Last week, a prospective client called me and asked, "Would you describe yourself as a prompt engineer?"

I swallowed hard one time and then said: “Yes, of course.”

The client explained their software developer had hit a wall with a mail sorting algorithm. They needed someone who could think more flexibly about language and context.

The Power of Natural Language

The developer's traditional coding approach had achieved only a 65% success rate in sorting emails. Within my first session of prompt crafting, that number jumped to 92%.

This mirrors what NVIDIA CEO Jensen Huang recently emphasised at the World Government Summit: the future of computing lies in human language, not traditional coding syntax.

Why Traditional Coders Sometimes Struggle

The challenge wasn't technical complexity. It was handling ambiguity.

Consider how you might explain to a colleague the difference between "urgent" and "important" emails. Now imagine trying to code that distinction into strict rules. Like Jensen Huang says, "There is an artistry to prompt engineering. It's how you fine-tune the instructions to get exactly what you want."

Breaking Free from Binary Thinking

Last week, I led an AI training session for skilled technical workers who needed generative AI for their reporting tasks. These were smart, capable people who work with their hands all day. Yet when it came to instructing generative AI, they struggled—not because they lacked intelligence, but because their approach was too binary, too deterministic.

Years of working with technical and logical systems trains us to think in absolutes—clear inputs, predictable outputs, strict if-then relationships. This approach serves us well when dealing with mechanical systems and traditional programming.

LLMs operate on statistical patterns and probabilities rather than binary logic. While they're built on deterministic systems at their core, their multiple transformer layers create interactions that become increasingly fluid and nuanced at the surface level where we engage with them.

They respond to context and natural language patterns in ways that more closely resemble human communication than traditional programming.

Those who will excel in this new landscape aren't necessarily the ones who can construct the most rigid logical frameworks. Instead, they'll be those who can navigate the fluid spaces between disciplines—people who understand both technical precision and human nuance, who can work with probabilities rather than certainties.

It's not about abandoning logic—it's about expanding our approach to include the organic, the contextual, the holistic.

The Reality of Integration

For — what we laughingly call — the foreseeable future, there's still going to be significant demand for traditional coding expertise.

As Tim Kellogg notes in his recent analysis, AI engineering itself splits into distinct specialties:

Data pipeline engineers focus on building RAG architectures and managing company knowledge bases. UX-focused engineers craft the human-AI collaboration interfaces. Research-oriented engineers work on model integration and fine-tuning.

Each of these roles requires deep technical knowledge. The field isn't replacing traditional programming—it's creating new specialties that blend coding expertise with AI capabilities.

But The Backlash Begins

A recent thread on a popular tech forum, however, revealed the intensity of emotions this shift from coding to natural language is triggering among traditional techies. Their responses ranged from dismissive to outright hostile:

"Just be glad we got to live through the good decades, because the shitocalypse is here," wrote one forum user.

"These people are really too stupid to see how bad the quality of AI-generated code actually is," another one declared.

Yet these passionate responses miss a crucial point. The shift towards natural language programming isn't about replacing traditional coding—it's about expanding who can participate in building digital solutions.

Traditional developers will still be essential for complex systems, integration, and infrastructure - though they will need to embrace different ways of thinking about their coding problems.

But they'll be joined by a new wave of creators who can speak both to machines and humans. And that can only be a good thing.

Clear communication of intentions—whether to humans or machines—is becoming our universal programming language.

And that's something we all need to master.

A word about the format of this email

This newsletter is shorter than you're used to. The newsletters I enjoy most deliver just one clear story, one meaningful insight.

That's the format I'm switching to.

In previous newsletters, you had to slog through four different sections - but I'm not doing that anymore. It's honestly a lot to create and — if my own inbox weariness is anything to go by, too much for my readers to keep up with, as well.

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