AI Is Like Fire - And We’re Just Learning to Build the Fireplace

Welcome to week 36 of my journey into a cybernetic life. This week I discovered a growing tension in the air — even Sam Altman recently said the AI bubble might burst soon. And he’s not wrong. We’re living in a time of massive expectations. Billions are flowing into AI startups, especially those that promise to disrupt entire industries. If your pitch includes phrases like “agentic software generation” or “infinite codebase management,” you’re likely to walk away with funding.

And yet, when you look at real productivity gains in enterprise settings — they’re... marginal at best. Sure, people write emails faster. Devs scaffold code more efficiently. But have our companies become radically more productive? Not yet. The enthusiasm is there — but so is the skepticism. Investors are starting to ask hard questions. And we’ve seen this story before — in the dot-com bubble, and later in crypto. When excitement outpaces real value, corrections are inevitable.

AI Is Like Fire – and We’re Just Learning to Build the Fireplace

What I observe:

The models are good enough. What we lack are the agentic processes and applications that make the power of AI predictable and controllable.

AI today reminds me of early fire in the stone age. We’ve seen the spark — and we know it’s powerful. But right now, it’s still dangerous. It hallucinates, bloats communications, contradicts itself. Sometimes you need another AI just to clean up the mess.

Just like fire needed centuries to move from basic heat to precision industry tools — AI needs process to become reliably useful.

And the numbers support this. According to a 2024 BCG report, only 19% of enterprises report measurable business impact from their current AI deployments — and most of that is concentrated in isolated, task-specific use cases like document summarization or customer service automation. Broad, transformative impact remains elusive.

Too Much Prompt, Not Enough Process

Right now, we’re using AI in a mainly unstructured way. We throw long prompts into a black box and hope something good comes out. We celebrate vibe coding and get surprised when results fall apart under complexity. But here’s the truth:

• On a micro level, AI is already excellent: writing emails, suggesting code snippets, completing functions.
• On a macro level, building complex systems, documents, or decisions it breaks without guidance.

This reflects a broader issue: prompting is not yet a discipline. It lacks composability, traceability, and reviewability — the things that made software engineering scalable in the first place. As LLMs grow in capability, our ability to orchestrate them meaningfully becomes the next limiting factor.

To succeed here, we need modularity: modular prompts, modular context, modular review loops. In a recent paper from Stanford HAI, researchers found that task decomposition and structured prompting significantly improved LLM accuracy and alignment — reinforcing the need for engineered process rather than creative vibes.

My rule of thumb: Never type more than 10 lines into a prompt box. Start building reusable prompts and context blocks. Compose them. Chain them. Review the results with another prompt.

This is the difference between Vibecoding and Engineering with AI.

The Way Forward

We need to stop thinking of LLMs as magic black boxes and start treating them like APIs — deterministic systems that need well-defined inputs, constraints, and interfaces. We need to build scaffolding around them — not just to protect users from failure, but to empower experts to scale their intent.

There is a massive opportunity here, but it requires maturity:

• PromptOps to manage the lifecycle of prompts like we manage code
• Context layers to ground the model's behavior
• Agentic pipelines to decompose and execute complex goals
• Review loops and guardrails to catch misalignment
• Feedback mechanisms to drive improvement over time

We are still early in this transition. But if AI is to transform knowledge work, it will do so through structure, not spontaneity.

Conclusion

AI is the fire. But until we build the fireplace — a process that contains, shapes, and controls it — we won’t realize its true value.

The bubble may burst. But what will remain is a force we can finally wield — if we learn how to build with it.

And yes — even this newsletter was written using a modular prompt stack: tone, POV, structure, research, outline, and final-generation prompt. Nothing fancy. Just structured.

Until next week – stay structured. Christian