Every startup begins with the same goal: solve an unsolved problem for a specific group of people. But here’s the often-overlooked question:
Which group of people should you solve for?
Before you can nail product-market fit, you need to find a community that truly speaks to you. It’s not about targeting an audience or pitching a solution. Those approaches often miss the mark. Instead, it’s about immersing yourself in a community where you feel connected and inspired. By being part of their world, you uncover the problems that matter — and the ones worth solving.
When I worked on my last startup, a decentralized social network for artists and creators, I didn’t start out building for them. I was working on something completely different. But I’d always been drawn to the arts community — I collected art, followed artists on Twitter, joined group chats, and had countless conversations. Over time, I started noticing their challenges. And some of those challenges were things I could help solve.
That shift didn’t come from pitching them. It came from showing up, listening, and learning.
The best communities aren’t a means to an end — they’re places where you feel genuinely connected. When you find your tribe, the path to meaningful solutions becomes clearer.
So, before you obsess over cool technology or brainstorm which problems to solve, ask yourself this: What communities fascinate me?
Once you have founder-community fit, the rest of the journey isn’t easy — but it’s easier.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
There’s a persistent myth that AI is here to replace human workers. It’s an easy narrative to believe—robots taking over jobs make for sensational headlines. But the truth is more nuanced. AI doesn’t eliminate jobs; it changes them. And that’s where the opportunity lies.
When I worked on Myra, an AI-powered customer service platform, this became clear. AI handled repetitive tasks like password resets and account updates, but it couldn’t handle nuance.
It’s the difference between “my driver wasted my time” and “my driver was wasted” — only humans could step in to resolve such issues with judgment and empathy. Customer service agents didn’t just solve problems; they monitored AI performance, flagged failures, and trained the system to improve. Their roles became more strategic and high-value.
This shift aligns with the 80/20 Value Principle. In most jobs, 80% of value comes from high-impact tasks, but workers spend most of their time on repetitive, low-value work. AI flips this dynamic. It handles the routine, freeing people to focus on creativity, problem-solving, and judgment.
Upskilling is the key to closing this gap. Workers need to shift from repetitive tasks to roles that involve creativity, critical thinking, and decision-making. Imagine a customer service agent in an AI-first workplace:
The World Economic Forum estimates that while 85 million jobs may be displaced by automation by 2025, 97 million new roles will emerge.
The biggest opportunity in the next 10 years will lie in preparing workers to embrace these shifts, equipping them to thrive in roles that are more rewarding, strategic, and impactful.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
Over the years, my interests have centered on technology, society, and how the two intersect. Here are three areas I’m actively exploring:
1. AI and its transformative impact
AI is my professional background, and its evolution continues to intrigue me. While the spotlight is on training ever-larger models, finding use cases, figuring out regulations, and creating AI policy — I’m also curious about what’s next — like new UX paradigms that go beyond chat. How can we design interfaces that make AI more intuitive and adaptable? Or what comes after LLMs? And will just adding more compute make these models truly reason?
I’m also exploring how AI reshapes society. If agents take over repetitive tasks, what happens to our roles and incomes? In a remote-first, AI-enabled world, how do social interactions and workplace dynamics change?
2. Climate change and sustainability
Climate change is the defining challenge of our time, yet the tech industry, particularly AI, often ignores its environmental impact. The carbon cost of training massive models is just one example of how innovation can clash with sustainability.
I’m diving into the climate tech space to better understand how we can align technological progress with environmental responsibility. What are the most promising innovations, and how do we ensure that progress doesn’t come at the planet’s expense?
3. The decentralized internet and free speech
For two years, I ran Solarplex, a social network for creators built on the decentralized protocol powering Bluesky. It allowed artists and musicians to connect directly with audiences, monetize through subscriptions, and create digital products — without relying on ads or centralized platforms.
As platforms like X tighten control, decentralization is becoming critical — not just for free speech but to address challenges like misinformation. I’m exploring how decentralized networks can empower users while balancing trust and accountability. This becomes even more important in an age where AI powered “slop” can truly influence large groups of people.
If you're exploring similar ideas, I’d love to exchange thoughts and learn from you.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
10 years ago, I built a chatbot called Myra that could handle restaurant recommendations, grocery orders, and even Uber bookings—all through WhatsApp. Excited by its potential, I decided to turn it into a company. Over the next five years, I went on an incredible journey, learning hard lessons about technology, business, and myself.
I hope sharing these 3 mistakes I made can help founders avoid some major pitfalls.
Focusing on the tech instead of the problem
I was so excited about what Myra could do that I didn’t stop to think about whether it solved a real customer problem. We built features that were cool but not valuable enough for anyone to pay for.
I learned that technology is a tool, not the destination. Start with the customer problem and let the tech follow — it’s the only way to build something people truly need.
Underestimating the importance of business development
I spent too much time obsessing over the product and not enough on how to sell it. I assumed that if we built something great, customers would naturally show up.
But the reality is, building the product is only half the battle. Understanding your market, creating a go-to-market strategy, and building relationships are just as critical — if not more so — than the tech itself.
Sticking too closely to the original vision
When we created the machine learning systems as part of Myra’s backend, I didn’t explore its potential as a standalone product. I was too attached to the idea of a chatbot assistant, even though the market wasn’t ready for it.
By failing to adapt to what customers actually wanted, I missed opportunities that could have led to better outcomes. This taught me to stay flexible and let the market guide the direction, rather than holding too tightly to a single vision.
Ultimately, success isn’t only about great technology — it’s about solving real problems, staying close to your market, and being flexible enough to pivot when needed.
What lessons have you learned in your journey?
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
Imagine this: Your AI assistant reminds you to pick up coffee filters—and suggests a deal at the local shop.
Welcome to the age of ad-supported AI, where your assistant is also a marketer. It’s inevitable, practical, and fraught with challenges.
Marissa Mayer’s vision of ad-sponsored AI chatbots could make these tools universally accessible by turning conversations into contextual ad opportunities.
After all, Google built an empire by pairing searches with ads. Why shouldn’t AI chatbots do the same?
But here’s the twist: unlike search engines, AI assistants don’t just answer questions—they hold entire conversations, remember preferences, and build persistent histories. This context-rich interaction makes them more capable of delivering laser-targeted suggestions. The flip side? Privacy risks escalate. How do we ensure your chatbot doesn’t become an all-seeing billboard for advertisers?
The future of AI assistants lies in balance. Done right, ads could fund free, advanced tools that genuinely help users. Done wrong, it’s a slippery slope into manipulation and data exploitation.
The question isn’t whether this model will happen—it’s how we’ll keep it from crossing ethical lines.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.