AI will not achieve technological equality; it will only reward the right people.

Author: Naman Bhansali

Translation: Deep潮 TechFlow

Deep潮 Guide: In the early stages of new technology adoption, people often have a “tech equality” illusion: when photography, music creation, or software development become effortless, does the competitive advantage disappear? Warp founder Naman Bhansali combines his personal experience crossing from an Indian small town to MIT with entrepreneurial practice leading AI-powered payroll solutions, revealing a counterintuitive truth: the more technology lowers the barriers (Floor), the higher the industry’s ceiling (Ceiling) rises.

In this era where execution becomes cheap or even can be “vibecoded” by AI, the author believes that the true moat is no longer just traffic distribution, but the inimitable “Taste,” deep insight into the underlying logic of complex systems, and patience to compound over a decade. This article is not only a reflection on AI entrepreneurship but also a strong argument for the power law: “Common technology leads to aristocratic results.”

The full text is as follows:

Whenever a new technology lowers entry barriers, the same predictions always follow: since everyone can do it now, no one has an advantage. Smartphone cameras make everyone a photographer; Spotify makes everyone a musician; AI makes everyone a software developer.

These predictions are always half-true: the floor indeed rises. More people participate in creation, release products, and join the competition. But they always overlook the ceiling. The ceiling rises even faster. The gap between the median level and the top level—between the middle and the elite—is not shrinking; it’s widening.

This is a characteristic of power laws: they don’t care about your intentions. Equalizing technology always results in aristocratic outcomes. Every time.

AI is no exception, and may even be more extreme.

Market Evolution

When Spotify launched, it did something truly radical: it enabled any musician on Earth to access distribution channels previously only reachable by record labels, marketing budgets, and luck. The result was a music industry explosion—millions of new artists, billions of new songs released. The floor indeed rose as promised.

But what followed was: the top 1% of artists now capture a larger share of plays than in the CD era. Not smaller, but bigger. More music, more competition, more ways for audiences—no longer limited by geography or shelf space—to gravitate toward the top works. Spotify didn’t unify music; it intensified the competition.

The same story repeats in writing, photography, and software. The internet has produced the largest number of creators in history, but also a harsher attention economy. More participants, higher top bets, same fundamental pattern: a tiny minority captures most of the value.

We’re surprised because we think linearly—expecting productivity gains to distribute evenly like pouring water into a flat container. But most complex systems don’t work that way; they never have. Power law distributions are not market quirks or technological failures—they are nature’s default. Technology didn’t create them; it merely reveals them.

Think of Kleiber’s Law: across all life forms—from bacteria to blue whales, spanning 27 orders of magnitude in body mass—metabolic rate scales with body mass to the 0.75 power. A whale’s metabolism isn’t proportional to its size; it follows a power law with high precision across nearly all life. No one designed this distribution; it’s simply how energy flows in complex systems, following their intrinsic logic.

Markets are complex systems; attention is a resource. When friction disappears—when geography, shelf space, and distribution costs no longer buffer—the market converges to its natural form. It’s not a normal distribution bell curve but a power law. Equalizing stories and aristocratic outcomes coexist, which is why every new technology catches us off guard. We see the floor rising and assume the ceiling is following at the same pace. But that’s not true—the ceiling is accelerating away.

AI will accelerate this process faster and more ruthlessly than any previous technology. The floor is rising in real time—anyone can publish products, design interfaces, write production code. But the ceiling is also rising, and faster. The key question: what determines your ultimate position?

When execution becomes cheap, taste becomes a signal

In 1981, Steve Jobs insisted that the internal circuit boards of the first Macintosh be beautiful—not for appearance, but for the unseen, internal parts. His engineers thought he was crazy. But he wasn’t. He understood something often dismissed as perfectionism but closer to a proof: how you do anything is how you do everything. Someone who makes the hidden parts beautiful isn’t just performing quality; it’s a reflection of their character—unable to release subpar work.

This matters because trust is hard to build but easy to fake quickly. We run heuristics to figure out who’s truly excellent and who’s just pretending. Credentials help but can be manipulated; pedigree helps but can be inherited. The hardest thing to fake is Taste—a persistent, observable commitment to a standard that no one demands. Jobs didn’t have to make the circuit boards beautiful; he chose to. That act alone signals how he would approach the unseen parts.

For most of the past decade, this signal was somewhat obscured. During the SaaS boom (roughly 2012–2022), execution became so standardized that distribution became the real scarce resource. If you could efficiently acquire customers, build a sales machine, and hit the “Rule of 40”—the product itself hardly mattered. As long as your go-to-market strategy was strong enough, you could win with an average product. The signals of Taste were drowned out in the noise of growth metrics.

AI has radically changed the signal-to-noise ratio. When anyone can generate a functional product, a beautiful interface, and a deployable codebase in an afternoon, whether something is “useful” is no longer a differentiator. The question becomes: is it truly excellent? Does this person understand the difference between “good” and “insanely great”? Even without external pressure, do they care enough to close that last gap?

