Playing Devil’s Advocate on AI Wrappers

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In the world of venture capital, making contrarian bets can be a catalyst for outsized returns. Unsurprisingly, the tech and investor communities often debate whether “AI wrappers” (applications built on top of foundational AI models) indeed present an investable opportunity—or whether they’re doomed to be features rather than platforms. Despite the negative stigma attached to these so-called “wrappers,” history shows that dismissing them might be premature.

Below, we’ll explore why building on top of existing AI infrastructure can mirror the success of past SaaS companies, how the AI ecosystem is structured, and why the application layer—often written off as “just wrappers”—might be one of the most exciting places to be.

An “AI wrapper” is a shorthand (sometimes used dismissively) for any lightweight application that layers a user experience or specific functionality on top of an existing AI model. A classic example is an app that allows users to “chat” with a PDF: you upload your document, and the tool uses an AI model to answer questions about it. Early on, these tools sprang up rapidly because ChatGPT didn’t allow PDF uploads or custom GPTs, leaving a need that small teams filled within days.

Because many of these applications were built quickly and often lacked a more profound roadmap, they were quickly overshadowed when more prominent players like OpenAI integrated the same features natively. This deluge of opportunistic creations led some investors to label them “AI wrappers” with a negative connotation—akin to fleeting trends that lack substance and defensibility.

Yet, if we step back and recall how most SaaS businesses were built, they almost always rely on foundational platforms and infrastructure. Calling a customer support platform a “database wrapper” because it depends on MySQL (or a telecom solution a “VoIP wrapper” because it uses Twilio) would sound absurd, but that’s often what’s happening now in the AI space.

Y Combinator partners recently noted that many SaaS companies could be described as “MySQL wrappers” if we oversimplify their relationship to foundational technologies. This is a flawed argument, but it points to an essential truth: successful businesses often outsource their core infrastructure so they can focus on differentiation and innovation at the application layer.

For example, companies like Aircall or Talkdesk outsourced their telephony infrastructure to Twilio—one of the largest providers of VoIP and SMS services—so they could invest resources in product features and integrations that genuinely matter to customers. Those companies established massive, billion-dollar enterprises by delivering workflows and user experiences tailored to specific business problems.

If we extend this analogy to AI, the question is less about whether “wrappers” have inherent value and more about whether the product provides enough differentiation.

An AI wrapper with an actual vision— that solves mission-critical workflows— and offers deep domain expertise can become essential.

Broadly speaking, today’s AI landscape can be divided into three layers:

Comprising cloud data centers, GPUs, operating systems, and other core hardware technologies from large corporations like Microsoft, Amazon, NVIDIA, and Google. The barrier to entry is immense—billions (or even trillions) of dollars—so it’s tough for new startups to compete here at scale.

This layer is dominated by large scaleups (OpenAI, Anthropic, Mistral) and tech giants (Meta, Google) racing to build the best foundational AI models. Whether one model outperforms the rest or eventually converges into a commoditized set of offerings remains to be seen. This layer also requires substantial capital investment, constraining the number of viable new entrants.

The application layer is where AI’s capabilities become tangible to end users. ChatGPT is the most famous example: a simple, text-based interface that ignited the public imagination.

The application layer is arguably where new startups have the biggest opportunities, especially if they can integrate foundational models in creative, high-value ways.

In the same way that Salesforce created a massive ecosystem around its CRM, the model and infrastructure layers in AI will likely enable entirely new generations of software companies to be built—and capture significant value themselves.

ChatGPT proved that even a bare-bones text interface can make huge waves if it delivers meaningful value. It wasn’t about bells and whistles—it was about unlocking a new, powerful experience that no one had seen before.

As AI becomes deeply integrated into OS-level assistants (e.g., Apple Intelligence), workplace productivity tools (Office 365, Google Workspace), and communications apps (Slack, Microsoft Teams), tens of millions of people will use AI daily. This growing user base, combined with breakthroughs in infrastructure and models, creates a massive opportunity for agile startups.

However, the nature of AI-based software is different from traditional rule-based apps. Designing, deploying, and iterating on an AI product requires new tools, new testing and quality assurance forms, and different data management and compliance approaches.

