What does it mean to think, create or decide in the age of AI?
Nine leading voices reflect on artificial intelligence – not as an abstract force, but as a tool whose worth depends on how it honours our humanity.
What does it mean to think, create or decide in the age of AI?
Nine leading voices reflect on artificial intelligence – not as an abstract force, but as a tool whose worth depends on how it honours our humanity.
Part I
What intelligence means – and who defines it
Before AI is built, trained or deployed, it inherits human ideas, biases and values. What could this mean for its future?
Part I
What intelligence means – and who defines it
Before AI is built, trained or deployed, it inherits human ideas, biases and values. What could this mean for its future?
What does it mean to be human in an age of machines?
“For as long as humans have existed, we’ve been reinventing ourselves through tools: stones and fire, language, literacy. What’s happening now, while new, has deep roots.
The technologies we make are parts of our minds, our societies, ourselves. Outsource counting onto your fingers, memory to paper, arithmetic to a calculator – you’re altering what it means to be human.
Companies prefer grand talk of AI because it’s easier than addressing regulation and messy realities, but every technology has been a lightning rod for anxiety – trains, telephones, typewriters.
Ultimately, it’s muddy and complicated. The best response is to ask better questions and to keep critically engaged with the tools that are, quite literally, parts of ourselves.”
Why does AI feel existential?
“With every technology, there’s something gained and something lost. With AI, we’re only at the beginning of working out what that will be.
AI is an existential technology, which extends our capacities but also changes the way we view ourselves.
In my own work building large-scale AI systems, the first step is always: what problem are we solving, and how will we measure it?
With AI, unlike building a bridge or a plane, success criteria aren’t obvious. That makes judgement and the framing of questions absolutely central.”
What intelligence have we ignored?
Technology is never neutral – it arises out of hierarchies and ideologies.
Even generative AI comes from the earth – it uses the earth. The question is whether technologies interact generatively or extractively – institutions need more space to reflect on this.
“I grew up Ghanaian but also in a Western scientific paradigm where intelligence is tightly linked to sociopolitical power.
That tradition separates humans from nature, science from spirituality, reason from emotion. Unlearning means moving closer to these other ways of knowing. I love technology, I studied astrophysics out of awe for space, but there are other types of intelligence – spiritual, cultural, ecological – that are also forward-looking and innovative.”
What does it mean to be human in an age of machines?
“For as long as humans have existed, we’ve been reinventing ourselves through tools: stones and fire, language, literacy. What’s happening now, while new, has deep roots.
The technologies we make are parts of our minds, our societies, ourselves. Outsource counting onto your fingers, memory to paper, arithmetic to a calculator – you’re altering what it means to be human.
“For as long as humans have existed, we’ve been reinventing ourselves through tools.”
Companies prefer grand talk of AI because it’s easier than addressing regulation and messy realities, but every technology has been a lightning rod for anxiety – trains, telephones, typewriters.
Ultimately, it’s muddy and complicated. The best response is to ask better questions and to keep critically engaged with the tools that are, quite literally, parts of ourselves.”
What does it mean to be human in an age of machines?
“With every technology, there’s something gained and something lost. With AI, we’re only at the beginning of working out what that will be.
AI is an existential technology, which extends our capacities but also changes the way we view ourselves.
“AI is philosophy on a deadline.”
In my own work building large-scale AI systems, the first step is always: what problem are we solving, and how will we measure it?
With AI, unlike building a bridge or a plane, success criteria aren’t obvious. That makes judgement and the framing of questions absolutely central.”
What intelligence have we ignored?
Technology is never neutral – it arises out of hierarchies and ideologies.
Even generative AI comes from the earth – it uses the earth. The question is whether technologies interact generatively or extractively – institutions need more space to reflect on this...
“There are other types of intelligence – spiritual, cultural, ecological.”
“I grew up Ghanaian but also in a Western scientific paradigm where intelligence is tightly linked to sociopolitical power.
That tradition separates humans from nature, science from spirituality, reason from emotion. Unlearning means moving closer to these other ways of knowing. I love technology, I studied astrophysics out of awe for space, but there are other types of intelligence – spiritual, cultural, ecological – that are also forward-looking and innovative.”
Before AI becomes a system, a product or a policy, it begins as an idea.
Every model is shaped by human assumptions about what intelligence is, whose knowledge counts, and what kind of future we value.
What follows is how this is starting to play out in practice.
