Tipu Ake - Growing Upward

Tipu ake means to grow, to rise, to flourish naturally as of plants reaching toward light,

and of people developing their potential.

Growing Upward:

AI: A Natural Expression of Human Wisdom

From Myth to Reality

Mai i te Kōrero Pūrākau ki te Ao Tūturu

People tend to come to AI through fear first, “the rogue robot”, the machine that turns on its maker. But that story has always been more about us than about the technology.

AI grew out of something much quieter: human curiosity, the desire to understand how we think, and whether that thinking could be shared with something we built. It is not a replacement for human judgment. It is made from it.

Alan Turing’s question “can machines think?” was deceptively simple.

What came after took decades: clunky expert systems that helped doctors rule out diagnoses, neural networks that slowly learned to see patterns no human could hold in their head at once, and eventually language models that could write a coherent sentence. None of it happened in a flash. It was incremental, often unglamorous, and always dependent on the people building it.

There is nothing new about the idea that no single person holds all the answers. Māori have understood this for generations. Wisdom lives across whānau, hapū, and iwi, and good decisions come through kōrero and whānaungatanga, through talking and through relationship.

AI learns in a similar way, pulling from an enormous collective of human knowledge rather than any one mind. The problem is that collective has never been equal, and what gets left out shapes what comes back.

Today’s AI reflects the knowledge, biases, and aspirations of the societies that build it. Demystifying AI shifts our response from fear to stewardship: from passive observers to active shapers of technology’s trajectory.

Algorithms as Reflections of Wisdom

He Rorohiko, He Whakaaro Tangata

Algorithms are built on layers of human understanding: the questions their designers ask, the data they are fed, and the objectives they optimise. Every choice embedded in a model, from which variables to weight to which outcomes to prioritise, encodes human values.

Think of an algorithm as a mirror assembled from thousands of fragments of maths, cognitive science, cultural assumptions, ethical choices made by the people who built it.

The trouble is that no mirror assembled that way is going to be perfect.

• Where the data runs thin, the reflection distorts.

• Where the history feeding it was unequal, the bias comes back out the other side.

Admitting that is not a concession, it is actually where trust starts.

The concept of Mātauranga Māori reminds us that wisdom is not value neutral. Knowledge is always held in relationship: with land, with ancestors, with community.

When algorithms encode knowledge without acknowledging its origins and context, they risk stripping it of meaning.

Designing more inclusive algorithms means sitting with some uncomfortable questions: whose knowledge are we actually drawing on here, whose worldview is baked in, and who ends up better off … and who doesn’t.

Understanding algorithms as reflections of collective wisdom changes our relationship with AI. Rather than surrendering to opaque outputs, we become curators continuously interrogating, refining, and redirecting algorithmic reasoning toward equitable ends.

Setting the Stage for Collaboration

Te Tūāhuatanga o te Mahi Tahi

Throughout history, tools have extended human agency:  the wheel, the printing press, computing, remember dub-dub-dub or www?

AI fits naturally into this lineage while introducing something new: the capacity to learn and adapt. This raises the stakes for transparency and human oversight, not as bureaucratic burdens but as essential acts of stewardship.

Working with AI is not a switch you flip on or something you figure out as you go. People bring the goals, the context, the judgment about what actually matters. AI brings speed and the ability to hold far more information at once than any one person can. When those things work together, the results can genuinely surprise you. But it takes some patience to get there, and a willingness to stay in the loop rather than hand things over completely.

Kaitiakitanga, guardianship, is needed here. A kaitiaki does not own what they protect. They care for it, make decisions on its behalf, and hold it in trust for whoever comes next.

That is exactly the kind of relationship we need with AI. Not ownership, not fear, not blind deference, but genuine responsibility for how it is used, what it carries forward, and who it serves.

Vyvienne Kyle 2026