At our final pitch presentations this semester, after hours of decks, metrics, and AI-powered solutions, one of the jury members, an investor, said something that stuck.

He didn’t talk about market sizing or defensibility. He didn’t comment on technical execution or AI implementation.

Instead, he changed the conversation. He asked us to treat learning how to be compassionate, curious, and genuinely interested in the people we build for as just as important as learning about AI, data, and financial models.

It resonated.

Lately, it feels like the tech and startup world is becoming obsessed with enabling everyone to create. I keep wondering what it would look like if we became just as obsessed with understanding the people on the other side.

When Creation Gets Easy..

Yes, AI has made creation dramatically easier. Writing, designing, building, presenting – almost everything can now be done faster and with very little friction. If you believe some of the voices in the ecosystem, entire products can be sketched, branded, and marketed in a day.

As creation becomes easier, what we value starts to shift. When creating is hard, effort itself signals meaning. When creating becomes easy, volume begins to stand in for it.

That’s why the default answer to the question “what should we get good at?” so often becomes speed.

More output.

Faster iterations.

“Shipping faster”, as they like to call it.

The promise is higher quality at scale.

The reality feels like higher quantity at scale. More Features, more Products, more Data.

.. Speed Becomes the Wrong Signal

In a world where creating gets cheap, speed becomes the signal everyone optimises for. But speed alone ignores the human factor.

And that’s exactly when it stops being the advantage.

To understand why, I keep coming back to a simple comparison.

Think about the difference between a doctor who actually listens and one who’s already typing your prescription before you finish talking. Or a nutritionist who asks about your life – your schedule, your stress, what you actually enjoy eating – versus one who hands you a generic meal plan.

The technical knowledge might be identical. But one feels like it was made for you. The other feels like you’re expected to adapt to it.

That difference is understanding.

Understanding what to look for in the first place. Recognising how people actually think and work, not just what they say. Noticing what they’ve learned to live with because changing it feels too costly.

Coming back to a doctors appointment. Think of a doctor who hears “stomach pain” and prescribes antibiotics by default, versus one who asks two more questions and realises the problem is stress and skipped meals.

Same knowledge. Different outcome. Different feeling.

And increasingly, that difference matters.

Understanding as the New Bottleneck

As AI is making creation almost frictionless, we’re surrounded by more output than ever.

But attention hasn’t followed. Time hasn’t expanded. If anything, it is becoming scarcer. People still move through the world with finite energy, curiosity, and patience. They don’t wake up hoping to consume more information. They wake up deciding what can safely be ignored.

This is the quiet contradiction of the moment: we’re celebrating tools that allow us to say more, while living among people who can barely absorb what’s already being said.

That’s what I mean when I say the bottleneck has shifted.

It’s no longer about whether we can create something (→ more features, more products, more data). It’s about whether what we build actually connects to the needs and realities of the people we’re building for.

What Happens When Understanding Is Missing

When tools get better, they amplify whatever understanding is already there. If the foundation is strong, AI helps you express that understanding more clearly and more quickly. If it isn’t, it helps you scale confusion – often at remarkable speed.

That’s why so much AI-generated output feels hollow. It knows how to speak, but not what it’s speaking into. It has syntax, but no stakes.

It shows up in products, companies, and solutions that are technically impressive but don’t quite solve the problem they claim to address – things that are “personalised” only in the sense that they display personalised data, without meaningfully changing the experience.

The result: a growing hunger for things that show care. Real-world experiences. Conversations that aren’t optimized. Solutions grounded in actual problems rather than abstract metrics. Products that feel personal, not just personalised.

This doesn’t feel like a rejection of technology. It feels like a correction.

A shift back toward things that clearly took someone the time to understand a real context before building.

In a landscape full of fluent output, that kind of attentiveness stands out.

That helps explain why certain approaches are resonating more right now.

Building in public. Showing the process. Letting people see the trade-offs, the uncertainty, the thinking before the conclusion. It shows that something wasn’t created simply because it could be. It signals care. It also explains why founder or employee led communication works so well. Why product-led growth succeeds when it actually solves a problem people already have.

Without real understanding, even the most polished output collapses into noise.

But with it, the tools become powerful.

I want to understand how to understand better

If anyone can build, then building alone is no longer the differentiator.

If anyone can create, then creation itself is no longer the moat.

What becomes more important than ever is the work that comes before building: understanding.

Understanding what it means to be tired, overwhelmed, or stretched thin. Understanding why something that looks small on paper can feel enormous in practice.

It is a skill that doesn’t come from looking at dashboards alone. It requires attentiveness – looking at the data, yes, but also knowing which data matters, recognising patterns, testing assumptions, and staying open to being wrong.

And this is what makes the moment exciting.

AI is making the work of understanding people more visible, more necessary, and more valuable. As agent-based systems take on more execution and technical barriers keep falling, the skills that remain scarce are deeply human. They are the ones machines can’t replicate (yet).

The ability to listen carefully.

To ask better questions.

To understand context deeply enough to know what’s worth building in the first place.

That’s the difference between a prescription typed before you finish talking and a doctor who listens. Between a generic meal plan and a nutritionist who actually gets your life.

AI is changing how we build.

Creating has never been easier.

Understanding what’s making things meaningfull? More important than ever.

When Anyone Can Create, that’s Left to Learn.