Reimagining the Future: The Perplexity AI's Way
A Pattern Breaker’s Guide to Reimagining the Status Quo
Introduction: The Problem Isn’t Solved—Yet
The question my inner voice often asks is: What else could be different? How can this product be rethought or reimagined? How might this problem be solved in a completely new way?
I ask these questions to satisfy my own curiosity. I often sit with the problem for a few minutes, trying to find a better solution. And more often than not, I conclude: There’s probably nothing significantly better that can be done, at least nothing can be done in a way that would be meaningfully more useful or impactful. But almost inevitably, I come across another product that solves the same problem in a way I never imagined—so elegantly, so effectively—that it completely shifts my perspective.
For a long time, I lived with a quiet assumption: that if something hasn’t already been improved, maybe it’s because it simply can’t be. In hindsight, I see that for what it was—a myth I had constructed for myself. It wasn't rooted in fact, but rather in a limited understanding of how innovation happens and how ideas evolve.
I don’t know if this belief is universal, but I suspect it’s not uncommon. And it was a belief that was completely shattered—in the best possible way—when I came across a paraphrased version of a famous Steve Jobs quote:
"Every product designed around you was made up by people who were not necessarily smarter than you. And you can change it, you can influence it, you can build your own things that others can use. But remember, it (product ) just happens to be this(designed) way, today."
That insight hit me deeply. It made me realize that the systems, products, and tools we interact with every day aren’t sacred or immutable—they're simply the result of someone else’s choices and imagination. And once you understand that, as Jobs said, you’ll never be the same again.
That shift in mindset filled me with hope, and purpose, and with a new pursuit: to stop accepting the status quo as permanent, and to start believing in the possibility of change—for the better.
If you're anything like me—if you've ever found yourself stuck in a mindset of quiet complacency or self-doubt—I hope this newsletter piece helps you break free from that pattern. Because the truth is, things can always be different. And sometimes, you just have to be the one to imagine how.
Reimagining the Familiar: The Power of Questioning Assumptions
Let us take transportation as an example. The lack of faster, more efficient movement—of both people and goods—has always been a major pain point for humanity. It’s not a new problem, but it’s one that continues to be reimagined and improved over time.
In the pre-industrial era, we domesticated animals for transport. Then came the invention of the wheel, which led to wagons and carts. During the industrial era, we introduced the steam engine. Today, we have cars, airplanes, and bullet trains. Even these are evolving—cars powered by electricity, hydrogen, and other emerging fuel technologies are becoming increasingly common.
But it certainly doesn’t stop here. The future holds more possibilities. So the real question becomes: What does it take to recognize what could be different? How do you build the mindset to imagine the “how” before it becomes obvious to the rest of the world?
The Pattern Breakers Mindset
Reading Pattern Breakers by Mike Maples Jr. and Peter Ziebelman opened my mind in a powerful way. The concepts in this book nurtured a mindset of seeing what others don’t—of recognizing early signals and imagining future breakthroughs before they materialize.
In the rest of this article, I want to apply those concepts to reverse-engineer a product that I believe has achieved remarkable success by rethinking a long-standing problem of Information Search & Retrieval: Perplexity AI. If you ask me why we are doing this? This is a training exercise for your muscles to nurture the pattern-breaking mindset, and in the inference phase, you can apply it to your own start-up ideas. I hope you got the AI pun 😉 Let’s begin this journey, shall we?
Case Study: Perplexity AI – Rethinking Information Retrieval
Perplexity AI was founded in 2022 by Aravind Srinivas, Denis Yarats, Johnny Ho, and Andy Konwinski. As of December 2024, the company was valued at $9 billion with around 100 employees. By March 2025, it was reportedly in early talks for another funding round that could double its valuation to $18 billion.
If you’re active on the internet or follow developments in AI, you probably already know the problem Perplexity is solving: information search & retrieval.
Is this a new problem? Absolutely not.
Since the earliest days of recorded knowledge, humanity has struggled with how to find the right information. We built libraries, developed classification systems, and created indexing techniques to make retrieval easier. With the digital age came computers, the web, and eventually search engines. Google’s algorithm revolutionized information retrieval and dominated the space for decades.
It seemed like the problem was “solved”—until Perplexity came along and challenged that assumption. ( I am sure somebody or Perplexity will challenge that assumption again :) )
As of late 2024, Perplexity had about 10 million monthly active users, and that number has been steadily growing. With strong user retention and increasing engagement, it’s clear that the product has achieved Product-Market Fit (PMF) in a domain once considered untouchable.
