From the Information Age to the Knowledge Age: One Age at a Time

TLDR;

• Quick questions and answers in bullet points are at the bottom. Scroll down and get your bite if you want more than just the TLDR.
• We're entering the Age of Knowledge: Specialized knowledge, data, and computing power are becoming the primary business differentiators before the Age of Intelligence.
• Value shifts from code to data: With AI and Large Language Models (LLMs) widely accessible, the real competitive edge lies in unique data and expertise, not software code.
• To become AI empowered: Focus on specialized knowledge, Explore Retrieval Augmented Generation (RAG), Leverage no-code platforms, Automate intangible processes.
• Embrace AI agents: Collaborate with AI agents that process information without traditional interfaces.
• Mindset shift is essential: Prioritize specialized knowledge and intelligent automation to stay competitive and lead the way into the Age of Intelligence.

For Readers

The Intelligence Age isn't here yet. Artificial General Intelligence (AGI) remains a future prospect. Before we reach that milestone, we're first entering the Age of Knowledge.

If everyone has access to the same Large Language Models (LLMs), then the only competitive differentiators will be specialized knowledge, data, or computing power. Frankly, there's one AI company landlord—NVIDIA—and everyone else is just renting space, including OpenAI, xAI, Facebook, Google, and Amazon.

Most AI companies are simply wrapping LLMs. Successful AI companies in vertical spaces like Legal, HR, and CRM are capitalizing on specialized knowledge, processes, and data that they have access to—but others don't. The actual value isn't in the code; it's in the prompts and the data. I believe the value of software is diminishing because eventually, the only thing that will matter is the core knowledge.

In software, it comes down to specialized knowledge stored in the designs and schemas of structures, processes, their own data, and how they've connected everything together. The rest can be inferred by AI and built out in predictable ways or through no-code platforms easily customized by humans.

What Does This Mean?

It means we need to focus on the knowledge. LLMs will keep getting smarter. The tools to build things will keep getting better—or disappear altogether into ambient AI.

Entering the Age of Knowledge

Generative AI is revolutionizing our perception of what's feasible to automate. It's not merely about chatbots or virtual assistants—we don't need another RAGbot demo with yet another database; that problem has been solved. It's about automating the step-by-step tasks in business processes that once required years of expertise and training—especially in knowledge work and management and connecting these processes with existing systems that do what they do well.

The Unchanging Core Principles

But what's truly important hasn't changed. The core principles of leadership and management remain vital:

How do we organize people to execute routine and non-routine tasks, both tangible and intangible, that require specialized knowledge or data?

The Future: Organizing People and AI Agents

Traditionally, we've relied on user interfaces for people, software to codify processes, databases to store information, and specialized systems for specific functions. But here's the future:

How do we organize people and AI agents to execute these tasks using specialized knowledge or data stored in our information systems?

Unlike humans, AI agents don't need user interfaces. They can absorb information from a variety of sources. Historically, routine tangible processes were codified into code or no-code manifests, while intangible processes relied on human expertise. Now, any information-heavy process—whether routine, non-routine, tangible, or intangible—can be automated.

Harnessing Your Data with RAG

While the public internet helped build LLMs, providing them with vast vocabularies, their true power emerges when we feed them our own data. This is the essence of Retrieval Augmented Generation (RAG). When LLMs are provided with your specific information, they don't "hallucinate"; they use exactly what you give them.

Imagine if every file, image, or document you have was stored in a "vector" format—the way LLMs understand data. You could intelligently ask questions and get precise answers from your own information. Agents take this a step further by breaking complex requests into smaller tasks, using RAG to find relevant answers or perform actions like searching the internet.

The Evolution of Programming

So, why do we still need programming or sequencing steps? Because that's what makes each business unique—it's how we approach problems, our trade secrets, our specialized knowledge. Predictable and routine processes are the foundation of society; they allow for consistent and reliable outcomes.

Programming is evolving. From the early days of Visual Basic to modern no-code platforms like Bubble or tools like Zapier and Airtable, we're moving toward using visual building blocks to create complex, customized processes. This commoditizes the "boring" code programmers used to write—saving data, displaying data, and so on.

