Startups are racing against LLMs
As LLMs become more powerful, capable, and generalizable, startups that build on top of them need to convince customers to stay
Introduction
Over the past few years, LLMs such as OpenAI’s GPT series, or Anthropic’s Claude, have transformed the technology landscape. These foundational models boast capabilities that range from natural-language understanding and content generation to complex reasoning and coding. The result has been a surge of entrepreneurial activity: startups racing to build complementary services, which harness the power of these LLMs while tailoring them to specialized use cases. These startups are frequently referred to, sometimes derisively, as GPT wrappers1.
Yet there is a looming, often unspoken dilemma: these startups are running on a treadmill whose speed is controlled by the very model providers they rely upon. As LLMs rapidly improve, each new generation absorbs additional capabilities that subsume the specialized functionality previously offered by a startup. This creates a perpetual race to outrun the foundational models. It is a race for which small ventures lack the capital, data, and resources to win.
GPT wrappers emerge from the aether
When OpenAI released GPT-3 in mid-2020, it astonished the broader tech community with its ability to generate coherent text and tackle diverse tasks. These tasks ranged from writing stories to answering research queries. Developers realized there were countless practical applications for an interface that could generate code, create marketing copy, or summarize complex documents at scale.
Entrepreneurs and engineers saw an opportunity. Although GPT-3 offered impressive raw capabilities, it was not, at the time, neatly packaged for specific use cases like legal drafting, medical diagnostics, or software engineering support. Startups began building wrappers around OpenAI’s API. These wrappers, which ranged from simple web apps to more elaborate AI orchestrations, made OpenAI’s product more user-friendly, domain-focused, or integrated into enterprise workflows. For instance, one startup might specialize in auto-generating marketing emails, while another focuses on generating robust code suggestions within an IDE.
The wave of activity sparked a gold rush. Investors sought to fund promising young companies that promised to deliver specialized solutions, with slick user interfaces, domain-trained prompt engineering, or subscription-based business models. In many ways, these GPT wrappers filled a gap between a powerful but generic model and customers who needed practical domain-specific AI assistance.
Foundational models start to evolve rapidly
Even before GPT-3 made headlines, researchers understood an important phenomenon known as scaling laws. Detailed in works by Jared Kaplan, DeepMind’s Chinchilla team, and scholars like Leopold Aschenbrenner, scaling laws imply that a language model’s performance on a broad array of tasks improves in predictable ways as you increase its computational resources (number of parameters, training data, and compute cycles).
Within just a few years of GPT-3’s introduction, OpenAI followed up with GPT-3.5, GPT-4, and, most recently, its o1 line of models. Other organizations, such as Anthropic with Claude, Google with PaLM, and others, have similarly pushed advanced capabilities at a rapid pace. Each new version showcased leaps in reasoning, coding support, language fluency, and specialized skills. For instance, GPT-4 introduced more sophisticated chain-of-thought reasoning, while also enabling function calling that lets the model interact with external APIs.
This frenetic pace is exactly what scaling laws predict. Even seemingly niche or emergent skills appear sooner than expected, as the model’s parameter count grows or as it is exposed to larger or higher-quality training data. Model providers, flush with capital and access to massive compute, can push the boundaries at a pace that wrapper startups struggle to match.
The threat to thin wrappers
Many of the first-wave GPT wrapper startups provided only incremental improvements: a friendlier web interface, a few specialized prompts, or a single integration. These thin wrappers delivered convenience, but not necessarily deep, defensible value. As the foundational model integrated function calling, retrieval plugins, or improved coding support, the new features quickly subsumed those wrapper functionalities.
A typical scenario would look something like the following: A coding assistant startup might use a GPT to generate scaffolding code, add a slick UI, and charge a monthly fee. But once the GPT’s next version becomes a more competent coder, the startup’s differentiator vanishes. Customers could “just use ChatGPT” directly, without an extra subscription. Key reasons these wrapper startups struggle:
Easy Feature Absorption. Foundational models incorporate advanced abilities, like multi-step reasoning, or better coding abilities, that replicate or outperform the thin wrapper’s functionalities.
