I was invited by Lynlee Foo on Friday, 22 May 2026, to her MoneyFM 89.3 radio program “Reset with Lynlee” and I shared my thoughts on the rapidly evolving AI situation in Singapore. Here’s the transcript, with light clean-up by Claude. In the MP4 video above, the auto-captioning was done by Canva’s AI. So as you can see, Gen AI is really handy for improving the delivery of content. However, the interview is still all human.
Lynlee Foo: Another major theme this week is AI and how quickly it’s entering everyday work. Between the Asia Tech x Singapore Summit, growing investment around AI technologies, and ongoing discussions globally around layoffs, productivity and workplace change, AI is becoming part of everyday working life much more quickly than many people expected — and not just in tech companies either. People are talking about AI in offices, schools, marketing teams, universities, even family chats.
Joining me now is Ian Tan, lecturer at NTU’s Wee Kim Wee School of Communication and Information. Ian’s work looks at AI-driven innovation with a focus on making complex concepts easy to understand. Ian, thanks for joining us.
Ian Tan: Hey, Lynlee. Thanks for having me.
Lynlee Foo: When you look at the AI conversation this week — the summit discussions, the headlines, reactions online — what stood out to you most?
Ian Tan: Well, I’m lost in the announcements, there are so many of them. Right now I’m actually looking at the Google AI summary of what was announced at ATxSG, the Asia Tech x Singapore Summit. There were OpenAI announcements — investing over $300 million. Then there were Google announcements with a brand-new national AI partnership, agentic AI for science, and many other areas.
We are now really ramping up in terms of how AI is permeating society, permeating institutions. The summit this week was a reflection of a pace that is increasing all the time. I last spoke to you three years ago, right?
Lynlee Foo: Yes, quite a while ago.
Ian Tan: ChatGPT had just launched its custom chatbots that people could create. Today we have advanced so much — those seem like primitive days compared to what we’re able to do with Gen AI today. This has both really good upsides and is also generating a lot of concerns across the board, and I think we can talk about that today.
Lynlee Foo: What’s changed is how quickly AI discussions have moved beyond the tech sector. We now hear people talking about ChatGPT in meetings and office discussions in a way we probably wouldn’t have even a year ago. Why do you think this shift happened so quickly?
Ian Tan: If we look at the official numbers, OpenAI’s ChatGPT has about 900 million users. Google’s Gemini also claims about 900 million users. There’s probably a huge overlap across the two apps, but we’re talking about nearly a billion people in the world actively using these top two Gen AI apps.
AI has become part of the daily workflow for many workers — whether they’re white-collar workers, blue-collar workers, or students. It’s become part of our daily routine: whenever we feel “I need to use AI,” we just use it.
The challenge is that as we are asked to embrace AI, what is the level of AI literacy for each individual? And how do we define AI literacy? These are big questions being tackled at many levels. As a lecturer at NTU, I have many questions about what teaching is like in this new age. What is learning? How do we impart skills when it seems that AI is taking away a lot of the need for thinking?
If you read the papers today, AI is on almost every headline. Sometimes it gets tiring to read, even for me, and you can’t escape from it. So first: you can’t escape the AI conversation. Second: there is pressure on companies to deploy AI, whether it’s self-inflicted or comes from somewhere else.
For example — this was over a year back — I received a request from a CIO of a company. They were rushing to get an AI workshop arranged, which I would conduct. Why? Because the CEO had come down on them and asked, “Why is there no AI training in our company?” The CEO had heard a lot about AI training, but nothing was happening in the company. So the team was scrambling to get a workshop together. This is quite common — I’ve seen it several times. People often start at the top in terms of the pressure.
At the bottom, if people are using AI, they may already have been using it for the past few years. They don’t necessarily feel they have to tell people, because they may be concerned: “What if people find out that I’m doing things very quickly now? Will I get more work given to me?” So we have all these things happening at multiple layers — whether you’re rank and file or leadership, people are using AI and seeing AI from different perspectives.
Lynlee Foo: Talking about pressure and keeping up — perhaps especially in Singapore, where there’s already a strong culture around staying employable and competitive — do you think people are putting pressure on themselves to keep up with AI?
