Innovation isn’t about having the most resources. It’s about reconfiguring what you have under constraints.
Karl Marx’s insight from his Preface to A Contribution to the Critique of Political Economy feels surprisingly modern: “It is not the consciousness of men that determines their existence, but their social existence that determines their consciousness.”
In other words, material conditions shape systems and progress. And nowhere is this clearer than in today’s US/China AI arms race.
Take China’s AI breakthroughs, like DeepSeek R1 and Huawei’s Ascend GPUs. These weren’t achieved despite U.S. sanctions; they were achieved because of them. Denied access to Nvidia’s cutting edge chips, China turned constraints into opportunities, optimizing models to run on homegrown hardware. This is historical materialism at work: when resources are limited, innovation thrives.
This isn’t a new phenomenon. The Indian Space Research Organization (ISRO), which operated with a fraction of NASA’s resources, successfully launched interplanetary missions at record-low costs. Scarcity didn’t hinder ISRO; it forced them to innovate more creatively and efficiently.
Contrast this with Silicon Valley, where the abundance-first mantra —more GPUs, bigger budgets, endless scale — has dominated for years. Nvidia’s hardware and billion-dollar training runs delivered groundbreaking AI models, but the cracks are showing.
Ironically, Big Tech’s pursuit of abundance has created artificial scarcity.
Training cutting-edge models is prohibitively expensive, locking progress behind capital and regulatory barriers. Yet scarcity is the birthplace of reinvention. It compels a rethinking of the entire stack: from chips to frameworks to methods, resulting in leaner, more adaptable systems.
For software builders, this moment signals opportunity. Foundational tech is getting cheaper, but transformative apps beyond LLM chatbots remain rare. As LLM companies face shrinking moats, consolidation looms — with partnerships likely becoming their lifeline. The next frontier lies at the app layer, where creativity, not capital, will decide the winners.
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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.
Forgotten bug reports. Awkward email drafts. Chaotic Slack threads. These aren’t just the digital clutter of our lives. They’re going to be the gold veins that power Big AI’s next gold rush.
Consider this — large language models have already been trained on most of the available data on the internet, and fervent media articles and AI CEOs lament the upcoming shortage of training data. Meanwhile, we’re drowning in our own digital detritus: half baked emails, slack DMs, random notes, and document drafts. This data is more valuable than ever because it reflects the unpredictability of the real world, and yet, ironically, has never seen the light of a training set.
This so called “exhaust data” isn’t glamorous, but it’s cheap, unfiltered and full of real world nuance.
Exactly what an AI model needs to understand how to tackle the edge cases. While curated datasets can give us 90% of what needs to be done, that last 10% will come from understanding how humans try, fail, and iterate at their tasks. Midnight bug reports and chaotic email drafts aren’t just noise; they’re the edge-case scenarios AI needs to become truly adaptive.
But there’s an uncomfortable truth lurking here. Who owns this data? Is it you, the creator? Is it your employer? Or the tool that you created this data with? Big AI and scrappy startups are both in the race to get to this data first. They’re not waiting to ask for permission, and they’re certainly not going to compensate you for it.
The upcoming gold rush is not going to be around better algorithms. Those gains are incremental. What will define the winners is access to the staggering volumes of exhaust data to fine-tune models. As models like DeepSeek R1 challenge the dominance of American AI, data, not innovation will be where the next battles will be fought.
Our digital leftovers are on the brink of becoming indispensable. But as your discarded files become someone else’s treasure, we’re forced to confront a sobering reality: in a world driven by surveillance and commodified data, how much control are we willing to give up over even our most mundane moments?
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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.
I thought customer interviews were about asking the right questions. Turns out, they’re about what you give, not what you get.
Early on, I treated customer interviews like a checklist: ask questions, take notes, move on. The result? Shallow insights and vague answers I couldn’t use. After one frustrating interview, though I realized I wasn’t giving anything back! My conversations were transactions, not relationships. 300+ interviews later, here’s what I learned.
I. Stop asking, start giving.
I came armed with generic questions like, “What’s your biggest challenge?” The responses? Polite but uninspired. Everything changed when I focused on offering value instead of just asking questions.
These small gestures turned interviews into genuine conversations. People felt heard, valued, and eager to share meaningful insights.
II. Ask questions that spark emotion.
Surface-level questions lead to surface-level answers. My favorite question is now,
“If I could wave a magic wand, what’s one thing about your workflow you’d fix tomorrow?”
This question sparked stories, not generic responses. And those stories revealed emotional pain points I could solve.
III. Test your story, not just your product.
I used to wait for perfect clarity before testing. Rookie mistake. After 10 interviews, I started testing messaging alongside product ideas. Framing pain points, and floating solutions helped refine not just what I built, but also how I described it. By 50 interviews, clear patterns emerged in both problems and messaging.
The best insights come from collaboration, not interrogation. Treat every conversation as the start of a partnership, and you’ll turn vague answers into actionable breakthroughs — and make customers into allies.
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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.
When anyone can create apps as easily as content, software stops being a product. It becomes a language of expression.
For decades, apps have been meticulously crafted products designed for broad audiences. But most of life happens in the long tail: fleeting moments, hyper-specific needs, and niche communities. Traditional apps ignored these because they were too costly or unprofitable to build. AI changes all that.
Just as the smartphone camera turned billions into photographers and social media made everyone a creator, AI is democratizing software. When building apps becomes as easy as writing a tweet, the very concept of “apps” will transform.
Here’s the new landscape:
This shift unlocks the long tail of functionality traditional apps ignored. Hyper-specific problems now have solutions, transforming how we engage with and monetize software.
Discovery and monetization are evolving
The app store isn’t going away, but its dominance will fade.
Disposable, remixable apps will thrive on new platforms that prioritize speed, community, and creativity. They’ll spread like ideas: viral, ephemeral, and deeply personal.
When apps become as easy to make as TikToks, they won’t just change software. They’ll change how we live, work, and create.
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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.
Every time you ask ChatGPT a question, a server gulps down electricity. Multiply that by billions of queries, and you begin to see the staggering energy appetite behind AI’s rise. For context, a Google search uses 0.3 Watt-hours of energy. ChatGPT? 2.9 Watt-hours — a 10x increase.
Data centers already account for 1–1.5% of global electricity consumption. With AI driving exponential growth, that figure could soar past 10% by 2030. At first glance, this sounds like an energy crisis waiting to happen.
But what if AI’s appetite for power isn’t the problem — but the solution?
Consider this: solar power costs as little as $24 per Mega Watt-hours, while coal can hit $166, and nuclear tops $140.
For tech giants running billions of AI queries, fossil fuels aren’t just dirty. They’re expensive. Renewables aren’t a green choice. They’re the only viable choice.
AI’s relentless energy demand is forcing companies to scale solar, wind, and battery storage not because it’s trendy, but because it’s cheap.
Capitalism, not carbon taxes, may become the surprising hero of the energy transition.
Scaling renewables comes with challenges. Intermittent power and storage are significant hurdles. However, AI’s growing demand is driving innovation in these areas at a pace that treaties and regulations cannot match. In the next five years, I believe we will see substantial investment in energy and data center technology to meet this demand.
Funnily enough, AI might cut more carbon emissions by driving demand for renewables than by optimizing energy use with “green algorithms.”
While politicians debate carbon taxes and climate treaties, AI is already reshaping the grid. And this is a future I’d pay attention to.
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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.