This inherent complexity, ironically, can be a moat for well-executed startups.

A core question arises: does a new AI product integrate into existing ecosystems or aim to disrupt them entirely? Building a “Salesforce add-on” or an “SAP add-on” can be tempting for quick revenue and user acquisition. But some argue it’s better to build a standalone product (like HubSpot or Zendesk did) and eventually compete head-to-head with the incumbents.

The integration path often wins in the short term:

• Faster go-to-market

• Less friction with existing users, data, and workflows

• Immediate ecosystem credibility

The disruption path can pay massive dividends if you truly redefine how tasks get done:

• Full control over the user experience and AI capabilities

• Ability to pivot faster without constraints imposed by existing platforms

• Larger potential upside if you become the new standard

It’s not a one-size-fits-all decision. In many cases, the best strategy might be a hybrid: start as an integration to gain early traction, then expand into a broader platform once you’ve learned the space and built customer relationships.

As large language models (LLMs) become more capable and commoditized, many founders consider building narrow or specialized models that claim incremental performance gains. But does a small model with marginally better outcomes really stand a chance against a general-purpose AI that’s constantly improving and already embedded in a user’s daily workflow?

It depends on use case and execution:

In intensely regulated or specialized industries (healthcare, finance, legal), fine-tuned smaller models might provide essential domain accuracy, compliance, or data privacy advantages. This specificity can be invaluable, and large LLMs might not easily replicate these benefits—at least not immediately.

Better tech alone rarely wins. Execution around brand, distribution, integrations, and user experience often decides a startup’s fate. A marginally better small model could lose if it lacks a comprehensive product strategy. Conversely, a well-branded, well-distributed “wrapper” that continuously leverages the best available API might outpace a specialized competitor in reaching users and iterating quickly.

Ultimately, focusing solely on “better tech” misses the bigger picture: brand, user experience, partnerships, and the ability to execute at scale typically matter more.

History shows that execution typically outweighs a slight technological advantage. Google wasn’t the first search engine; Apple didn’t make the first smartphone. They excelled at user-centric design, brand building, and establishing robust developer or partner ecosystems.

In AI:

• Speed matters. Releasing often, listening to users, and iterating beats slow perfection.

• Distribution channels matter. Where do your customers discover and adopt your product? Do you have partnerships or a marketplace presence that catapults growth?

• Workflow integration matters. Software is rarely isolated; it lives alongside CRMs, ERPs, or communication platforms. A solution that seamlessly integrates and automates complex tasks becomes indispensable.

Why focus on marginally better tech? Because in some domains, slight gains in accuracy or performance can be game-changing. But if you’re only banking on that without the distribution, product design, and brand muscle to back it up, you’re likely to lose to a more holistic competitor.

Far from being “just wrappers,” these AI applications can offer massive value by simplifying, automating, or reimagining workflows that were previously manual or cumbersome.

Building on Salesforce or another incumbent platform can provide immediate user access and revenue, but it can also limit long-term independence. Choose your strategy based on your domain, your market, and your ambition.

A marginally better small model might not guarantee victory if a powerful, general-purpose model is already widely embedded. But domain-specific needs, privacy concerns, and compliance can tilt the market in favor of specialized solutions.

Whether you’re building a small model, a large model, or a “simple” wrapper, the real game-changer is how well you execute on product, distribution, partnerships, and user experience.

Successfully embedding AI into mission-critical workflows—where your platform becomes the default interface—is a robust defense. Best-in-class integrations, testing frameworks, security, and support can form a moat that’s hard for more prominent players or copycats to replicate quickly.

Calling AI applications “wrappers” overlooks the reality that all modern software is built atop deeper layers of technology. While it’s easy to be skeptical—especially with opportunistic founders churning out low-effort apps—there are immense opportunities for startups that focus on workflow design, robust integrations, and meaningful AI-driven value.

Will the next wave of AI products be pure disruptors, fully integrating into existing ecosystems, or a bit of both? That depends on each founder’s strategic vision, the user needs they target, and how well they execute.

After all, in AI as in any tech domain, building a sustainable advantage usually depends less on having “the best model” and more on having the best product—and the best product is almost always the result of relentless, disciplined execution.

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