So, what did they do differently? How did they re-imagine the new “how”?
Inflection Theory: The Spark for Change
One of the core ideas from Pattern Breakers is Inflection Theory, and it’s a key reason for Perplexity’s success.
An inflection is an external event that creates the potential for radical change. This could be a new technology, regulation, or idea. The invention of electricity, for instance, moved us from oil lamps to electric lights and now powers everything from phones to trains.
While inflections are typically external, companies can also create and consume their own. Google, for example, designs its own Tensor Processing Units (TPUs) to run its AI workloads—here, the internal innovation becomes the inflection point.
In Perplexity’s case, the inflection was the rise of Generative AI—the ability of software to create text, images, videos, and more from what it has learned. While this might seem like an obvious opportunity in hindsight, recognizing an inflection before the world validates it is the real challenge.
Recognizing a true inflection often feels like solving a tricky math problem. You struggle for a while, but once you see the solution, it seems so obvious you wonder how you missed it. The only way to get better at noticing inflections is by immersing yourself in the world around you—observing, studying, and reflecting on patterns, even from the past. The book also gives examples of what Stress tests these inflections. So, here is an example of what it would look like from Perplexity’s point of view of its Inflection - Generative AI.
Insights: From Inflection to Impact
Identifying an inflection is only the beginning. The next step is forming a powerful insight—a non-obvious truth about how an inflection can be used to reshape human behavior or capability.
This part is hard. It requires deep understanding and imagination.
In Perplexity’s case, the founders realized that generative AI could fundamentally change how people search, learn, and interact with information. They didn’t just ride the generative AI wave—they channeled it into a precise insight: People don’t just want information; they want answers, quickly and reliably. This could radically change the way human beings get the information they need.
I said finding this insight is not easy. Let’s consider another historical parallel to explain why it isn’t so easy. When touchscreen technology became viable, Steve Jobs had the foresight to see it wouldn’t just be a new input method—it would transform how people use and experience mobile devices. Companies like Nokia, Microsoft, and Blackberry failed to see this. That’s the nature of true insight: it’s usually non-obvious—and often non-consensus—until proven right. Imagine big tech-savvy, capital-rich companies such as Nokia and Microsoft not being able to anticipate the inflection point and leverage the insight; how difficult would it be for people who are even further away from the inflection point?
This brings us to another characteristic of powerful insights: they must be both non-consensus and correct. Steve Jobs' belief in the smartphone was exactly that. Similarly, Perplexity’s insight, in a market where Google holds over 90% dominance, was radically contrarian—and yet, increasingly correct. Let’s dive a little deep now :
Breaking Down the Insight: Why It Works
To evaluate whether Perplexity’s insight holds up, we can ask three critical questions drawn from Pattern Breakers:
1. Why is the insight right?
Because humans naturally communicate through conversation. It’s how we learn, exchange ideas, and clarify doubts. Perplexity recognized that generative AI could enable this same conversational experience with machines—transforming the traditionally transactional experience of search into something more intuitive and human-like.
This is reinforced by advances in natural language processing. Even Google’s traditional search uses vector-based semantic search to understand user intent and relationships between words. With the rapid development of technologies like Retrieval-Augmented Generation (RAG), which grounds LLM outputs in curated, trusted content, the ability to serve accurate, contextual answers is only improving. These trends confirm that Perplexity’s foundational insight—that we’re ready for a conversational paradigm of search—is well-grounded.
2. Why is the insight non-consensus?
Because despite the promise of generative AI, it remains a deeply misunderstood and complex technology. Most organizations—even those who understand the technical possibilities—don’t know where to start and how to apply.
There’s also skepticism. Concerns around trust, hallucinations, and accuracy in AI-generated responses make many wary. On top of that, users are deeply habituated to keyword-based search, and shifting that behavior is no small task.
Moreover, incumbents like Google face a strategic dilemma. Their core business—ad-driven keyword search—is at odds with building a true AI-native search engine that might cannibalize that revenue. With over 90% market share, there’s little incentive for them to disrupt themselves. And for others, the idea of competing against a near-monopoly is daunting. That’s why Perplexity’s move was so non-consensus—it dared to go where few thought it was viable.
3. Why is the timing right?
Because the models are at their worst today—and they’re only going to improve from here. Perplexity understood the inflection deeply: that LLMs are improving exponentially, while inference costs are falling. What seems expensive or imperfect today will soon be fast, cheap, and ubiquitous.
This aligns with Jevons’ Paradox, a historical pattern where improved efficiency leads to increased usage. In 1865, economist William Jevons observed that more efficient steam engines led to greater, not less, coal consumption in British factories. Similarly, as generative AI becomes more efficient, it will drive more usage—not less.
