Q: എച്ച് ആർ മേഖലയിൽ സജീവമായിരിക്കെ താങ്കൾ പെട്ടെന്ന് ഒരു പാൻ ഇന്ത്യൻ AI speaker ആയി സ്വയം groom ചെയ്തിരിക്കുന്നു. ഒരു ക്വാണ്ടം leap എന്ന് പറയുന്നതാവും ശരി. അല്ലേ?
A: December 2022 was a shock. I opened ChatGPT the way you open a toy, but it turned out to be more like opening a new continent. I wasn’t “using tools”; I was staring at the tectonic plates of work shifting under my feet. Civilizational shifts don’t announce themselves, they creep in, and then one morning you realize nothing will be the same again.
The models were growing at a speed I’d never seen in two decades of watching technology. Faster than mobile. Faster than social. The only way to learn at that pace was to throw myself into the fire: speaking about AI, not after I mastered it, but because I hadn’t. Real feedback is the fastest feedback.
That experiment snowballed. 100+ organizations, 30,000+ leaders and professionals. Every talk was a mirror, showing me what I didn’t yet know. And that’s the point: I’m still a learner. A ‘pro-learner’ isn’t the one who knows the most, but the one obsessed with making the messy suddenly obvious. That’s the real work, turning AI’s complexity into clarity, not just for others, but for myself first.
Q: സാങ്കേതികമായി അടിസ്ഥാന തലത്തിൽ, അല്ലെങ്കിൽ peripheral വിജ്ഞാനപരിധിയിൽ നിൽക്കുന്നവർ ക്കുവേണ്ടി generative AI എന്നതിന്റെ ഒരു ചെറു വിവരണം നൽകാമോ?
A: Generative AI sounds technical, but at its core it’s very simple: it makes things. Essays, images, videos, music, code, voices, even cleaned-up datasets. For the first time, we have a machine that doesn’t just calculate, it creates.
What makes this different from the old AI is not just the power, but the distribution. This isn’t locked away in research labs or giant companies. It’s on your laptop, in your browser. You can ask it to do work today, get paid for that work tomorrow, and the loop is closed.
That changes the economics of time. If something that used to take eight hours now takes twenty minutes, the real question isn’t just about productivity, it’s about identity. What do you do with the other seven hours and forty minutes?
The promise of AI isn’t that it makes us more mechanical. It’s that it makes us less. By offloading the rote and repetitive, it leaves us the parts that are uniquely human: thinking, imagining, deciding, building trust. The irony is that the more work AI does, the more ‘human work’ there is left for us to do.
To put things in simple way:
Generative AI is all about generating things. Be it an intelligent essay, an absolutely new image, or a video, or a piece of music, or code base, or even voice, or analyse or clean data. All with the power of AI models trained by billions of data and reinforcement learning through human feedback. Unlike traditional AI, this is more democratic. AI is in our hands that can help create economically valuable work. We can start to use AI to do a piece of work for which we get paid. AI becomes an assistant to do our knowledge work. If the work we do in 8 hours can be done in 20 minutes, that is truly saving us time and efforts. It allows us to focus on more strategic and developmental creative and meaningful relationship building work. AI allows us to do more human work, by taking away the mechanical work from us.
Q: Chat Gpt, Google Gemini തുടങ്ങിയവ വിവിധ മേഖലകളിലെ തൊഴിലുകൾ വൻ തോതിൽ ഇല്ലാതാക്കുമെന്നതാണ് ഇത് സംബന്ധിച്ച് ഏറ്റവും ആദ്യം ഉയർന്നു വരുന്ന ആശങ്ക. കമ്പ്യൂട്ടറുകൾ വരുമ്പോൾ ഉണ്ടായിരുന്ന ആധികൾ നിരവധിയായ പുതു ഡിജിറ്റൽ തൊഴില വസരങ്ങളിലൂടെ ഒട്ടൊക്കെ പരിഹരിക്കപ്പെടുകയുണ്ടായി. അത് പോലെ ബഹുതലങ്ങളിൽ ഉള്ള മനുഷ്യ സേവനങ്ങൾ A I മേഖലയിൽ ആവശ്യം വരുമോ? ചില ഉദാഹരണങ്ങൾ?
A: 7 00 million people are already using ChatGPT. At first, it looks like a toy, but for most it’s become a tool: writing, learning, organizing, even therapy. Productivity goes up. And with it, expectations.
The pattern is familiar. When the tools get better, the bar gets higher. AI eats the grunt work in knowledge and service jobs, and what’s left is the part that can’t be automated: strategy, judgement, trust. Employers notice. They start to prefer people who know how to work with AI, not fight against it.
The real leverage comes when you stop thinking of AI as “a tool” and start thinking of it as “a team.” A professional who can orchestrate a handful of AI assistants doesn’t just work faster, they become a multiplier. They can handle bigger portfolios, more complex projects, without needing more people.
That’s why AI skills aren’t optional. They’re the new literacy. The difference between those who stay relevant and those who quietly fall behind will be how well they learn to work with their new team of invisible colleagues.
