Over the past couple of years, we’ve seen a wave of “wrapper” AI companies pop up. These are the startups that don’t train their own models, but instead sit on top of foundation models like GPT or Claude, adding prompts, workflows, and lightweight integrations to make them feel like full products. They were the easiest way for many people to experience generative AI for the first time.

But we don’t think this model will last. Wrappers might look useful in the short run, but the foundations they’re built on are shifting fast — and what feels like a moat today is often gone by the next model release. In this blog, we’ll walk through five reasons why wrappers are set up to fail, and why the future of AI is being shaped elsewhere.

Argument 1 : Model providers inevitably absorb wrappers’ moats

Wrapper companies build thin layers around foundation models, adding prompts, tools, or workflow logic. But these moats are structurally unsustainable. Every interaction with wrappers — prompts, user data, workflows — is routed back to model providers like OpenAI or Anthropic. Through RLHF and fine-tuning, providers internalize the very optimizations wrappers offer, making the next model release natively capable of those tasks.

This creates a flywheel: wrappers train away their own edge, while providers scale the capability across the entire market. Combined with providers’ stronger economic incentives to sell outcomes rather than raw tokens, wrappers are left with no durable differentiation. Their advantage is absorbed upstream — their moat effectively dissolves.

Evidences

• Startups like Windsurf and Cursor have been outpaced or made redundant by more integrated and capable offerings such as OpenAI Codex, Anthropic’s Claude Code, or Google’s Jules.

• In late August 2025, Anthropic announced a policy change stating that it will now use user chats and coding sessions from its consumer products (free, pro, and max tiers of Claude) to train its AI models by default, unless users actively opt out. The company emphasized that real-world interactions—such as coding help or writing assistance—provide valuable feedback that improves the model’s performance on similar tasks in the future, creating a continuous learning loop to enhance Claude’s capabilities.

• The paper on Multi-turn Reinforcement Learning from Human Preference Feedback introduces new methods for aligning Large Language Models with human preferences over entire multi-turn conversations, rather than at individual turns. Which is identical to multi-turn workflows, impact of multi-turn RLHF can already be found with latest GPT-5, new iterations of Claude code etc. Which understands the scope of discussions, workflows and their next step.
  
• The InstructGPT paper by OpenAI explains how user-submitted prompts from the OpenAI API, along with labeler-created prompts, were used to gather data for training language models. Human labourers ranked different model outputs for these prompts, and these rankings were used to build a reward model as part of the Reinforcement Learning from Human Feedback (RLHF) process. This reward model then guided fine-tuning, allowing the model to learn to prefer responses that align more closely with human preferences—making real user behavior and feedback central to improving the model’s performance.
  

Argument 2 : Industry is shifting towards universal/adaptive agents

The AI market is moving beyond domain-specific agents toward universal, adaptive ones. These systems can handle multi-turn workflows, learn from repeated interactions, and evolve across contexts — effectively becoming flexible “interns” that improve with use.

For wrapper companies, this shift is existential. Their value lies in encoding workflows and domain knowledge into prompts and thin integrations. But adaptive agents learn those same workflows directly, folding the wrapper’s contribution back into the foundation model. Over time, the model natively replicates and scales what wrappers provide.

As universal agents rise, wrapper moats erode faster. Instead of maintaining niche applications, wrappers are displaced by adaptive systems that continuously improve and generalize — making wrappers redundant.

Evidences

• Google’s Jules and Anthropic’s Claude Code demonstrate advanced agentic planning, integrated tool use, and real-time self-critique—capabilities that were once the domain of orchestrated wrapper systems but are now embedded directly in the model layer, further collapsing the need for external tools.
  
• Universal agentic tools like Manus and GPT operators are rapidly making wrapper AI tools obsolete. Unlike wrappers, which primarily layer a thin interface over foundational models to perform narrow tasks, agentic platforms operate with deeper autonomy, orchestration, and adaptability. They integrate reasoning, planning, and multi-step execution natively, reducing the need for brittle wrappers that rely on prompt hacks or API chaining. In effect, Manus, GPT Operator, and similar players are commoditizing what wrappers once offered, while providing a foundation for scalable, durable, and general-purpose automation—leaving wrappers as an evolutionary dead end.
  
• The Model Context Protocol (MCP), developed by Anthropic and increasingly adopted across the AI industry—including by OpenAI, DeepMind, and others—is a standardized framework for how large language models interface with tools, external data sources, share context, and execute functions. It simplifies the complex, custom integrations that wrappers typically build by standardizing the connection between models, APIs, prompt logic, and tools. This lowers the barrier for universal agents to handle workflows that previously required bespoke engineering. As these capabilities become standardized and built-in at the model level, much of what made wrappers valuable—custom tool integration and workflow logic—is commoditized, reducing their relevance in a world dominated by adaptive, general-purpose agents.

• OpenAI introduced built-in tools in its “Agents SDK” and “Responses API” features. These include things like web search, file search, computer use, code interpreter, image generation. Instead of wrappers/custom agents built externally, developers can now use these capabilities natively via OpenAI’s APIs.
  
• OpenAI’s Responses API is explicitly described as “combining the simplicity of the Chat Completions API with the tool-use capabilities of the Assistants API for building agents.” This means rather than build your own wrapper or agent orchestration system, OpenAI is exposing agent-like/workflow features directly.
  
• Anthropic also offers “Tool Use” features: Claude (Anthropic’s model) can invoke tools, fetch external data, call APIs, integrate structured inputs/outputs, etc. This again corresponds to functionality wrappers used to implement.