Perplexity is also well-positioned to ride the regulatory tailwinds. Governments and enterprises around the world are now working to define safe, responsible frameworks for AI deployment. This momentum will likely accelerate adoption of AI-native products that prioritize transparency, reliability, and safety—principles that Perplexity has embraced early.
I have summarized what we have learned so far. Please look at the table below.
Living in the Future: How to Spot Inflections Before They Happen
So far, I’ve used Perplexity as a case study to explain how the concepts from Pattern Breakers—like inflections and insights—can be used to validate a product idea. But we still haven’t fully answered the deeper question: How do you reimagine the “how”? Yes, you can identify an inflection, derive a powerful insight, and validate it using stress tests. But what if you miss the inflection altogether? What if you realize it only after it's too late?
Imagine launching a mobile food delivery app after the market has already matured. You’d be too late to ride the original wave. So the real question is: How do you position yourself to catch the next wave before it crests?
I believe the answer lies in pursuit and exposure. If you have a strong passion for a domain and the willingness to acquire the skills necessary to thrive in it, your journey itself will expose you to emerging opportunities. As you immerse yourself in that pursuit, you’ll start noticing gaps, technologies, and trends others might overlook. The key is to stay close to the edge—where change begins.
There’s a quote by science fiction author William Gibson, also cited in Pattern Breakers:
“The future is already here—it’s just not evenly distributed.”
That’s absolutely true. Somewhere in the world, someone is already working on what could become the next inflection point. For example, I was recently listening to the Google DeepMind podcast on Superintelligence: The Era of Experience, where they explored how machines might learn autonomously—without human data. It made me realize: while many of us are just beginning to understand LLMs and generative AI that work based on data that humans input, there are people already thinking beyond it.
Let’s go back to Aravind Srinivas, the CEO and co-founder of Perplexity. If you ask how he was able to "live in the future," the answer is in his trajectory. He was exposed to machine learning early on during his undergraduate days. He interned at OpenAI in 2018—years before ChatGPT entered public awareness. He also interned at DeepMind and Google, and later worked as a research scientist at OpenAI in 2021 on language and diffusion models. That early, hands-on exposure to cutting-edge work helped him recognize the generative AI inflection before the rest of the world caught on.
Of course, not everyone needs to follow that exact path. But the principle holds: to live in the future, you must first step out of the present. This could mean working at a frontier company, building side projects in an emerging field, exploring policy and regulatory trends, or simply surrounding yourself with people who are ahead of the curve.
It’s not about predicting the future with certainty. It’s about being close enough to see it forming. And that’s not impossible—it just takes pursuit, patience, and positioning.
Final Thoughts: The Problem Isn’t Solved—Yet
We've come a long way in this article. If you ask me what the core message is, it's this: The problem isn't solved yet. Any solution available today doesn't necessarily solve the problem completely—the scale is relative. There is always a better way to solve it. Sometimes, you need to imagine the "how."
There’s a common saying: “Don’t fix what isn’t broken.” But how do you truly know something isn’t broken unless you’ve experienced what the future could look like?
If you go to battle with a sword while your opponent has guns, you might only realize too late that your system is outdated. In my definition, that is broken too. That’s the danger of assuming something works simply because it hasn’t failed—yet.
That’s why it’s so important to recognize that you have the power to influence, to change, and to build something entirely new. To see things not as they are, but as they could be.
Perplexity proved this. It entered a market that was long dominated by a giant—Google—and carved out a space of its own. Not because it was fixing something broken, but because it imagined a better way. And that’s the key: success isn’t final. What sets companies like Perplexity apart is their ability to continuously envision the future and act on it, even when the rest of the world hasn’t caught up.
Another thing that I want to highlight is that you don’t always need to solve an existing problem. Often, an inflection point—a new technology, regulation, or idea—creates the opportunity to solve problems that previously couldn’t be solved at all.
So remember, once again:
Everything around you—the products you use, the systems you live with—was designed by people who were no smarter than you. You can influence it. You can change it. You can build something new that others will use.
It just happens to be this way today.
Because the truth is: things can always be different.
And sometimes, you just have to be the one to imagine how.
Side Note: This article just leverages some of the core concepts in the book Pattern Breakers. This article in no way has the depth covered in the book. But, I would highly encourage folks to read and apply the concepts in the book to develop the capability to re-imagine the future, but I hope this glimpse was helpful.
Sources
Pattern Breakers by Mike Maples Jr & Peter Ziebelman