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AI / Large Language Models

Three Big Bets on the Future of AI

At the upcoming SingleStore Now conference, developers will get hands-on sessions on how to build and scale compelling enterprise-ready gen AI applications
Sep 18th, 2023 9:13am by
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In April 2023, Goldman Sachs released a report estimating that advancements in generative AI have the potential to drive a 7% (or approximately $7 trillion) “increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period.” This prospect so clearly highlights why it is important to get the future of generative AI right, especially as it relates to data — the key piece that is arguably the heart of this technology.

So, what does the future of generative AI look like? A big part of it will be split-second curation, consolidation across multiple data sources and types and providing context to LLMs. To thrive and function their best, LLMs will need fresh, curated data and context for applications — all of which needs to happen in milliseconds, snaps of real-time. Let’s dive a bit deeper into the three tenets I am betting as the future of AI:

1. An Ensemble of LLMs

LLMs, or large language models, are “deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.” LLMs are the backbone of generative AI. As this technology continues to evolve, there is not going to be one universal LLM that dominates the market. Instead, organizations will leverage an ensemble of LLMs to power use cases, something we already see emerging today. For example, GPT4 is rumored to be not just one massive model but a collection of 10+ different models, each with 100 billion parameters all stitched together. Consequently, enterprises will have to have a combination of LLMs or foundational models that they start to leverage. I believe enterprises will hedge their bets and costs by utilizing multiple foundational models that accomplish specific tasks better than the others. This includes both open source LLMs like Llama 2 and Hugging Face, and private LLMs like OpenAI, Anthropic and Cohere.

2. AI Data Planes Emerge

For businesses, I believe there will be an AI data plane that sits between their ensemble of LLMs and their corporate data. Incorporating an AI data plane provides additional context and clean data to an ensemble of LLMs for instant responses based on data within the enterprise firewalls. These data planes will have to have the ability to ingest, store and process vector embeddings — along with other data types and structures, including hybrid search. This includes managing data access, security and governance, as well as a thin layer of intelligence that helps prototype and build applications rapidly and easily.

3. Real-time AI Will Increasingly Become the Norm

As AI proliferates and we start to interface with more audio and video-enabled AI,  businesses will demand access to fresh data in real-time (milliseconds) to provide the right context for foundational models. LLMs and other multi-structured foundational models will need to respond to requests in real-time and, in turn, will need their data planes to have real-time capabilities to process and analyze data in diverse formats. To execute on real-time AI, enterprises need to continuously vectorize data streams as they are ingested and utilize those for AI applications. Consequently, organizations will increasingly move toward a zero ETL philosophy to minimize data movement, complexities and latencies to power their AI apps.


The world of AI and generative AI is fast evolving. Newer applications, foundational and business models and supporting technologies are quickly emerging. SingleStore has been at the forefront of this gen AI revolution, with its built-in vector and multi-model capabilities to power fast real-time AI applications. Understanding the small pieces that make up the larger puzzle is key to getting the generative AI revolution right — and creating a future where this technology can be used to elevate human lives.

At our upcoming conference, SingleStore Now, we’ll be demonstrating hands-on sessions on how developers can build and scale compelling enterprise-ready generative AI applications. The event will feature customers, partners, industry leaders and practitioners like Harrison Chase, co-founder and CEO of LangChain. To learn more and to register for SingleStore Now, visit

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TNS owner Insight Partners is an investor in: Pragma, SingleStore, Real.
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