Argument 3 : Model Providers Will Shift from Selling Tokens to Delivering Outcomes

The economics of AI favor providers moving up the stack. Selling API tokens is a low-margin, volume-driven business. In contrast, delivering outcomes — vertical applications, workflow automation, and domain-specific solutions — offers higher margins and direct ownership of customer value.

For wrapper companies, this shift is fatal. Their role as intermediaries depends on reselling token access wrapped in prompts and workflows. But model providers have both the incentive and capability to subsume those layers, capturing more revenue while eliminating middlemen.

Every wrapper interaction — prompts, workflows, user data — is absorbed back into the model through RLHF and fine-tuning, accelerating this dynamic. Over time, the provider not only owns distribution but also replicates the wrapper’s edge natively, leaving no space for sustainable wrapper businesses.

Evidences

• Intercom’s Fin switched from a per-seat SaaS model to $0.99 per resolved conversation, showing how outcome-based pricing captures more value.
  
• OpenAI is not just selling APIs but moving into verticals, including a planned Jobs Platform for AI-driven hiring and matchmaking.
  
• Anthropic’s vertical offerings (e.g., Claude for Financial Services with Bloomberg/S&P integrations) illustrate provider expansion directly into wrapper territory.
  
• GitHub Copilot charges $10/month with unlimited usage, while Cursor costs $20/month with strict request limits. Developers have migrated to provider-native tools for both cost and convenience.

Argument 4 : Enterprises Will Abandon Wrappers and Proprietary Models and own their own AI

Usage of wrappers and third party AI services/models would leak proprietary workflows, eroding the very advantages that make enterprises competitive. By routing data, SOPs, and processes through external APIs, companies unintentionally democratize their differentiators for example,  what makes the #1 pharma or tech company unique – their proprietary process, check and balances, SOPs etc, becomes available to all competitors in the next model release.

Enterprises will recognize this as an existential risk. Continuing to rely on wrappers or external proprietary models means losing control over their most valuable asset: domain-specific data and processes. The rational path forward is to own AI infrastructure directly — training and running models in-house, on proprietary data — to preserve uniqueness, safeguard moats, and avoid being commoditized by foundation providers.

Evidences

• A Business Insider study calls out that AI and security concerns fuel a shift to on-premises IT infrastructure as companies seek more control of data.
  
• Another study warns that “Shadow AI” — employees using unapproved AI tools — creates major risks of sensitive company data leaks and urges organizations to manage this with clear policies, training, monitoring, and secure internal AI alternatives.
  
• A new research report by MIT Sloan Management Review and Boston Consulting Group found that more than half (55%) of all AI failures come from third-party tools. Company leadership might not even know about all of the AI tools that are being used throughout their organizations, a phenomenon known as “shadow AI.”
  
• BNP Paribas rolled out an internal “LLM-as-a-Service” platform run by the bank’s IT teams to give business units secure access to models; also partnered with Mistral AI as part of a European “sovereign AI” push.
  
• Bloomberg built BloombergGPT, a finance-tuned LLM developed and operated for internal products and workflows (not an external LLM SaaS).
  
• Samsung Electronics developed Samsung Gauss (and Gauss 2) as in-house GenAI models used for employees and device features; these are operated by Samsung rather than consumed as a third-party AI service.

Argument 5 : The True Power of AI Is in Adaptation — But Wrappers Lock It Into Static Workflows

AI’s unique strength is its ability to learn and adapt continuously. With every interaction, it can refine responses, personalize workflows, and improve both top and low performers — compounding efficiency over time.

Wrappers, however, constrain this potential. By hardcoding prompts and rigid workflows, they replicate the old software paradigm: standardized, one-size-fits-all processes. This strips AI of its adaptive advantage and locks organizations into static logic that quickly becomes obsolete.

In effect, wrappers force AI to behave like traditional software — limiting dynamism, reducing differentiation, and preventing enterprises from realizing AI’s true compounding power.

Evidences

• The AI Agents Reality Check (Cre4T3Tiv3) is a rigorous benchmark designed to quantify the difference between true autonomous agents and simpler “wrapper” or prompt-chained systems. In the standard 5-iteration evaluation, Real Agents achieved ~ 93% success vs ~ 20-26% for Wrappers, with much higher context retention and lower cost per successful task. Under stress tests (tool failures, unstable networks), Wrappers’ performance collapsed (~ 20-25%), while Real Agents held up at ~ 75%.
  
• Industry is moving towards “Agentic RAG” and “dynamic Agents” that can dynamically plan, reflect, adjust retrieval strategies etc. This adaptability improves how well they handle varied input or changing requirements.

Final Thoughts,

The short-lived utility of AI wrapper companies highlights a deeper truth: the real value in enterprise AI doesn’t come from thin layers of prompts and integrations, but from durable ownership of intelligence itself. Model providers are already absorbing wrappers’ advantages, shifting toward adaptive agents, embedding tool use, and capturing the economic upside of outcomes rather than tokens. At the same time, enterprises are recognizing the existential risks of outsourcing their workflows and data to external APIs, and are beginning to build or fine-tune models in-house to protect their competitive edge.

Wrappers, by design, freeze AI’s potential into static workflows—robbing it of its adaptive power and leaving businesses exposed to commoditization. The companies that win in the next wave of AI won’t be those building wrappers around someone else’s intelligence, but those who take control of their own models, data, and infrastructure. In other words, the future of enterprise AI will belong to those who treat AI not as an outsourced API, but as a sovereign capability.