth noting that not every startup using existing AI models is doomed to fail or deceitful in intent. In fact, some are candid about their approach and still offer value. The smarter founders recognize the wrapper trap and are already taking steps to escape it. They are investing in proprietary improvements – for example, Jasper, an AI writing assistant, decided to develop its own language models after initially relying on OpenAI, knowing that long-term success meant owning the tech.

Others are carving out specialized niches where general models fall short, thereby augmenting GPT with domain expertise. By deeply integrating AI into specific workflows (say, an AI tool embedded in hospital record systems that also handles data privacy and compliance), a startup can offer a complete solution rather than a standalone AI feature. Such integration builds switching costs and user loyalty beyond the AI’s raw output.

For the biotech sector in particular, the path to maturity will involve a shakeout. The glut of GPT-wrappers will likely be winnowed as the market discovers which tools actually deliver results. We may see consolidation, with larger firms acquiring startups that have good front-end products and plugging in their own AI models (why pay perpetual API fees to a third party if you can bring the tech in-house?).

Big pharmaceutical companies have already partnered with AI firms in droves – half of the top 50 pharma companies have some form of AI collaboration or licensing deal. Those partnerships will gravitate toward startups offering real novel tech or exclusive data, not ones offering what a dozen others can also provide.

In the end, separating hype from substance is in everyone’s interest. Biotech and healthcare stand to benefit immensely from authentic AI advancements – be it a new machine-learning model that can model protein folding pathways or a diagnostic algorithm that truly learns from millions of patient scans.

We are beginning to see the outlines of what lasting AI innovation will look like in these fields. It probably won’t be called “ChatGPT for cancer” or come with a cute AI name, but rather will be a deeply tested system quietly integrated into research labs and clinics, perhaps not even hyped as AI at all because it simply works.

For now, amid the hype cycle, healthy skepticism is warranted. As the old saying (almost) goes, if the AI claims sound too good to be true, maybe the “intelligence” is coming from elsewhere.

Or as Bartleby the Scrivener might have warned in Herman Melville’s tale: when asked to do the hard work, far too many startups today “would prefer not to.” The onus is on us – investors, scientists, clinicians – to peek behind the AI curtain. Demand to see whether there is a great and powerful innovation back there, or just a little GPT engine manned by a clever entrepreneur pulling levers. The promise of biotechnology deserves more than just artificial hype; it deserves real intelligence.

Sources: Recent analyses of the biotech AI landscape, including industry funding reports and expert commentaries, informed this article’s insights. Key references include an American Hospital Association report on generative AI startups in health care, a Rockefeller University review on the hype around medical foundation models, and a candid discussion on AI startups’ over-reliance on external APIs.

Examples like BloodGPT’s development of a tailored model (vs. generic chatbots) illustrate the differences between merely wrapping existing AI and building new solutions. As always, distinguishing true innovation from wrapper theatrics is crucial in assessing the real impact of “AI-powered” biotech companies.