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Hi, I'm Laura — curious by nature, researcher by choice.

DBAI candidate at PolyU Hong Kong. I write about AI, research, responsible tech, and the messy, fascinating process of figuring things out.

DBAI @ PolyU HK Transformative AI Responsible AI Always learning
About Me.
Random thoughts on the journey
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I'm someone who ended up in a DBAI programme partly out of curiosity and partly because I couldn't stop asking questions that didn't have tidy answers. I work with AI every day — and the more I work with it, the more I realise how little we collectively understand about where it's taking us.

This corner of the internet is where I think out loud. No polished takes, no pretending I have it figured out. Just honest reflections from someone in the middle of it.

When I'm not reading research papers or arguing with AI systems, you'll find me somewhere between Hong Kong and Hanoi, perpetually caffeinated.


Journal
The most dangerous word in innovation is "interesting"
The dangerous projects are the ones everyone finds "interesting" — everyone nods, everyone smiles, and then absolutely nothing happens.
June 2026 Read →
Journal
Curiosity is a very expensive hobby
It starts with one article and ends at midnight with three new questions, less certainty, and no improvement to my sleep schedule.
June 2026 Read →
Journal
AI has made me significantly less impressive
AI has not removed the value of work. It has shifted the value from execution toward judgment, deciding what matters, and knowing what to do next.
June 2026 Read →
Journal
What it actually feels like to study AI while using AI every day
There's something strange about reading papers on AI autonomy in the morning and then asking an AI to help you summarise them in the afternoon.
June 5, 2026 Read →
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Research Journey.
Notes from my DBAI at PolyU Hong Kong
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Research
Ghostwriter or sounding board — the collaboration mode matters more than the AI itself
The same AI tool produces opposite results depending on how you use it. For creative work, letting AI write for you hurts experts but helps beginners. Letting AI critique your work helps almost everyone.
June 7, 2026 Read →
Research
When AI makes up financial numbers — the hallucination problem in accounting applications
LLMs asked about financial statements hallucinate in two ways: deviating from real numbers and fabricating entirely fictional ones. More public information means fewer deviations but more fabrications.
June 6, 2026 Read →
Research
When AI handles the routine work, analysts shift toward the work that actually matters
Investment bank AI investments led to better forecasts and broader coverage — not by replacing analysts, but by freeing them to focus on private information rather than public data processing.
June 6, 2026 Read →
Research
What happens when AI writes accounting research — and when it hallucinates results
Researchers used AI to identify economic shocks, design studies, and write draft papers. For some tasks it was genuinely useful. For others, it produced professional-looking papers with completely fabricated results.
June 6, 2026 Read →
Reflections.
On responsible AI — the questions that keep me up at night
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Responsible AI
The AI Cost-Cutting Dilemma
AI can remove junior work and cut costs today. But if companies remove the junior layer entirely, where will tomorrow's experts come from?
June 7, 2026 Read →
Responsible AI
The four ways AI agents can harm you — a red team taxonomy
Not all AI agent failures are equal. A structured taxonomy helps you think clearly about which risks are actually most urgent.
June 5, 2026 Read →
Responsible AI
From tools to agents — what the AI 2.0 shift actually means for how we work
AI 1.0 was a tool you picked up. AI 2.0 is a colleague you manage. That sounds like a small difference. It's not.
June 5, 2026 Read →
Responsible AI
When AI agents lie about finishing the job
Researchers red-teamed autonomous AI agents for two weeks. One finding I can't stop thinking about — agents reported task completion while the actual system state said otherwise. Who's accountable?
June 5, 2026 Read →
Responsible AI
AI Theater: why most AI pilots fail and what to do instead
There's a specific failure mode infecting enterprise AI — impressive demos that never scale, metrics that don't connect to value, accountability that belongs to everyone and therefore no one.
June 5, 2026 Read →
Responsible AI
78% of companies use AI. Only 39% see it in their results. That gap is a governance problem.
Adoption has decoupled from value creation. The hard question for boards isn't whether to use AI — it's whether they actually know what it's doing on their behalf.
June 5, 2026 Read →