Q: അതിവേഗം വളരുകയും പരിണ മിക്കുകയും ചെയ്യുന്ന ഈ രംഗത്ത് ഇന്ത്യയുടെ നില എവിടെയാണ്?
a) നവസാങ്കേതിക പര്യാപ്തതയിൽ ( digital capacity ) b) അടിസ്ഥാനസൗകര്യ ലഭ്യതയിൽ c) വിദ്യാഭ്യാസ സൗകര്യങ്ങളിൽ d) knowledge updation ൽ e) AI പ്രതിഭാസത്തോട് പൊരുത്തപ്പെടാനും മുന്നോട്ട് നീങ്ങാനും ഉള്ള സർക്കാർ നയങ്ങളും പദ്ധതികളും സംബന്ധിച്ച്. 5. പല വ്യത്യസത മേഖലകളെക്കുറിച്ചുമുള്ള താങ്കളുടെ ഭാഷണങ്ങളും ക്ലാസ്സുകളും U ട്യൂബിൽ hot ആയിരിക്കുന്നു. മലയാളനാട് പോലെയുള്ള online മാധ്യമരംഗത്ത് എങ്ങനെയാണു chat ബോട്ടുകൾ പ്രയോജനപ്പെടുക? ഒപ്പം എഴുത്തിന്റെ ലോകത്തെക്കുറിച്ച് കൂടി അറിയാനുണ്ട്. ആശയങ്ങളും എഴുതാനുള്ള ആഗ്രഹവും ഉള്ളപ്പോഴും എഴുത്തിലേക്കു എത്താനും അതിന് വേണ്ടി സമയവും അധ്വാനവും ചിലവഴിക്കാനും സാധിക്കാതെ വരുന്നവർ ഉണ്ട്. അവർക്ക് എങ്ങനെ ഇവ രചനയ്ക്ക് സഹായകമാക്കാൻ കഴിയും?
A: India has always been better at use-cases than infrastructure. We didn’t build the big AI models, but we’re getting good at building on top of them. That turns out to be just as interesting, because products, not models, are where people actually feel the change.
We treat AI less like a technology and more like a new way of working. Adoption here is fast, sometimes messy, but real. Schools already use AI, mostly for small, obvious tasks. The missed opportunity is imagination. With tools like ChatGPT or Gemini, you could give every student a personalized tutor. At that point, the excuse of ‘not enough attention’ disappears for both learners and teachers.
In മലയാളനാട്, the leverage is even clearer. Many people here have original ideas but struggle to express them. Until now, that was a bottleneck. AI removes it. You can feed in fragments, half-thoughts, raw notes—and it will help you shape them into something articulate, something shareable – without losing the real intended meaning.
The barrier of words is gone. What’s left is whether you have something worth saying.
Q: prompt എന്നത് ഒന്ന് വിശദീകരിക്കാമോ?
A: For most of computing history, we’ve had to talk to machines in their language. Code, GUIs, structured clicks. Now, for the first time, we can talk to them. That’s what a prompt is: the input that tells the model what to do, written in plain language. It feels trivial until you realize it changes everything about how we work with computers.
The catch is that not all prompts are equal. The output is only as good as the input. People judge AI by the responses they get, but what they’re really testing is their own ability to ask. Prompting is a skill, and most people don’t know it yet.
A good prompt isn’t just a question, it’s a blueprint. It can include a role, a task, a context, constraints, even the format of the answer you want. The difference between a casual prompt and a crafted one can be the difference between something mediocre and something extraordinary.
And the loop is recursive. You can use AI to teach you how to prompt it better. Which means the people who learn to write great prompts aren’t just using the tool; they’re compounding it. They’re the ones who’ll pull the most leverage from this shift.
Q: ഉത്തരങ്ങളുടെയും assignments ന്റെയും രൂപത്തിൽ, matter തയ്യാർ ചെയ്തു ലഭിക്കുന്നത് വിദ്യാർത്ഥികളുടെ ആലോചിക്കാനും, വിശകലനം ചെയ്യാനും ഗ്രഹിക്കാനും ഉള്ള താല്പര്യത്തെയും അന്വേഷണ കൗതുകത്തെയും തണുപ്പിക്കില്ലേ? അത് ഭാവിതലമുറയുടെ ബൗദ്ധികവ്യായാമത്തെയും വളർച്ചയെയും ( intellectual capacity building ) മന്ദീഭവിപ്പിക്കുമോ?
A: The hardest part of learning has always been getting started. The activation energy was high, too much time to gather material, too much effort to make sense of it. Most people gave up before they even began.
Now that barrier is collapsing. In my recent book, Fluid Intelligence, I argue that conventional learning time can shrink by as much as 95%. The reason is simple: personalization. Each of us learns differently, and AI finally lets us lean into that. You can begin light, playful, even messy, and level up gradually. No more wrestling with rigid courses designed to overwhelm.
AI doesn’t just feed you information. It shapes insights, asks questions back, and pushes your thinking. It can be a co-worker, a classmate, a teacher who never gets tired. The guilt of “not learning the right way” disappears. You just start, in your own way.
That shift matters. Because once the inertia of learning is gone, what’s left is only the curiosity to keep going.
Q: എന്തും ചോദിക്കു എന്നാണല്ലോ A I ബോട്ടുകളുടെ അഭിവാദ്യം. തെറ്റുകൾ ഉണ്ടാവാം എന്ന മുന്നറിയിപ്പ് ഉണ്ട്. എന്താണ് Chat bot hallucination?
A: AI models don’t know things the way people do. They’re trained to guess the next word. That’s their whole game: good guesswork. Which means even when the prompt is unclear, or the model has no real answer, it will still try. It guesses anyway, because silence would break the flow.
ChatGPT in particular is built for conversation. And conversations don’t like gaps. So the model fills them, even if it has to invent. That’s what we call “hallucination,” but really it’s just the model doing what it was designed to do. Keep talking.
The way to handle this isn’t complicated. If you don’t want guesses, tell it not to guess. Ask it to stick to facts. The clearer you are about what you expect, the less it will try to make things up just to please you.
Cover: Jyothis Paravoor
Krishnakumar: Digital creator. Cofounder and CEO at Green pepper consulting India Ltd. Practice Leader and educator in Generative AI
