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  • Created: Oct 1, 2024
  • Author: thanh
  • Tags: llmai
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  • Folder: playground/ai/building-llm-system
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The rise of AI applications with LLM

In the course of technological history, few developments have captured the imagination and transformed industries as swiftly and profoundly as the recent surge in artificial intelligence. The release of ChatGPT marked a pivotal moment, followed by other tech giants entering the arena. Google introduced Gemini, Facebook unveiled Llama, and Anthropic launched Claude. These powerful AI foundation models have demonstrated an unprecedented ability to drive a wide array of tasks, significantly boosting productivity and creating substantial economic value. As a result, teams and individuals across various sectors have begun to explore innovative ways to harness AI for building a new wave of applications.

However, a significant roadblock has emerged on this path of innovation: the cost. Training large language models (LLMs) requires vast amounts of data, immense computational power, and specialized talent—resources that only a select few organizations can afford. This scenario is reminiscent of the early days of cloud computing, drawing parallels to the story of Amazon Web Services. In response to this challenge, a new paradigm has emerged: model-as-a-service. This approach allows models to be provided for others to use as a service, democratizing access to AI capabilities.

The advent of model-as-a-service has been transformative. Now, anyone wishing to leverage AI to build applications can do so with minimal upfront investments. Without these APIs, utilizing an AI model would require substantial infrastructure to host and optimize the serving of these models. With model APIs, developers can incorporate these powerful models into their applications via a single API call, dramatically lowering the barrier to entry for AI-driven innovation.

The power of foundation models extends beyond their ability to perform existing tasks more efficiently. Their capacity to generate open-ended responses makes them capable of tackling a broader range of tasks, including those previously thought impossible or not even conceived. This versatility has opened up new frontiers in application development.

The impact of AI on various domains is profound. Since AI can now write at a level comparable to or even surpassing human capabilities, it has the potential to automate or partially automate virtually every task that requires communication—which encompasses a vast array of human activities. AI is being employed to write emails, respond to customer inquiries, and summarize complex contracts. The accessibility of AI tools has democratized content creation; anyone with a computer and an internet connection now has access to tools that can instantly generate customized, high-quality images and videos for design, marketing materials, professional headshots, art concepts, book illustrations, and more.

Furthermore, AI’s capabilities extend to synthesizing training data and writing code, both of which contribute to the development of even more powerful models. The ability of AI to write code has been particularly transformative, enabling individuals without a software engineering background to rapidly turn their ideas into functional code and present them to users. The introduction of prompt engineering has further simplified interaction with these models, allowing users to work with them using plain English rather than traditional programming languages. This development has truly democratized AI application development, making it accessible to a much wider audience.

As AI applications become more cost-effective to build and quicker to bring to market, the return on investment for AI initiatives has become increasingly attractive. This has led to a proliferation of AI applications and services across various domains, both in greenfield products and AI integration, including:

The impact of this AI revolution is evident in several key areas:

Open source dominance

The number of new repositories for model development has nearly tripled from 2022 to 2023. In the period from 2023 to 2024, four out of the five most starred repositories on GitHub were related to AI and LLMs, underscoring the community’s intense focus on AI development.

Startup funding

According to a recent analysis of Y Combinator’s Summer 2024 batch, an astounding 72% of startups are focused on AI—a dramatic increase from just 1% in the winter of 2012. This trend far outpaces previous technology waves, such as the crypto boom.

Market interest

The interest in AI within the corporate world has surged dramatically. More than 16% of companies in the Russell 3000 now mention AI technology on earnings calls, up from less than 1% in 2016. Notably, about half of this increase occurred after the release of ChatGPT in Q4 2022. This heightened interest is often predictive of increased company-level capital spending in the technology.

Economic projections

The generative AI market is poised for explosive growth. Bloomberg Intelligence projects that the market will expand from $40 billion in 2022 to a staggering $1.3 trillion by 2032. This forecast underscores the immense economic potential and transformative power of AI technologies across industries.

To conclude, it is evident that AI has become one of the most disruptive forces in both technology and business. It is fascinating that ordinary people now have access to desiccated brains with the help of the internet and launch all sorts of ideas within. AI seems to be everywhere and seems to be here to change how we do work, how we innovate and how the economy is shaped. Within the next couple of years, we will already be witnessing an increase in organizations availing of AI, which brings with it fresh and exciting possibilities for firms and individuals as well.

References

Next: Use cases