Ian Tan: This is a very complicated situation. Most people understand the need to learn AI. However, they may not know what they should learn about AI. Let’s say you meet someone on the street, and they tell you, “You need to learn how to create your own AI solutions, and you need to know coding — you need to learn Python.” That’s one person’s point of view. Another person would say, “I know how to create chatbots in Copilot and Google Gemini, and I’m able to accelerate my work.” And recently we had a minister who shared that he’s used autonomous agents — with a technology called OpenClaw [sic] — to help him in his work.
So you hear all these different conversations and it gets very confusing. It can also be very scary: what do I need to learn? Having done AI training for the past few years, I’m very clear that everybody needs to first understand what this whole Gen AI thing is about — what it can do and what it cannot do — but also apply it to their work on a regular basis, daily or every other day, to understand how it applies to their work.
You may not need to know coding. This is something I’d like to tell the audience: it is not necessary to learn coding to have AI help you in many ways. If you do want to go into coding, you can build more complex AI solutions, but even then it takes time to learn. Not everybody has the attitude to learn coding either. I’m a coder — I’ve been trying to learn Python for years, and I’m still not very good at it.
Everybody just needs to know how things work, and then how it works for them. If you understand how this AI technology works, why it hallucinates, how it’s improving over time, and what different outputs and results it can give you — then you match that to your type of work or what you aspire to do. How can it help you? Observing how other people use AI and seeing if you can learn lessons from them — that’s what’s needed today. We need both foundational understanding and practical application. Then we will have a good grasp of how this technology is good for us.
Take another skill altogether — cooking. During the pandemic, many of us stayed home and tried to learn how to cook our own dishes. But once the pandemic ended, we all went back to the office, and we’d dapao our food. For most people, we didn’t need to continue cooking because the need was no longer there. We learned how to cook and how to do different things during the pandemic, and once the need was over, we moved on and maybe went back to old habits.
AI is similar. If you find it can help you in your work in certain ways, you’re going to continue using it. If you’ve learned AI and you find it’s just not really applicable to what you do — that’s fine too. You move on. Maybe one day it’ll be useful to you. This is where I’d say we need to negotiate with the technology, understand it better, and apply it in practical ways for ourselves.
Lynlee Foo: In terms of keeping up with AI, many people probably overestimate how far ahead everyone else already is — a bit of a FOMO thing. But the truth is, many organisations are still experimenting and learning, if I’m not mistaken.
Ian Tan: Yeah — and there’s both overestimation and underestimation. Some organisations feel they need to buy an AI solution right now and subscribe to a very expensive plan from a major provider. Then they realise that people may not know how to use the subscription they have bought, because they didn’t get enough relevant training to begin with. Some folks say, “We should only use free AI apps, because that’s good enough for my company,” and they don’t want to provide an AI training budget. I see all these differing perspectives everywhere, and there’s no right or wrong — except that sometimes there’s not enough testing of the AI applications to see where it’s relevant to themselves and where it should not be used, in order to have clarity in deploying AI in the workplace.
Lynlee Foo: At the same time, there’s a huge amount of AI messaging right now. Every company seems to be talking about AI integration, AI strategy, AI transformation. How difficult is it for ordinary people to tell the difference between genuine change and hype?
Ian Tan: Right now I’m helping a B2B company go through this transformation. It started when the boss brought me in and said, “I want to transform my leaders into AI users and AI champions.” I trained them, and it was challenging for some of them to understand what was going on — “Why am I coming here for training? What should I be able to do with this technology?” There was uncertainty, and sometimes fear that the technology could take over parts of their job that they consider vital to their job survival.
After the training was done, you see many eyes light up. First, they understand how the technology works and how they can apply it. Second, we always do exercises where I say, “Let’s look at a real work problem that you have. Pair up, discuss your work problems, and we’ll create prototype solutions.” They start applying it and say, “Ah, it can work in this way — and I tried this other way, it doesn’t work.”
This experimentation phase is the learning phase. If you think about it, all learning comes from experimentation. We often think learning is sitting down, reading a textbook, and then taking a test. That’s very old-school thinking. Today, we know better — learning is really trying it, hitting a wall, failing a bit, and then trying again. That’s the best way to learn. If people are not given the opportunity to try and maybe make some mistakes with AI within a safe space, then they may never truly learn what this whole AI thing is about.
