In the landscape of the rapidly evolving AI today, many founders and observers find themselves concerned with the idea that successful startups must build a fundamental technology from zero. Nowhere is this story is more widespread than among those who launch “LLM packaging” – companies whose main offer is based on large -language models (LLM) such as GPT or Claude. There is a temptation to reject these companies as lacking in innovation or technical depth. But this perspective lacks a deeper truth: Customers do not care if you are “just packaging” – they care if you solve their problem1.
The “wrapper” economy of AI technology: the value is used, not in the invention
Each company thrives something “envelops”. Uber is a giant of 190 billion dollars, but its platform is essentially a wrapper around taxis. Airbnb, worth $ 87 billion, is a market wrapping the concept of hotels. The real value of these companies was not invent taxis or hotels, but creating seamless evolutionary solutions for transport and accommodation, respectively1.
The same dynamic takes place in AI. Companies like Harvey (AI legal, evaluation of $ 5 billion, $ 75 million arr), perplexity (research fueled by AI, evaluation of $ 18 billion, monthly income rate of $ 150 million) and cursor (development tools, evaluation of $ 10 billion) are prospered as “lots”1. What they have in common is a relentless accent on Resolution of real and vertical problems – not Build everything from scratch.
Infrastructure vs solutions: why packages are required
Foundation model suppliers – Openai, Anthropic, Google – are infrastructure Companies. Their platforms are for general use and cannot approach each vertical case, use cases or workflow. They need focused packaging1.
False ideas and moat: are packaging sustainable?
The skeptics argue that LLM packaging is vulnerable: what happens if fundamental AI suppliers simply build the functionality themselves? This risk is real, but not different from the risks faced with Uber and Airbnb during their ascents. The trick is to build Distribution dumpsins and significant product differentiation1.
Companies like Uber have sailed on local regulations, assembled with large pilot networks and gained user confidence – benefits that are not easily reproduced by infrastructure players. In the AI, the same goes: the packaging that goes deep on vertical problems and provide incremental improvements that count to users can gain on distribution, brand and execution1.
That said, low effort packaging – Those who do not more than call an API with an prompt – are likely to be crushed as infrastructure providers are evolving. Mission -centered packagingWho redefine the workflows or deal with the complex, nuanced pain points, have power.
Concentrate on value, not vanity
Customers pay results, not for the technical purity of your solution. Uber users wanted reliable and affordable rides, not a revolution in vehicle engineering. AI products users want tools that make their workflow smarter, faster or more intuitive – with little interest in the underlying technological battery1.
Will the future: Will the trend of “Wrapper” last?
It is true that the obstacles to entry into AI application-layer companies seem lower today than in previous platform changes. As the LLM infrastructure improves quickly and is quickly consolidated, not all “wrapper” will survive. The market can see a “PETS.COM vs Amazon” defeat: only those who solve real needs, build faithful user bases and forge a strong distribution to survive in the media threshing cycle1.
Conclusion
“WRAPPER” criticism lacks the point. Innovative solutions companies envelop technology, not because they lack ambition, but because this is where value is created. As history shows, the future belongs to those obsessed with the resolution of customer problems – not to those who are concerned about the thickness of their technological layer.
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