In today’s rapidly growing AI landscape, many founders and observers find themselves fully focused on successful startups that must build fundamental technologies from scratch. In the introduction of what is called “LLM wrapping paper”, this narrative is more common. There is a temptation to see these businesses as lack of innovation or technological depth. However, this view misses a deeper fact: Customers don’t care if you are “just a wrapper” – they care if you solve their problem1.
AI technology “packers” economy: value is being used, not invention
Every successful company will “wrap” something. Uber is a $190B behemoth, but its platform is essentially the wrapper around a taxi. Airbnb is worth $87B and is a market around the hotel concept. The real value of these businesses is no Invent a taxi or hotel, but create seamless, scalable solutions for transportation and accommodation, respectively.
The same dynamics play a role in AI. Companies like Harvey (Legal AI, $5B valuation, $75 million ARR), Pllexity (AI-driven search, 18B valuation, $150 million monthly revenue run rate) and Cursor (Developer Tools, $10B+ valuation) are booming around LLMS1. What they have in common is ruthless attention Solve real vertical problems – no Build everything from scratch.
Infrastructure and Solutions: Why wrappers are needed
Basic Model Provider – OpenAI, Human, Google – Yes Infrastructure company. Their platform is universal and cannot be solved by every vertical, use case or workflow. They need solution-centric wrappers to bring their technology to market and provide full potential for specific customer needs1.
Misunderstanding and moat: Is wrapping paper sustainable?
Skeptics believe that LLM wrappers are vulnerable: What if the underlying AI provider just builds the features themselves? This risk is real, but it is no different from the risks faced during the rise of Uber and Airbnb. The trick is to build Distribution nurse Meaning Product Differentiation1.
Companies like Uber have browsed local regulations, assembled a massive driver network and gained user trust—the advantage is not easily replicated by infrastructure participants. In AI, this is also true: Deepening into vertical issues and providing users with important improvements to the packaging can win on distribution, branding and execution 1.
that is, Low drink wrapping paper – Those who aren’t just calling APIs with tips – As infrastructure providers grow, they can be crushed. Task-driven wrapping paperredefine workflow or solve complex pain points, with endurance.
Focus on value, not vanity
Customers pay for results, not for the technical purity of the solution. Uber users want reliable, affordable rides, not a revolution in vehicle engineering. AI product users need tools to make their workflow smarter, faster or more intuitive, and they are not very interested in the basic technology stack1.
Future: Will the “wrapper” trend continue?
Indeed, today’s AI application-level enterprises have lower barriers to entry than previous platform shifts. As LLM infrastructure is rapidly improving and consolidating, not every “wrapping paper” can survive. The market may see bonuses for “Pets.com vs. Amazon”: Only those who address actual needs, build a loyal user base and forge a strong distribution can surpass the hype Cycle1.
in conclusion
“Wrapping Paper” criticism missed this. Innovative solutions companies packaging technologies, not because they lack ambitions, but because that is where value is created.. As history shows, the future belongs to those who are obsessed with solving customer problems, not those who are worried about their thickness of their technology.
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