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

Equalizing technology always results in aristocratization—it’s happened every time.

Author: Naman Bhansali

Translation: Deep Tide TechFlow

Deep Tide Editorial: In the early days of new technology adoption, people often fall into the illusion of “tech equality”: when photography, music creation, or software development become effortless, does the competitive advantage disappear? Warp founder Naman Bhansali, combining his personal journey from a small town in India to MIT and his entrepreneurial experience leading AI-powered payroll solutions, reveals a counterintuitive truth: the more technology lowers the floor (entry barrier), the higher the industry’s ceiling (top potential) rises.

In an era where execution becomes cheap—so cheap that AI can even “vibecode” (vibrationally encode)—the author believes that true moats are no longer just about traffic distribution but about unforgeable “taste,” deep insights into the underlying logic of complex systems, and patience to compound over a decade. This article offers not only a sober reflection on AI startups but also a powerful argument for the law of power-law: every new technology tends to produce aristocratic outcomes.

Full Text:

Whenever a new technology lowers the entry barrier, the same predictions always follow: since everyone can do it now, no one has an advantage anymore. Smartphones 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 overlook the ceiling—the top level. The ceiling rises even faster. The gap between the median and the top—between the average and the elite—does not shrink; it widens.

This is a hallmark of power laws: they don’t care about your intentions. Technology that promotes equality always results in aristocratization. Every time.

AI is no exception; in fact, it may be even more extreme.

Market Evolution

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

But what happened next was: the top 1% of artists now capture a larger share of plays than during 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-tier 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 stakes at the top, and the same fundamental pattern: a tiny minority captures the vast majority of 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 doesn’t create them; it merely reveals them.

Think of Kleiber’s Law: across all life forms—from bacteria to blue whales—metabolic rate scales with body weight to the 0.75 power. Whale metabolism isn’t proportional to whale size. This relationship is a power law, and it holds with remarkable precision across nearly all biological forms. 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 system—markets converge toward their natural form. That form isn’t a normal (bell curve) distribution but a power law. Equality stories coexist with aristocratic outcomes, which is why every new technology catches us off guard. We see the floor rising and assume the ceiling is rising at the same pace. But that’s not true—the ceiling is accelerating away.

AI will accelerate this process more rapidly and intensely 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 proof of work

In 1981, Steve Jobs insisted that the internal circuitry of the first Macintosh be beautiful—not for appearance, but for the unseen parts inside. His engineers thought he was crazy. But he wasn’t. He understood something often dismissed as perfectionism but actually 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; they have a personality that can’t tolerate releasing subpar work.

This matters because trust is hard to build but easy to fake quickly. We run heuristics—rules of thumb—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 truly unforgeable asset is taste—an enduring, observable commitment to a high standard for standards that no one demands. Jobs didn’t have to make the circuit boards beautiful; he chose to. That choice signals how he would do things in unseen areas.

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 was almost irrelevant. 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 trust heuristics. A beautiful 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 Stage Rewards

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

Between 2012 and 2022, SaaS architecture stabilized. Cloud infrastructure became cheap and standardized; development tools matured. Building a functional product was hard but “solved”—you could hire your way there, follow established patterns, and as long as resources were sufficient, reach the minimum bar. The real scarce resource that separated winners from mediocrities was distribution. Could you acquire customers efficiently? Could you build a repeatable sales motion? Did 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. In that context, they were correct. The SaaS peak produced SaaS founders who were rational adaptations.

But I felt stifled.

I grew up in a small town in India with a population of 250 million. Only about three students per year in all of India get into MIT. Without exception, they come 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 in. Not to boast, but to illustrate a microcosm of this article’s point: when entry barriers are high, pedigree predicts outcomes; when barriers open, deep people always win. In a room full of elite backgrounds, I was a bet on depth. It’s the only way I knew how to bet.

I studied physics, math, and computer science—fields where the deepest insights don’t come 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 damaging overall integrity.

When I was in my early twenties looking at startups, I saw a landscape where these deep insights seemed irrelevant. Market premiums favored “go-to-market,” not the product itself. Building technically excellent things seemed naive—an interference with 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 adapting to the previous stage but those who can see the infinite possibilities of the new stage before others do.

So I quit my job and founded Warp.