Low Switching Costs. If the wrapper’s sole offering is a shallow feature set, customers can switch back to direct ChatGPT usage as soon as it offers comparable functionality.
High Customer Acquisition Costs. Wrappers must invest in sales and marketing to convince customers of their added value, yet that value is fleeting if ChatGPT moves to incorporate it natively.
Consequently, many wrapper startups find themselves overshadowed by advancements in foundational models. If a more advanced ChatGPT model can replicate a thin wrapper’s features, the startup’s future is grim.
Scaling laws force wrapper startups to run a race they can’t win
The deeper question is why GPT-style foundational models evolve so fast. According to scaling laws, large language models improve in a roughly predictable fashion when provided with more data, parameters, and compute. Just as Moore’s Law forecasts exponential gains in CPU transistor density, scaling laws illuminate how LLM capabilities jump as one exceeds certain thresholds of model size and training cycles.
In practice, this means large AI labs—OpenAI, Google, Anthropic, Meta, etc.—have a direct route to leveling up performance across a largue suite of tasks. These labs have vast amounts of capital, high-caliber AI research talent, and enormous amounts of data. Each time they incorporate feedback or new data sets, they can systematically integrate specialized capabilities sold by wrapper startups.
In order to maintain their edge, startups must continually add new layers of functionality or specialized knowledge faster than these labs can bake the same features into their base models. Yet hyperscalers have the advantage of integrating improvements at the model level, which is more powerful and efficient than an external application layer.
Thus arises the race to outrun scaling laws: can a startup develop genuinely unique value at a pace exceeding the direct improvements of the underlying model? For thin wrappers, the answer is usually “no.”
The customer is always right and they always want convenience
No business can survive without customers who see a compelling reason to use its product or service. In the context of GPT wrapper startups, potential customers evaluate numerous factors before signing a contract:
Immediate Gains vs Future Obsolescence
Value Now: If the startup’s product can deliver significant ROI this quarter, it might be worth adopting. A domain-specific coding assistant on steriods that cuts developer time in half has near-term appeal.
Future Risk: But is ChatGPT about to release a new model that delivers comparable coding support for free? If so, early adopters might regret paying for a third-party solution that will soon be obsolete.
Cost vs ROI
Cost Premium: Many startups charge a subscription fee on top of foundational model usage, so the total cost of ownership is higher.
Potential Return: If the product solves a specialized pain point, such as compliance in a regulated industry, or advanced data analytics, the near-term productivity gains might justify the markup.
Integration Complexity
Onboarding & Training: Adopting a new system often involves training developers, customizing workflows, or even integrating with enterprise software.
Switching Costs: If the startup’s solution is easy to replace, or if a foundational model provides the capability, customers will balk at making a big investment in the first place.
Regulatory and Compliance Requirements
Specialized Solutions: Healthcare or finance organizations require data governance, audits, and compliance. A specialized startup might have these capabilities.
GPT Evolution: Over time, major AI labs might also launch enterprise-grade, compliance-friendly versions. Customers ask: “Will waiting a few months deliver similar compliance from GPT itself?”
Vendor Longevity
Stability vs Rapid Model Improvements: Large enterprises worry about startups running out of funding or losing relevancy if overshadowed by the next version of a foundational model.
Brand & Trust: A startup with strong industry recognition or big-name clients might reassure potential customers that it won’t disappear overnight.
From this vantage point, the major question is whether a startup’s specialized features outweigh the risk that future foundational model releases will obviate the startup’s raison d’être. If the startup can’t demonstrate a true defensible moat, such as proprietary data or robust vertical integration, then most customers will either wait or choose a competitor.
Strategies for startup survival
All of the foregoing means that the deck is stacked against GPT wrapper startups. However, all is not lost. Some do manage to stay ahead, but they do so by focusing on deeper advantages beyond “thin” or easily replicable feature sets. Five core strategies follow:
Proprietary or Exclusive Data
Unique Knowledge Sources: A startup with exclusive licensing agreements, private knowledge bases, or real-time data streams can deliver insights that the base LLM simply does not have.
Constant Refresh: Even if a big model eventually gets partial access to similar data, a startup can remain competitive by continuously updating a specialized data pipeline, delivering fresh or highly specialized insights.
