La extinción de Gemini 3.1: Por qué las bases de datos vectoriales están muertas

On February 19, 2026, Google quietly rolled out Gemini 3.1 Pro to developers and enterprise subscribers. The mainstream tech press immediately fixated on the benchmark scores, specifically its 77.1% on the ARC-AGI-2 reasoning test, which more than doubled the performance of its predecessor.

But evaluating Gemini 3.1 simply as an iterative intelligence upgrade is a profound analytical blind spot.

When you synthesize elite agentic reasoning with a perfectly rigid 1-million token context window, you do not just get a smarter foundation model. You induce a structural collapse in the enterprise Artificial Intelligence (AI) infrastructure stack. The most lucrative middle layer of the AI boom, the vector database, is now dead tech walking.

For the past two years, corporations poured billions into infrastructure providers like Pinecone and Milvus to build Retrieval-Augmented Generation (RAG) pipelines. RAG was the duct tape of the AI revolution. It was an architectural workaround for models that could not retain or reason across massive datasets. With the February 19 release of Gemini 3.1 Pro, Google ripped the tape off.

The Duct Tape Architecture

To understand the extinction event, you have to understand the fundamental engineering constraint of the 2023 through 2025 AI era: context amnesia.

When an enterprise wanted to build an AI agent to analyze its internal documentation, say, 10,000 pages of legal contracts, it faced a hard limit. Early generative models could only process roughly 8,000 to 128,000 tokens at a time. If you fed the model a massive library, it would either crash, hallucinate, or suffer from the “lost in the middle” phenomenon, where it simply forgot the core details of the documents.

Enter the vector database.

The industry solution was to slice those 10,000 pages into tiny text chunks, mathematically convert those chunks into high-dimensional coordinates called embeddings, and store them in a specialized Vector Database. When an operator submitted a query, the application would not actually query the foundation system to read the documents. Instead, it performed a semantic search across the vector database to retrieve the five most relevant text chunks, bundled those chunks into a short prompt, and handed them to the system to generate an answer.

This architecture spawned a massive sub-industry. The global vector database market was valued at $2.11 billion in 2024, projected to grow at a 25.5% CAGR to nearly $13 billion by 2032. Every modern enterprise AI application was essentially a patchwork of embedding models, vector storage limits, routing layers, and retrieval pipelines.

To quantify the complexity, look at a standard RAG latency equation: $T_{response} = t_{embedding} + t_{vector\_search} + t_{LLM\_generation} + t_{network\_overhead}$

Each variable represented a distinct vendor, a discrete point of failure, and an additional unit of cost. It was brilliant, complex, and entirely temporary.

The Physics of Obsidian Memory

The premise of the vector database market assumed that context windows would remain expensive, narrow, and structurally fragile. Gemini 3.1 Pro shatters that assumption with brutal native efficiency.

With its 1-million token context window, Gemini 3.1 Pro can ingest roughly 3,000 pages of dense technical documentation natively, without slicing, without embeddings, and without an external database. But long context is not a new concept. Google introduced experimental million-token variants months ago.

The extinction trigger is the reasoning upgrade. Previously, if you fed an AI a million tokens, it operated like a sloppy intern. It could summarize the text, but it struggled with complex, multi-step logic spanning discrete data points. Gemini 3.1 Pro, heavily optimized for software engineering and financial workflows, possesses what analysts call “obsidian memory.” It does not just hold the data. It manipulates the data with high-fidelity, native agentic reasoning.

Consider a legal tech startup building an automated due diligence agent.

The Old Stack (RAG):

1. An operator uploads 50 contracts.
2. Custom software slices the contracts.
3. An embedding model creates vectors.
4. Pinecone stores the vectors natively.
5. A search query retrieves the chunks.
6. A foundation model synthesizes the chunks.

