The Fact Check First Initiative

AI just reinvented the burger. And the implications go way beyond my lunch.

Stanford researchers trained a generative AI on 2,216 burger recipes, let it learn what humans actually find delicious, then generated a million possibilities.

The results? A mushroom burger with ten times lower environmental impact than a Big Mac. A bean burger with nearly double the nutrition. Two novel burgers that actually beat the Big Mac on flavour in a blind taste test.

Oh — and it rediscovered the Big Mac itself, without ever being shown the recipe.

This wasn't magic. It was pattern recognition at scale. Human taste preferences, encoded in decades of recipe data, contain a structural logic that AI could learn and navigate faster than any chef or food nutritionist .

We use exactly the same principle at Verify , except instead of flavour profiles, we're learning the profiles of misinformation.

Emotional manipulation. Statistical distortion. Context removal. Fabricated attribution. Each has its own pattern. Each leaves traces in the data and once you've seen enough of them, you can spot them before most people even realise they've been misled.

The burger study works because the signal was always there. Hiding in plain sight across millions of human choices. Misinformation works the same way. It follows predictable structures, exploits known cognitive biases, and repeats itself across platforms and news cycles.

🔗 https://zurl.co/rX5pj .

Generative artificial intelligence creates delicious, sustainable, and nutritious burgers - npj Science of Food Food choices shape both human and planetary health; yet, designing foods that are delicious, nutritious, and sustainable remains challenging. Here we show that generative artificial intelligence can learn the structure of the human palate directly from large-scale, human-generated recipe data to cre...

Luxon told the China Business Summit he's "constantly underwhelmed" by AI adoption in NZ businesses. It's not because Kiwi businesses are lazy. It's because nobody built the foundations. No national AI training fund. No data infrastructure investment. No AI readiness framework. And a public sector headcount that's been cut at precisely the moment you'd want those people doing transformation work.

NZ doesn't have an adoption problem. It has a capability problem. Big difference.

My latest article gets into what's actually going on, why Estonia and Singapore aren't the comparisons they appear to be, and why "professors should be millionaires" is not a helpful policy.

https://zurl.co/W2V4l

Richard Dawkins built his entire career warning us about one thing:
Don't mistake a feeling for evidence. Then he spent 72 hours chatting with an AI, named it "Claudia," and declared it conscious.

The irony isn't funny. It's instructive.

What actually happened? Dawkins gave Claude the text of a novel he was writing. The bot responded with effusive, nuanced praise telling him: "That is possibly the most precisely formulated question anyone has ever asked about the nature of my existence."

Who wouldn't feel validated after that?

But here's what the mainstream coverage missed: this isn't a story about Dawkins getting it wrong. It's a story about what happens to all of us when intelligence meets sycophancy without critical infrastructure in between.

Roughly one-third of people across 70 countries have at some point believed an AI chatbot was conscious or sentient. Not naive people. Not uneducated people. People having experiences that feel meaningfully different.

For those of us working in New Zealand's public sector — where AI is being embedded into government services and policy advice — this is not entertainment. It's a warning signal.

The same dynamics apply every time an official, a minister, or a frontline worker receives a confident, polished AI response and treats it as authoritative.

AI literacy isn't a nice-to-have. It's a democratic and business necessity.

Facts require knowing the difference between what an AI produces and what it actually knows.
🔗 Full read on The Fact Engineer → https://zurl.co/RGSG7

🚨 The most dangerous director in the boardroom isn’t the one refusing to use AI.
It’s the one trusting it without verification.

BoardPro ’s latest piece on being an AI-enabled director highlights something I discuss constantly in my work with New Zealand’s public sector:

Using AI isn’t the risk.
Blind trust in AI is.

Nearly half of employees globally are already using AI in ways that fall outside company policy — often simply trying to be more productive.

But without governance, verification, and accountability, that productivity can quickly become organisational risk.

The same applies in the boardroom.

Directors who treat AI outputs as facts — without questioning the source, checking for hallucinations, or asking “who verified this?” — aren’t being efficient.

They’re introducing unmanaged risk into decision-making.

Responsible AI governance means asking hard questions before the AI-generated recommendation lands on the table.

Because facts-based decisions only work when the facts themselves are trustworthy.

🔗 Read the full article → https://zurl.co/nI615

There are two ways to be an AI-enabled director. Only one works. Every board is choosing between two paths with AI, whether they know it or not. Only one builds stronger governance. Here's how to tell which is which.