Complex Vertical Workflows
From LLM to End-to-End Solutions: Instead of merely generating text, a startup might automate entire tasks—drafting docouments, checking for compliance, integrating with third-party APIs, etc.
High Switching Costs: Deep integration with enterprise processes makes it harder for customers to abandon the startup in favor of direct access to a LLM.
Regulatory and Compliance Layers
Certifications & Audits: By achieving HIPAA, FedRAMP, or SOC 2 compliance, a startup can address risk-averse industries that standard GPT usage might not immediately satisfy.
Industry Partnerships: A specialized AI medical assistnat, for example, might partner with major healthcare providers. Such alliances become a moat that a new LLM update can’t simply replicate overnight.
Brand, Trust, and Support
Domain Expertise: In fields like finance or law, a brand that consistently delivers reliable outputs can build lasting credibility.
Service & SLAs: Offering robust customer support, guaranteed uptime, and accountability for errors can make the difference for enterprise clients who need more than a self-serve model.
Orchestration of Multiple Models and Tools
Advanced AI Pipelines: A startup might chain together multiple LLMs, retrieval systems, or analytics platforms, adding specialized logic to orchestrate tasks in ways a single model alone doesn’t replicate.
Continuous Innovation: If the startup is agile, and keeps layering on new capabilities, it may maintain a lead, though this is the toughest strategy, given resource constraints.
These strategies all revolve around creating true differentiation rather than shallow improvements. The result is an offering that customers cannot easily replace with the next LLM release.
Customer acquisition and lifetime value
As if running a race against foundational model builders was not enough, startups also have to acquire customers profitably. That is, their cost of customer acquisition (CAC) should be lower than the lifetime value (LTV) of that customer. For GPT wrappers, this is a balancing act:
High CAC in a Crowded Market. There are hundreds of GPT wrappers vying for attention, so marketing is expensive. A startup must stand out enough for prospective customers to convert.
Potentially Short LTV. If a LLM quickly replicates the startup’s features, customers may cancel subscriptions, yielding small lifetime customer value. This churn undermines profitability.
Margin Pressures. GPT wrappers are intermediaries, sitting between the underlying foundational model and the end users. This means the GPT wrapper pays usage fees to the LLM provider, in the form of tokens or API calls, and charge customers enough to cover these fees plus overhead. If they mark up too steeply for marginal features, customers will balk.
In short, startups building on top of large language models must ensure that their added value justifies the recurring expense. If they operate in a niche with minimal direct competition and can deliver high ROI, they may retain a healthy LTV despite LLMs’ increasing capabilities. Otherwise, cost-conscious customers will gravitate toward standard LLM usage or competing solutions.
Conclusion
The interplay between cutting-edge foundational models and startups building on top of them is both thrilling and daunting. On one hand, the possibility of harnessing advanced AI capabilities for a variety of applications, from coding and law to finance and medicine, represents a leap forward in software automation. On the other, the inherent dynamism of LLMs means that wrappers perpetually face obsolescence. Scaling laws predict, and real-world observations confirm, that major AI labs will continue upgrading their models at a rapid pace, often absorbing specialized functionality that once gave certain startups an edge.
For potential customers, the result is a need to balance immediate productivity benefits against longer-term viability. For investors and entrepreneurs, the message is clear: thin wrappers that simply repackage LLMs are unlikely to stand the test of time. By contrast, companies that build deep moats, whether through proprietary data, complex workflows, enterprise-grade compliance, brand trust, or advanced AI orchestration, may hold an enduring place in the AI ecosystem.
Ultimately, the ongoing dance between foundational model providers and specialized startups will shape the future of AI-powered services. Much like the PC market of the 1980s or the rise of cloud computing in the 2010s, a few specialized players will differentiate themselves, survive, and thrive, while countless imitators fade. By understanding this narrative and tailoring offerings accordingly, AI startups stand a fighting chance in the race to remain relevant in the era of large language models.
I will use the terms “GPT” and “GPT wrappers” throughout this essay, but it’s important to note that startups can conceivably be built on any kind of foundational model, not just OpenAI’s GPT series of models.
Very well done.