And when you insert AI into the mix, we can make these routine processes intelligent. This is the magic happening now, and it's only becoming more magical.

Quick Questions, Quick Answers

What Is the Age of Knowledge?

The Age of Knowledge is a period where specialized knowledge, data, and computing power become the primary differentiators in business.

• LLMs Are Commonplace: With Large Language Models (LLMs) becoming widely accessible, the competitive edge shifts to who has the most relevant and specialized information.
• Specialized Knowledge Matters: Companies that leverage unique data and expertise will outperform those relying solely on general AI models.

Why Focus on Specialized Knowledge Over Software?

Is software losing its value in the AI era?

• Diminishing Software Value: The real value isn't in the code anymore; it's in the prompts and the data fed into AI systems.
• AI Can Infer and Build: AI can now predictably build out processes and structures, reducing the need for traditional coding.
• No-Code Platforms Rise: Tools like Bubble, Zapier, and Airtable allow for easy customization without deep programming knowledge. Add AI to the mix, and it is magic.

How Is Generative AI Changing Business Automation?

Can AI automate tasks that once required years of expertise?

• Beyond Chatbots: Generative AI isn't just about virtual assistants; it's about automating complex business processes. Think ambient processes that continuously process information to help people act quickly and focus on key decisions and strategy.
• Automating Expertise: Tasks that required specialized knowledge and training are now within AI's capabilities. And if they aren't already the means to create fine tuned LLMs or SLMs (small language models) is very accessible.
• Information-Heavy Processes: Any process involving significant information—routine or non-routine—can be automated.

What Is Retrieval Augmented Generation (RAG) and Why Is It Important?

How does feeding your own data into AI models enhance their performance?

• Eliminating Hallucinations: By providing AI with your specific data, you reduce inaccuracies and irrelevant outputs.
• Personalized AI Responses: RAG allows AI to use your data, giving you precise answers tailored to your needs.
• Agents and RAG: AI agents can break down complex tasks and use RAG to find relevant information or perform actions like internet searches.

I really wish there was a better acronym. Along side my pile of words I don't like but have to use...

Do We Still Need Programming in the Age of AI?

With AI's advancements, is programming still necessary?

• Uniqueness Through Programming: Programming and process sequencing make your business unique and protect trade secrets.
• Foundation of Society: Predictable and routine processes are essential for consistent and reliable outcomes.
• Evolution of Programming: The shift is toward visual building blocks and no-code platforms, commoditizing routine coding tasks.

How can you become AI Empowered today? Empower your AI.

What practical steps can you take to leverage AI in your business or personal life?

1. Focus on Specialized Knowledge: Invest in building and curating the unique knowledge and data that set your business apart.
2. Explore RAG (Retrieval Augmented Generation): Start experimenting with feeding your own data into AI models to get accurate and relevant outputs.
3. Leverage No-Code Platforms: Use tools that allow you to build intelligent systems without deep programming knowledge.
4. Automate Intangible Processes: Consider how AI can handle complex, non-routine tasks that were previously the domain of experts.
5. Embrace AI Agents: Think of AI agents as collaborators that can absorb and process information without traditional interfaces.

Why Is This Transition Important?

What does moving into the Age of Knowledge mean for the future of business and technology?

• Staying Competitive: Businesses that adapt will lead the way into the Age of Intelligence. They will come to the table with knowledge that only they have.
• Innovation Opportunities: Focusing on knowledge allows for innovative solutions and services. Everyone worth their weight knows knowledge management changes human companies, it's as if not more effective for human + AI companies.
• Efficiency Gains: Automating processes frees up resources for strategic initiatives. Let the humans create and practice becoming more than just information processors.

As someone deeply involved in the design and architecture of intelligent business platforms and systems—what I call Intelcraft—I've witnessed firsthand how these shifts are reshaping industries. The Age of Knowledge is here, and those who adapt will lead the way into the Age of Intelligence.

Are you ready to get all your [****] together so that AI can finally be useful for you?

At Anant Corporation , we have been building and scaling business platforms. Today, we build intelligent business platforms by organizing, curating, and creating specialized Playbooks around NoCode, Data, and AI with teams that love to use AI to make ideas happen, fast.