The AI scaffolding layer is collapsing, and that's not a bad thing. According to Jerry Liu, co-founder and CEO of LlamaIndex, this collapse is actually a sign of progress. The scaffolding layer that developers once needed to ship LLM applications - indexing layers, query engines, retrieval pipelines, carefully orchestrated agent loops - is no longer necessary.
As Liu explains in a new podcast, the need for frameworks to help users compose deterministic workflows is diminishing. With every new release, models demonstrate incremental capabilities to reason over massive amounts of unstructured data, and they're getting better at it than humans. They can be trusted to reason extensively, self-correct, and perform multi-step planning.
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The Rise of Context as the New Moat
Liu's LlamaIndex is one of the foremost retrieval-augmented generation (RAG) frameworks connecting private, custom, and domain-specific data to LLMs. However, even he acknowledges that these types of frameworks are becoming less relevant. The core differentiator now is context - the ability of agents to decipher file formats and extract the right information.
Providing higher accuracy and cheaper parsing becomes key, and LlamaIndex is well-positioned here due to its developments with agentic document processing via optical character recognition (OCR). As Liu notes, context is becoming the new moat, and companies that can provide the most accurate and efficient context will be the ones that thrive.
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The collapse of the scaffolding layer also means that the layers between programmers and non-programmers are collapsing. Coding agents excel at writing code, meaning devs don't need to rely on extensive libraries. In fact, about 95% of LlamaIndex code is generated by AI. This means that the new programming language is essentially English, and devs can just point Claude Code at a problem and let the AI handle it.
Keeping Stacks Modular in the Age of AI
There's growing concern about builders like Anthropic locking in session data, and Liu emphasizes the importance of modularity and agnosticism. Builders shouldn't bet on any one frontier model or overbuild in a way that overcomplicates components of the stack.
Retrieval has evolved into agent-plus-sandbox, and enterprises must ensure that their code bases are tech debt free and adaptable to changing patterns. They also have to acknowledge that some parts of the stack will eventually need to be thrown away as a matter of course. As Liu notes, with every new model release, there's always a different model that is kind of the winner, and companies need to be flexible to take advantage of it.
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