This is especially true for business-critical software—systems handling payroll, compliance, employee data, and legal risks. These aren’t products you can try out casually and abandon next quarter. Switching costs are real; failure modes are severe; those deploying the system are responsible for consequences. Before signing, they run all trust heuristics. An attractive product is one of the loudest signals—saying: the builders care. They care about the visible parts, which suggests they also care about the invisible.

In a world where execution is cheap, taste is proof of work.

What the new phase rewards

This logic has always held, but the market environment over the past decade made it nearly invisible. Once, the most important skill in software wasn’t even about the software itself.

Between 2012 and 2022, SaaS’s core architecture was set. Cloud infrastructure became cheap and standardized; development tools matured. Building a functional product was hard but a “solved problem”—you could hire your way there, follow established patterns, and as long as resources were sufficient, hit the minimum bar. The real scarcity was distribution—can you acquire customers efficiently? Can you build a repeatable sales motion? Do you understand unit economics well enough to fuel growth with reinvestment?

Founders thriving in that environment mostly came from sales, consulting, or finance. They understood metrics that sounded like science fiction ten years earlier: NDR, ACV, Magic Number, Rule of 40. They lived in spreadsheets and pipeline reviews, and in that context, they were correct. The SaaS peak produced SaaS founders of the same era—an evolution driven by rational adaptation.

But I felt stifled.

I grew up in a small town in India with 250 million people. Only about three students per year in all of India get into MIT. Without exception, they came from expensive prep schools in Delhi, Mumbai, or Bangalore—institutions built specifically for that purpose. I was the first in my state’s history to get into MIT. I mention this not to boast but to illustrate a microcosm of the argument: when entry barriers are high, pedigree predicts success; when barriers open, deep people always win. In a room full of elites, I was a chip that won by depth. That’s the only bet I know.

I studied physics, math, and computer science—fields where the deepest insights aren’t from process optimization but from seeing truths others miss. My master’s thesis was on straggler mitigation in distributed machine learning: how to optimize when some parts lag behind in large-scale systems without compromising integrity.

When I looked at the startup world in my early twenties, I saw a landscape where these deep insights seemed irrelevant. Market premium favored go-to-market, not product. Building technically excellent things seemed naive—seen as a distraction from the “real game” of customer acquisition, retention, and sales velocity.

Then, at the end of 2022, the environment shifted.

What ChatGPT demonstrated—more intuitively and shockingly than years of research papers—is that the curve has bent. A new S-curve has begun. Phase transitions no longer reward those best at the previous stage but those who can see the new stage’s infinite possibilities before others.

So I quit my job and founded Warp.

This is a very specific bet. There are over 800 tax agencies in the US—federal, state, local—each with its own reporting requirements, deadlines, and compliance logic. No APIs, no programmatic access. For decades, payroll providers handled this by stacking humans—thousands of compliance experts manually navigating these systems not designed for scale. Giants like ADP, Paylocity, Paychex built entire business models around this complexity—they don’t solve it; they absorb it into headcount and pass costs to clients.

In 2022, I saw AI agents still fragile. But I also saw the curve of improvement. Someone deeply immersed in large-scale distributed systems and model evolution could make a precise bet: the fragile tech of today would become incredibly powerful in a few years. So we bet: build an AI-native platform from first principles, starting with the most difficult workflows—those that traditional giants can never automate due to architecture constraints.

Now, that bet is paying off. But more broadly, it’s about pattern recognition. In the AI era, technical founders not only have engineering advantages but also insight advantages. They see different entry points, make different bets. They can examine a system deemed “permanently complex” and ask: what’s needed for true automation? And, crucially, they can build the answer themselves.

The peak SaaS era’s giants are rational optimizers under constraints. AI is removing those constraints and installing new ones. In this new environment, scarce resources are no longer distribution but the ability to see possibilities—and to build them with the right standards of taste and belief. But there’s a third variable that decides everything, and that’s where most AI-era founders make catastrophic mistakes.

Long-term game in high gear

The current startup meme: you have two years to escape the permanent bottom. Build fast, raise fast, then exit or fail.

I understand where this mindset comes from. The speed of AI’s evolution creates a survival crisis; the window to catch the wave feels razor-thin. Young founders seeing overnight success stories on Twitter assume the game is about speed—those who run fastest in the shortest time win.

But that’s correct in a completely wrong dimension.

Speed of execution is critical—I believe it deeply (hence the name Warp). But speed isn’t the same as shortsightedness. The most valuable companies in AI aren’t built by those who cash out in two years. They’re built by those who sprint for ten and enjoy compound growth.