The New Stack (Gemini 3.1 Pro):

1. An operator uploads 50 contracts.
2. Gemini 3.1 Pro reads them and outputs the analysis.

For developers, eliminating the intermediary retrieval layer removes massive latency constraints, complex synchronization logic, and literal database hosting fees. You collapse a $5,000 a month infrastructure bill into a flat API token charge.

The Financial Mechanics of RAG Retrieval

To fully grasp why the enterprise market will violently pivot away from vector databases, you must analyze the true unit economics of a scaled RAG deployment.

The mainstream narrative suggests that RAG is a cheap alternative to Native Context. This was true when cloud providers charged astronomical API fees per million tokens. However, the cost of RAG is not just the embedding API or the generation inference. The true cost is the persistent memory allocation in cloud computing.

Vector databases rely heavily on RAM (Random Access Memory) to deliver sub-millisecond similarity search. Storing 1 billion 768-dimensional vectors requires roughly 3 Terabytes (TB) of memory. That requires highly provisioned, specialized cloud instances running 24 hours a day, 7 days a week, regardless of whether anyone is querying the database.

Conversely, a flat native context window is ephemeral. When you pass 1 million tokens straight into Gemini 3.1 Pro, you only pay for the exact compute cycles used during that specific inference request. When the generation finishes, the compute spins down to zero cost.

When you compare a persistent $50,000 annual vector hosting bill against a purely transactional token fee that scales directly with usage, the Chief Financial Officer at any major enterprise will mandate the transition to the native context window.

Where the Smart Money Retreats

Does this mean Pinecone, Weaviate, and Milvus go bankrupt tomorrow? No. But the institutional reality is that they are being violently shoved into a corner.

Vector databases will not vanish completely. They are retreating to the only domain where native context windows cannot compete: real-time search across billions of fast-twitch documents. If you are a social media giant searching through a billion user posts per second, or an e-commerce platform matching sparse behavioral data against a massive product catalog, you still need high-speed vector retrieval.

But that is not where the easy money is.

The explosive growth projection for vector databases was built entirely on bounded enterprise use cases: scraping Human Resources wikis, analyzing proprietary codebases, and querying bounded financial archives. These are datasets that easily fit within 1-million, or quickly scaling 2-million, token windows.

When you remove bounded enterprise applications from the vector database Total Addressable Market (TAM), the $13 billion projection for 2032 evaporates. Institutional investors know this. It is why the industry is seeing a shift away from middleware infrastructure investments and a consolidation of capital back into the foundation model hyperscalers who control the compute.

System Design and Latency

One of the most profound advantages of moving away from vector embeddings is the reduction in architectural cognitive load.

When building a traditional Retrieval-Augmented Generation system, developers are constantly tuning chunk sizes, adjusting overlap ratios, and experimenting with different embedding routines. If the vector database returns the wrong text chunk, the final generated output will be wrong, no matter how capable the underlying architecture is. This creates a brittle system where debugging requires forensically tracing the vector similarity scores.

With Gemini 3.1 Pro, developers effectively offload the entire retrieval mechanism to the transformer’s attention heads. Because the model sees the entire document perfectly, it handles the search internally during the forward pass. This reduces the application code from thousands of lines of orchestration logic down to a simple API call.

The Infrastructure Consolidation

What the market is witnessing is a classic technology platform consolidation, echoing the collapse of early internet middleware.

In the 1990s, when building a website required a complex stack of discrete routing, hosting, and content management tools, a thriving ecosystem of middleware vendors commanded multi-billion dollar valuations. Eventually, platforms like Amazon Web Services unified those services into native, frictionless primitives.

Google is executing the exact same playbook. By solving the memory retention and reasoning problem natively within the model architecture, they are rendering external memory systems obsolete for the vast majority of B2B applications.

This is the hidden cost of the AI revolution. The hyperscalers are not just building smarter models; they are absorbing the surrounding infrastructure value. With the release of Gemini 3.1 Pro, Google has signaled that the age of the RAG patchwork is over. The future of enterprise AI does not involve searching a database. It simply involves asking a machine that never forgets.