The Rise of AI Adoption: Unleashing Economic Potential and Transforming Application Development

It’s hard to ignore the rapid and widespread adoption of artificial intelligence (AI) and its transformative impact on business. At Armorblox, we’re no strangers to AI. We’ve been leveraging AI, LLMs, and ML for years to protect businesses against email-based threats. We were using GPT before it was cool by training it to detect malicious intent within emails.

We’ve always known AI would change the way we do business, but to what extent was hard to say. Now, with more interest and adoption, we have some recently released insights into how rapidly it is being adopted and the role we can anticipate AI to play in our lives, including the economic impact it will have, the way it will change the job landscape, and the changes it is making to the development of new technologies.

Key insights are now available from [McKinsey & Company’s “The Economic Potential of Generative AI: The Next Productivity Frontier” ](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction)and [Sequoia Capital’s “The New Language Model Stack: How Companies Are Bringing AI Applications to Life”](https://www.sequoiacap.com/article/llm-stack-perspective/). Let’s review the key findings from this new research and explore the wider landscape of AI adoption and the implications it holds for businesses across industries. From productivity transformations to industry-specific applications, we’ll delve into AI’s influence, guided by Armorblox’s real-world experience in building custom LLMs and generative AI.

## **The Economic Potential of Generative AI**

McKinsey & Company’s “[The Economic Potential of Generative AI: The Next Productivity Frontier](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier)” report highlights the enormous economic impact that generative AI can have on businesses across sectors. Generative AI refers to AI systems that can autonomously produce new content, such as text, images, music, and more. Here are some of the key findings from this new research.

### **Productivity Transformation**

Generative AI has the potential to drive a significant productivity revolution, **estimated to contribute between $2.6 trillion and $4.4 trillion to the global economy.** This immense value is driven by improved decision-making, enhanced creativity, and automation of complex tasks. The report estimates that generative AI has the potential to automate half of today’s work activities by 2045.

### **Industry-Specific Applications**

The impact of generative AI extends to various industries, including healthcare, retail, manufacturing, software engineering, and finance. AI-powered automation and optimization enable businesses to streamline operations, reduce costs, and deliver personalized experiences to customers. In the high-tech sector alone, McKinsey & Company estimates an economic impact of $460 billion.

### **Job Displacement and Creation**

While AI adoption may result in job displacement in certain areas, the report suggests that the net impact will likely be positive. As AI automates repetitive tasks, it frees up human workers to focus on higher-value activities, leading to the creation of new job roles and opportunities.

## **The New Language Model Stack**

Sequoia Capital’s report, “[The New Language Model Stack: How companies are bringing AI applications to life](https://www.sequoiacap.com/article/llm-stack-perspective/)” provides insights into the speed of adoption of AI and LMs in the development of new technology and applications. Sequoia Capital interviewed 33 companies within the Sequoia network to gain insights into the applications being built and the technology stacks being used. Here are some of the key findings.

### **Adoption of Language Models is Underway**

Almost every company in the Sequoia network is integrating language models into their products. Examples include code auto-complete, improved chatbots, and AI-driven workflows in fields like art, marketing, sales, legal, accounting, and more. The new stack for these applications revolves around language model APIs, retrieval mechanisms, and orchestration, with increasing usage of open source tools. Many companies have adopted foundation model APIs, with OpenAI’s GPT being the most popular choice. A significant portion of these companies are also utilizing retrieval mechanisms such as vector databases, while some express interest in LLM orchestration frameworks like LangChain.

### **Companies are customizing LMs**

Companies are aiming to customize language models to their specific contexts and data. There are three main ways to achieve this: training a custom model from scratch, fine-tuning a base model with proprietary data, or using a pre-trained model with relevant context retrieval. The latter approach, often utilizing vector databases, is more accessible and cost-effective. The stack for LLM APIs and custom model training is expected to converge over time. Despite LLM APIs being readily available, many companies are still interested in training and fine-tuning their own models. It was also reported that startups are working on tools that cater to both custom model training and LLM API utilization.

### **Ease of Use of LMs Will Continue to Improve**

Sequoia Capital reports that the stack is becoming more developer-friendly, enabling a broader range of developers to work with language models. Tools like LangChain work to simplify the development process by addressing common challenges and facilitating integration with other systems and data sources. Language model applications are moving toward being multi-modal, combining text, speech, and image/video generation. In addition, the integration of multiple generative models enables richer user experiences and more complex tasks.

## **The Transformative Impact of AI**

Despite ‌current progress, we are still in the early stages of the AI revolution. As AI continues to advance, organizations that embrace its potential can gain a significant competitive advantage. McKinsey & Company’s report highlights the economic potential of generative AI, while Sequoia Capital’s report provides a framework for understanding how companies are adopting AI from a development standpoint and the tools they are using to do so.

As businesses and society move forward, it is essential to recognize that responsible and ethical AI adoption is key. Collaboration between humans and AI systems, along with ongoing investment in skills development, will ensure a successful transition into an AI-powered future.

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