Short-termism is wrong because the most valuable assets in software—private data, deep customer relationships, switching costs, regulatory expertise—take years to build. No matter how much capital or AI power competitors bring, they can’t copy this quickly. When Warp processes payroll across states, we’re accumulating compliance data across thousands of jurisdictions. Every tax notice solved, every border case handled, every state registration completed trains a system that becomes harder to replicate over time. It’s not a feature; it’s a moat—built by deep, high-quality work over a long period, creating density of quality.

This compounding isn’t visible in year one. It’s faint in year two. By year five, it’s the entire game.

Frank Slootman, former CEO of Snowflake, built and scaled more software than anyone alive. He succinctly states: get used to being “uncomfortable.” Not for a sprint, but as a permanent state. The early “fog of war” in startups—uncertainty, incomplete info, urgent decisions—doesn’t go away after two years. It evolves; new uncertainties replace old. The founders who endure aren’t those who find certainty but those who learn to move clearly through fog.

Building a company is brutally hard, and that’s hard to convey to those who haven’t done it. You live in constant slight fear, punctuated by higher-level terrors. You make thousands of decisions with incomplete info, knowing a string of mistakes could end everything. Those “overnight successes” on Twitter are not just outliers in a power law—they’re extreme outliers. Using their cases to guide your strategy is like studying marathon runners who took wrong turns and still finished 5 km to train for a marathon.

Why do it? Not for comfort, not for high odds. But because, for some, not doing so feels like not truly living. The only thing worse than the fear of building something from nothing is the silent suffocation of never trying.

And—if you bet right, see truths others haven’t priced, and execute with aesthetic and conviction over a long cycle—the results are not just financial. You build something that truly changes how people work. You create a product people love to use. You hire and empower those who perform at their best in your own creation.

This is a ten-year project. AI can’t change that; it never has.

AI changes the ceiling—what founders who endure to the end can reach in ten years.

The unseen ceiling

So, what will software look like on the other side of all this?

Optimists say AI creates abundance—more products, more builders, more value distributed to more people. They’re right. Pessimists say AI destroys the moat—anything can be copied in an afternoon, defense is dead. They’re partly right. But both focus on the floor; no one pays attention to the ceiling.

In the future, thousands of point solutions will emerge—tiny, functional, AI-generated tools capable of solving narrow problems. Many won’t be built by companies but by individuals or internal teams solving their own pain points. For some low-barrier, easily replaceable categories, the market will democratize truly. The floor is high, competition fierce, margins razor-thin.

But for business-critical systems—handling cash flow, compliance, employee data, legal risks—the situation is different. These are workflows with extremely low tolerance for error. Payroll failures mean employees don’t get paid; tax errors mean IRS comes knocking; benefits gaps during open enrollment mean real people lose coverage. Those choosing software bear responsibility for consequences. This responsibility can’t be outsourced to an AI cobbled together in an afternoon.

For these workflows, companies will continue to trust vendors. The “winner-takes-all” dynamic will be even more extreme than in previous software generations. Not only because of network effects (though that’s true), but because an AI-native platform that accumulates private data across millions of transactions and thousands of edge cases creates a moat that’s nearly impossible for latecomers to jump. The moat isn’t just a feature set; it’s the quality built over long periods of high standards in a domain that punishes errors.

This means the software market will be more consolidated than SaaS. I expect in ten years, HR and payroll won’t have 20 companies with single-digit market shares. Instead, two or three platforms will dominate most value, with a long tail of point solutions barely making a dent. The same pattern will apply across categories with complex compliance, data accumulation, and high switching costs.

Leading companies at these distribution peaks will look very similar: founded by technically talented people with genuine product taste; built from day one on AI-native architecture; operating in markets where incumbents can’t respond structurally without dismantling their existing business. They will have made a unique insight bet early—seeing some unpriced truth created by AI—and stuck with it long enough for compound growth to become clear.

I’ve been describing these founders in the abstract, but I know exactly who they are because I’m trying to become one.

In 2022, I founded Warp because I believed the entire employee operations stack—payroll, compliance, benefits, onboarding, device management, HR workflows—is built on manual labor and legacy architecture that AI can replace entirely. Not improve, but replace. Old giants built billion-dollar businesses by absorbing complexity into headcount; we aim to eliminate complexity at the source.

Three years in, the bet has proven right. Since launch, we’ve processed over $500 million in transactions, are growing rapidly, and serve companies building the world’s most important technology. Every month, the compliance data, edge cases, and integrations we accumulate make the platform harder to copy and more valuable to customers. The moat is still early but has already taken shape and is accelerating.

I share this not because Warp’s success is predestined—nothing in a power law world is—but because the logic that brought us here is exactly what I described throughout: seeing the truth. Going deeper than anyone else. Building a high standard that sustains itself without external pressure. Persisting long enough to see if you’re right.

The companies of the AI era will be built by those who understand: entry has never been scarce; insight is. Execution has never been the moat; taste is. Speed has never been an advantage; depth is.

The power law doesn’t care about your intentions. But it rewards the right ones.