Let me give you another example. I have an ex-colleague who called me up a few weeks ago and said, “Hey, Ian, I need some help. My company has been retrenching people, and now for those of us left — the survivors — they want us to produce a PowerPoint report to explain how we’re using AI in our work to become more useful, more productive, and so on.” She was panicking, because she said, “I had already been using AI for my work. I’ve been using AI for research, using it to verify certain information — and I’m not sure what else I need to show my company in order to save my job.”
It’s a long story I won’t go into, but I sat down and helped her think through her workflows and understand where else she could apply her company’s AI tools to her current job role. The other thing that was striking — she’d gone for AI training offered by the company, and she didn’t realise so many other things could be done. Perhaps the training was too technical, or wasn’t very relevant to her job role. She didn’t know quite a few things that Gen AI can do today that she could apply to her job. We had a conversation, things worked out, and she’s now better able to explain to her bosses how she uses AI in her work.
This presents a scenario — and I’m sure it’s happening in many companies — where people are now expected to explain how they are an AI user. That is very distressing, especially if you don’t know who to turn to, if you don’t have an AI coach you can confide in. We’re in a very challenging scenario where there’s pressure from all sides, optimism that AI can help change society for the better, and a lot of concern about AI’s impact on the environment. With all this mixed messaging, any ordinary person would just get confused or feel very lost — and they need a guiding hand.
Lynlee Foo: For many professionals, it’s definitely about adapting gradually rather than trying to reinvent themselves completely overnight.
Ian Tan: Yes — but gradual definitely takes time. I’m still learning AI every day. I started four years ago, and I’m still picking up new skills and trying new ways of using AI. My advice to everybody is: start right now, and do it in small doses. Some people who are more tech-savvy will pick it up faster; some people will need a bit more time. The most important thing is to just keep applying it to your work. Keep asking, “How can I improve my work?”
A lot of people think AI is simply for speeding up your work. I prefer the word improve. How can it give you better output — which you can evaluate and learn from, learning how AI thinks and works? For example, you could easily clean up a badly written essay today with AI and just submit it. The better way is to look at the mistakes you made, which AI pointed out and corrected, and work on improving your essay writing. The next time you write an essay — for school or any other scenario — you’re better than before. So we can use AI to help us improve, rather than to simply clean things up and send them off while we are no better.
Lynlee Foo: Talking about essays in school — you’re an educator at NTU, you teach. What are you hearing from your students about AI right now?
Ian Tan: I’m hearing a lot of different perspectives. Surprisingly, there are quite a few who are quite anti-AI because of its impact on the environment, and I understand why. There are those who are anti-AI but not in a thoughtful way, and I have to pull them aside and say, “Hey, this is the more thoughtful approach,” and teach them how to do it while retaining that critical thinking.
Then there are many for whom it’s become part of their learning toolkit. They use it regularly and become very comfortable with it, but they’re not going beyond using AI to help them study. You can use AI for many things — learning new skills, exploring the world, understanding a perspective that you disagree with. In my lessons and my interactions with students, I try to show them that we should use this as a resource, as a tool to improve our critical thinking, so we can ask better, deeper questions and get better output.
University, to me, is always a place of knowledge and learning. We call it higher education, but it’s really also about critical thinking. We come here to open our minds to new possibilities. The tool can be used for that — but if you choose to use it to offload your thinking and let the machine do the work for you, then you do a great disservice to yourself. What I try to do is demonstrate all the different ways students could use AI to improve their work, rather than offload their work.
Lynlee Foo: To wrap up, Ian — this is an important shift. While AI may still be evolving quickly, this week’s developments also showed that AI is no longer confined to the tech sector. It’s becoming part of how people work, study, communicate, and think about professional value in everyday life. Ian, thank you so much for your views today.
Ian Tan: Thank you, Lynlee.
Lynlee Foo: That was Ian Tan, lecturer at NTU’s Wee Kim Wee School of Communication and Information, and a generative AI coach. I’m your host, Lynlee.