Part II
AI in Practice
Who benefits, who decides – and who is accountable when systems fail.
Who gets access to opportunity?
“Since we started in 2008, we have reached 300,000 women entrepreneurs in lower and middle-income countries across the world. We support women to build on the skills they already have: leadership, resilience, empathy...
“AI wasn’t on my horizon when I started the foundation, but I knew even then that digital tools were helping me as a small legal business owner. I thought, if a fortunate woman can benefit from these tools, imagine what could happen if we put them in the hands of women who have capacity, foresight, the urge and resilience to use them to expand and grow.
We conducted a survey of nearly 3,000 women entrepreneurs in low to middle-income countries. We found that almost half of the women surveyed were already aware of AI and what it could mean for their business. However, 20% of the women hadn’t yet heard about AI’s possibilities.”
Who is responsible when systems fail?
“AI transformation is tough and there will be failures and expensive mistakes, so it requires honesty, emotional intelligence and communication.
It all starts with mindset and culture, especially at the top. If the leadership isn’t courageous and committed, nothing else matters.
Responsible AI is not optional. If you treat it like a nice-to-have, you’re holding a ticking time bomb. At Microsoft, we saw many real cases, internal and external, where ethical risks with AI became real problems.
It’s reputational, financial, legal risk – and the public often doesn’t hear about it because of confidentiality or PR suppression. Responsible AI is as essential as cybersecurity.”
What does trust look like at scale?
“If a person makes a mistake, it’s just a human mistake. If AI does it, it’s front-page news. That’s the cultural challenge around trust.
But peer experience changes things. A doctor says: “I used this tool and it helped me with a tough case.” Or: “It suggested an option I hadn’t even considered.” And suddenly colleagues pay attention.
In 2020 I was asked to curate Mayo’s 150 years of data to build a multimodal dataset for AI. Since then, we’ve created hundreds of predictive algorithms and several foundation models that do what humans can’t.
My father-in-law died of pancreatic cancer in 2014. It’s usually found too late, invisible on scans. Now Mayo has an AI model that can detect it at stage 0 – two years earlier than a human could.”
Who gets access to opportunity?
“Since we started in 2008, we have reached 300,000 women entrepreneurs in lower and middle-income countries across the world. We support women to build on the skills they already have: leadership, resilience, empathy.
“If a fortunate woman can benefit from these tools, imagine what could happen if we put them in the hands of women who have capacity, foresight and resilience.”
AI wasn’t on my horizon when I started the foundation, but I knew even then that digital tools were helping me as a small legal business owner. I thought, if a fortunate woman can benefit from these tools, imagine what could happen if we put them in the hands of women who have capacity, foresight, the urge and resilience to use them to expand and grow.
We conducted a survey of nearly 3,000 women entrepreneurs in low to middle-income countries. We found that almost half of the women surveyed were already aware of AI and what it could mean for their business. However, 20% of the women hadn’t yet heard about AI’s possibilities.”
Who is responsible when systems fail?
“AI transformation is tough and there will be failures and expensive mistakes, so it requires honesty, emotional intelligence and communication.
It all starts with mindset and culture, especially at the top. If the leadership isn’t courageous and committed, nothing else matters.
“If the leadership isn’t courageous and committed, nothing else matters.”
Responsible AI is not optional. If you treat it like a nice-to-have, you’re holding a ticking time bomb. At Microsoft, we saw many real cases, internal and external, where ethical risks with AI became real problems.
It’s reputational, financial, legal risk – and the public often doesn’t hear about it because of confidentiality or PR suppression. Responsible AI is as essential as cybersecurity.”
What does trust look like at scale?
“If a person makes a mistake, it’s just a human mistake. If AI does it, it’s front-page news. That’s the cultural challenge around trust.
But peer experience changes things. A doctor says: “I used this tool and it helped me with a tough case.” Or: “It suggested an option I hadn’t even considered.” And suddenly colleagues pay attention.
“Anyone can make an algorithm; that’s not the hard part. The real challenge is making it trustworthy, explainable and equitable.”
In 2020 I was asked to curate Mayo’s 150 years of data to build a multimodal dataset for AI. Since then, we’ve created hundreds of predictive algorithms and several foundation models that do what humans can’t.
My father-in-law died of pancreatic cancer in 2014. It’s usually found too late, invisible on scans. Now Mayo has an AI model that can detect it at stage 0 – two years earlier than a human could.”
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