This is a very specific bet. In the U.S., there are over 800 tax agencies—federal, state, local—each with its own filing requirements, deadlines, and compliance logic. No APIs, no programmatic access. For decades, payroll providers handled this the same way: by stacking humans. Thousands of compliance experts manually navigated these systems not designed for scale. Giants like ADP, Paylocity, Paychex built entire business models around this complexity—they didn’t solve it but absorbed it into headcount and passed 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, targeting the most difficult workflows—those that traditional giants can never automate due to architectural constraints.

Now, that bet is paying off. But a broader pattern is emerging: 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 recognize possibilities—and to embed that insight into standards of taste and belief. But there’s a third variable that decides everything, and that’s where most AI founders make catastrophic mistakes.

Long-term game in a high-speed world

Today’s startup meme: you have two years to escape the perpetual bottom. Build fast, raise fast, or exit—or perish.

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. On Twitter, young people who go viral overnight assume the game is about speed—those who run the fastest in the shortest time win.

But they’re wrong in the wrong dimension.

Speed of execution is critical—I believe that deeply (it’s even in Warp’s name). But speed of execution is not the same as short-sightedness. The founders who will build the most valuable companies in AI are not those who cash out after two years. They are those who sprint for ten and enjoy compound growth.

Short-termism is mistaken 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 capability competitors bring, they can’t replicate this quickly. When Warp handles payroll across states, we’re accumulating compliance data across thousands of jurisdictions. Every tax notice resolved, every border case handled, every state registration completed trains a system that becomes harder to copy over time. It’s not just 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 everything.

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

Building a company is brutally hard, and that’s hard to convey to newcomers. You live in constant slight fear, punctuated by higher-level terrors. You make thousands of decisions with incomplete information, knowing one wrong move can end everything. Those “overnight successes” you see on Twitter are not just outliers—they are extreme outliers in a power-law distribution. Using their example to guide your strategy is like studying the wrong turns and missteps of runners who finished 5 km in 30 minutes to train for a marathon.

So why do it? Not for comfort. Not because the odds are in your favor. 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 “not trying.”

And if you bet right—if you see truths others haven’t priced in, if you execute with aesthetic and conviction over a long enough cycle—the results will be more than financial. You will build something that fundamentally changes how people work. You will create a product people love to use. And you will hire and empower those who perform at their best in the business you’ve built with your own hands.

This is a ten-year project. AI will never change that.

What AI changes is the ceiling—what those who endure to the end can reach.

The unseen ceiling

So, what will the future of software look like?

Optimists say AI creates abundance—more products, more builders, more value distributed to more people. They’re right. Pessimists say AI destroys the moats of software—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 even 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 will be high, competition fierce, and profit margins razor-thin.

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

For these workflows, companies will continue to trust vendors. And among those vendors, the “winner-takes-all” dynamic will be even more extreme than in previous software generations. Not just because of network effects (though that’s true), but because an AI-native platform that has accumulated private data across millions of transactions and thousands of edge cases will have a compounding advantage that makes “leapfrogging” impossible. The moat isn’t just a feature set; it’s the quality built through long-term, high-standard operations in a domain that punishes errors.

This means the degree of market consolidation will surpass SaaS. I expect that in ten years, HR and payroll won’t be divided among twenty small players. Instead, two or three platforms will dominate most of the value, with a long tail of point solutions barely making a dent. The same pattern will play out across categories where complexity, data accumulation, and switching costs matter.

The companies at the top of these distributions will look very similar: founded by technically talented people with genuine product taste; built on AI-native architecture from day one; 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 abstractly, but I know exactly who they are because I am trying to become one myself.

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

Three years in, the bet is paying off. 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 accumulated compliance data, edge cases handled, and integrations built 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 guaranteed—nothing in a power-law world is guaranteed—but because the logic that brought us here is exactly what I’ve described throughout: see the truth, dig deeper than anyone else, build a high standard that sustains itself without external pressure, and persist long enough to see if you’re right.

The companies of excellence in the AI era will be built by those who understand: entry has never been scarce; insight (Insight) is. Execution has never been a moat; taste (Taste) is. Speed has never been an advantage; depth (Depth) is.

Power laws don’t care about your intentions. But they reward the right ones.