The AI Knowledge Stack: Understanding Your Place and Maximising Leverage

Introduction

Artificial Intelligence is not a monolithic field, it’s an entire ecosystem with different levels of expertise, each playing a crucial role in advancing the technology and applying it effectively. From designing the hardware that powers AI to crafting high-level AI solutions for businesses, the AI knowledge stack is layered with increasing levels of leverage.

The higher up you are in the stack, the more impact you can have with less technical depth. Conversely, the deeper your understanding of the lower levels, the more capable you become at making high-level decisions.

For business owners and entrepreneurs, the key to success lies at the top of this stack—understanding where AI can be applied to optimize workflows and create value. Meanwhile, developers and specialists must dive deeper to master specific areas of AI, depending on their career goals.

Let’s break down the AI stack from the ground up, going up in levels of abstraction.

Level 1: Hardware Designers (GPUs & Specialised Chips)

At the very foundation of AI lies hardware. GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and other specialised chips are what make AI models possible in the first place. Companies like NVIDIA, AMD, and Google invest billions in developing faster, more efficient hardware to power the latest AI breakthroughs.

This level requires deep knowledge of semiconductor design, parallel computing, and electrical engineering. While essential, it’s a highly specialised area that most AI professionals don’t need to master.

Level 2: Model and Transformer Developers (Pre-Training Stage)

Once the hardware is in place, AI researchers and engineers design and pre-train foundational models like GPT, Llama, or Gemini. These models are built using enormous datasets and require immense computational power.

This field involves deep learning, neural network architecture, and large-scale distributed training. It’s where companies like OpenAI, Google DeepMind, and Anthropic operate, pushing the boundaries of AI capabilities.

Level 3: Model Fine-Tuning & Alignment Engineers

After a model is pre-trained, it needs to be fine-tuned and aligned to ensure safety, reliability, and usefulness. Fine-tuning involves training the model on more specific data, while alignment ensures that the AI behaves in a way that aligns with human values and business objectives.

This is where reinforcement learning and human-in-the-loop techniques come into play. It’s a crucial step in making AI models practical for real-world applications.

Level 4: AI Developers (API & Application Builders)

Once models are trained and fine-tuned, developers leverage them through APIs to build applications. This is where AI meets real-world business use cases. Developers integrate models into chatbots, customer service solutions, workflow automations, and more.

This level requires software development skills, API integration, and problem-solving to create AI-powered products and services. It’s where a lot of AI-powered SaaS businesses operate.

Level 5: Automation Tool Users (Make.com, Zapier, No-Code AI Tools)

Not all AI users are developers—some leverage automation platforms like Make.com and Zapier to build AI-powered workflows without writing code. These tools allow businesses to integrate AI into their operations seamlessly.

This level is great for business owners and non-technical professionals who want to enhance productivity without deep AI expertise. It provides leverage but limits customisation and innovation compared to lower levels.

Level 6: Prompt Engineers & AI Optimizers

Prompt engineering is the art of crafting inputs to get optimal outputs from AI models. This has become a specialised skill, with some professionals dedicating their entire careers to optimising AI responses.

Understanding AI’s capabilities and quirks allows prompt engineers to extract the best results from models, making them valuable in industries relying on AI-generated content, automation, and decision-making.

Level 7: AI Solution Architects & Business Strategists

At the top of the stack are AI solution architects and strategists—the people who understand what AI is good at, where it can be applied in a business, and how to design AI-powered workflows for maximum impact.

This is where the highest leverage exists. Business owners and executives don’t need to know how AI models are trained, but they need to understand what’s possible. Entrepreneurs and AI consultants (like myself) operate at this level, identifying AI opportunities and ensuring their successful implementation.

The Key Takeaways

1. Leverage increases as you go up the stack. The higher you are, the more impact you can have without deep technical expertise.
2. A deeper understanding of the lower levels makes you more effective at the top. Knowing how AI works under the hood enables better decision-making.
3. Business owners should focus on the top levels. Your job is to understand what AI can do and identify opportunities.
4. Developers should aim for the middle levels. This allows them to build and integrate AI-powered solutions.
5. Entrepreneurs and AI professionals should be fluent across multiple levels. This enables them to spot opportunities, validate them, and execute effectively.
6. Some may specialise in a single level. Becoming an expert in a niche, such as GPU design, model training, or prompt engineering, is another valid path.

Conclusion

Understanding the AI knowledge stack helps professionals and business owners position themselves strategically. The key is to operate at the level that maximises your leverage and impact.

As an entrepreneur and AI consultant, I have worked at all levels of this stack (some more than others), identifying where AI can optimise workflows, determining feasibility, and implementing solutions.

In this newsletter I will aim to cover all of the levels of this stack, making all of the posts accessible to both business owners, entrepreneurs, developers, students looking to improve their understanding of AI.