The new industry revolution with large language models

The world is in the middle of a new industrial revolution, and large language models are leading the charge. By 2026, language models have grown exponentially in size and capability and moved from research demos into everyday business tools, giving them an unprecedented ability to generate intelligent insights and predictions. Businesses that can harness this power will be able to gain a significant competitive advantage.

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Moore Law in Large Language Models?

As you can see in the following photo, the size of language models are growing exponentially in the past years. While people are still impressed by the powerful capabilities of GPT3, Switch Transformer and Wudao have already surpassed the size of GPT3 in a short amount of time. If this trend continues, many industries will be disrupted, and we will see a fundamental change in our society within our lifetime.  

(From Tsinghua University)

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Impact on ML product development

The recent development of a large language model will fundamentally change how ML products are built. Building ML products used to take from a few months to a couple of years, to collect, clean and label data, train the model, fine tune parameters, then build a product. After that, ML teams need to monitor the performance of the models and keep refining it. The following is the previous machine learning product development process.

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With language models, the ML product development process shortens dramatically. People can build a prototype in days through prompt design and tuning the API parameters. With additional parameter fine tuning, example testing, and product development, an experienced engineering team can launch and iterate an AI product in months. By 2026, this has become the default way many teams ship AI features, accelerated further by mature APIs and agentic frameworks.

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What are the use cases of large language models?

Large languages can be used in many different tasks, including:

• Text generation: many startups have been using large language models to write blogs, product descriptions, ads, twitter content etc. Even part of this article is written by an AI writer that I created myself.
• Chatbot: Chatbot is not new, but with large language models, we can bring chatbot to the next level. This may disrupt the current customer service chatbot industry.
• Q&A: With a few examples, people can simply write a question and get the answer directly from a large language model. Someday people may stop using Google to search through websites
• Translation: Large language models work perfectly when it comes to translating as well.
• Writing code: Yes, we can even write code with large language models. One day we human engineers may lose our jobs to AI programmers?

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So can everyone build ML products? What are the barriers?

With a large language model, companies can focus on customers, build a great product, and solve a pain point. Certainly even with a large language model, engineering teams still need to put a lot of effort into building a product, including user interface design, system design, server deployment, and etc. It still takes a seasoned engineering team to build a great product.  We may see a large wave of ML startups emerging, but only the ones who truly understand their customers can win.

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Laxis is building the next generation of AI assistant with large language model

Large language models have the potential to revolutionize the way businesses operate. At Laxis, we are super excited to create the next generation of AI assistants with large language models. It is not just an AI note taker, but a true AI assistant that can provide real time conversation insights and automate your day to day work. If you are interested, feel free to reach out to [email protected] for a demo.

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Frequently Asked Questions

What is a large language model and why is it considered an industrial revolution?

A large language model is an AI system trained on vast amounts of text so it can understand and generate human-like language, answer questions, summarize, translate, and write code. It is described as an industrial revolution because, much like steam or electricity before it, this technology dramatically lowers the cost and time required to build intelligent products and is reshaping how nearly every industry operates.

How do large language models speed up AI product development?

Before large language models, building a machine learning product could take months or years to collect data, label it, train a model, and tune parameters. With modern language models and mature APIs, teams can build a working prototype in days through prompt design and parameter tuning, then launch and iterate a full AI product in months rather than years.

What are the most common business use cases for large language models?

Large language models are widely used for text generation such as blogs, ads, and product descriptions, as well as chatbots, question answering, translation, and even writing code. These same capabilities power tools like Laxis, which uses language models to turn conversations into real-time insights, summaries, and follow-up actions.

Can any company build products with large language models, or are there barriers?

Language models make it far easier to start, but building a great product still requires real engineering effort in user interface design, system architecture, and deployment, along with a deep understanding of customer pain points. Access to the underlying technology is no longer the main barrier, so the companies that truly understand their customers are the ones most likely to win.