Running towards “the suck”

The most subtle kind of failure is the one that hides behind competence. You’re doing well enough to be seen as good, but not enough to feel the stretch. You’re failing to fail.

Real growth begins where feedback starts to hurt. Progress demands discomfort, but our reptile brain is wired to avoid it. We often choose fluency over friction, familiarity over feedback, comfort over clarity; and in doing so, we stop evolving.

I was reminded of this viscerally through tennis. When I first took up the sport three years ago, my improvement rate was steep. Every week, a new shot landed, a new motion clicked. But then the curve flattened abruptly. I could rally cleanly in training but collapsed in matches. All the training, straight out the window; it just would not translate in action. Instead, it translated to a few broken rackets.

That phase lasted years. Painful, repetitive, unrewarding, and often downright revolting. Similarly with windsurfing, I have thought of quitting many a times; “why do I always pick sports that produce more frustration than they do joy?” I would type into GPT.

The only thing that kept me going, besides a genuine love for act of playing, was a quiet belief that something underneath was rewiring; that if I only endured “the suck” for a little longer, I would eventually climb into the next level of the learning curve. And so instead of playing tentative in matches to hold my own and scrape a W by, I decided to suck some more instead.

And one random Tuesday afternoon, it happened. Somewhere along the line, the patterns had inverted. Instead of scrambling to think of what to do next, I started flowing. Time dilated, my form maintained, and the points started racking up. The system finally integrated.

That’s when I think I started climbing the slope of enlightenment.

The same mechanics apply to work, to relationships, to self-development. The only sustainable path to getting good enough, then getting good, then becoming great, is learning to get over failure faster than it accumulates; standing your ground as it tries to beat you into submission.

The moment you stop failing, you’ve stopped learning. And that’s the real failure.

The second derivative of conflict resolution

I wrote this after noticing a pattern in how good teams and good relationships evolve. It’s not that they avoid conflict, but rather they metabolize it faster every time. The model that emerged was a mathematical one; relationships as learning systems, their health measurable by the slope of repair.

One of the beliefs I hold most firmly is that the best predictor of success in any relationship, whether romantic, friendship, or team, is the second derivative of conflict resolution.

By conflict, I don’t mean shouting or drama. I mean any point where expectations diverge and two internal models of reality collide.

A great relationship is not one without friction; it’s one where friction resolves faster and cleaner over time. The first time you face conflict, it takes a day to recover. The second, six hours. The third, ninety minutes. The fourth, twenty. After that, the curve asymptotes toward zero.

That curve, the rate at which repair accelerates, is what I call the second derivative of conflict resolution (SDCF). It measures not harmony, but learning. Every disagreement, once resolved, adds a building block to shared understanding, which means you don’t have to fight the same fight twice.

This reframes relationship quality from being about harmony to being about adaptive efficiency. The first derivative of conflict resolution shows how quickly a single conflict resolves (i.e. the velocity of recovery). The second derivative shows how that velocity improves over time (i.e. whether the system learns). In simpler terms, what matters isn’t how fast you repair once, but how fast you get better at repairing.

If over successive conflicts the first derivative (recovery speed) becomes more negative more quickly, meaning repair happens faster each time, then the second derivative across conflicts is positive in the direction of learning. Conversely, when the second derivative flattens or turns negative (i.e.when conflicts take just as long, or longer, to resolve) it’s a sign of structural incompatibility. The system isn’t learning. What looks like “communication problems” is really the absence of adaptation.

Most people assess relationships based on emotional tenor; how good they feel or how frequently they argue. But the SDCF model suggests something different; conflict isn’t a sign of failure, but rather it is signal. Each disagreement surfaces new data about boundaries, needs, and blind spots.

In that sense, the counterintuitive truth is that the path to relational strength runs straight through conflict.

Every repair is a form of learning; every argument, a test of how well two people can turn friction into shared understanding. What ultimately defines longevity is how efficiently that learning compounds, and how each conflict leaves the system slightly more aligned than before.

What we often call being “well-matched” is really just phase alignment under low stress. A relationship that truly compounds is one where both people elevate each other through conflict.

Common sense suggests compatible people should recover faster, but the inverse is also true; people who recover faster become more compatible. The variable you can actually control is the learning rate; the slope of repair.

It’s worth highlighting that awareness itself changes the shape of the curve. Most relationships operate unconsciously along their derivative, unaware of whether repair is accelerating or stalling. But once you can see the curve, you can influence it. Awareness reigns in entropy, and replaces drift with structure.

That awareness can have two outcomes, both good. It can either help a relationship move to a higher level of coherence, or reveal that the system has reached its limit, that its slope will never meaningfully improve, and thus allow it to end cleanly. Both outcomes are infinitely better than unconscious decay.

This lens changes how you think about relational “success.” It’s not about avoiding arguments or achieving constant peace. It’s about whether repair gets faster and deeper each time. Whether the feedback loop between conflict and understanding tightens. Whether the relationship compounds.

It also applies beyond the personal. Teams, partnerships, and organizations all have a SDCF. The best companies aren’t those without disagreement but those whose disagreement resolution curve steepens with time, as they learn to metabolize tension into clarity.

A team’s greatness isn’t its lack of internal debate, but how fast it integrates disagreement into improved operating norms. Cultures that avoid conflict decay, while cultures that metabolize it evolve.

If you believe this, then conflict stops being something to fear. It becomes diagnostic. You run toward it, because every repair is a data point on the curve. A chance to move the derivative in the right direction.

That, to me, is what separates fragile from enduring systems, whether personal or collective. It’s not how they avoid stress, but how quickly and gracefully they repair after it. The rest is just noise.

Coase in the age of code

I recently came across an essay I wrote in university in 2011, about Ronald Coase and the theory of the firm. It was dry, academic, and deeply curious about a question that still feels pertinent today; why do firms exist at all?

Back then, the answer felt settled. Coase had explained that firms emerge because organizing through the market is costly; contracts take time, negotiations add friction, and information is imperfect. When it becomes cheaper to manage people internally than to transact externally, a firm is born. The invisible boundary of the firm, he said, lies at the point where these two costs meet.

Reading that old paper now, after a decade spent around blockchains, DAOs, and “trustless” financial systems, I’m struck by how cyclical the question feels. Crypto’s grand promise was to eliminate the very frictions that gave birth to the firm; to replace bureaucracy with code, management with incentives, contracts with consensus.

If Coase’s firm existed to minimize transaction costs, and those costs could be automated away, then perhaps the firm itself could be dissolved.

That was the dream. But it didn’t happen.

When you replace contracts with smart contracts, you still need judgment: what counts as a valid state, what to upgrade, when to fork. When you remove hierarchy, you rediscover governance, only now it’s slower, noisier, and happening in public.

Bounded rationality didn’t vanish with blockchains. It simply migrated to Discord. The same cognitive limits that once defined the borders of the firm now define the borders of the network.

Agency problems persist too. Token holders delegate to committees, multisigs, or core teams. Power concentrates. Decision-making slows. Coordination becomes its own cost center. Every “decentralized” organization ends up rebuilding a managerial layer; sometimes reluctantly, sometimes accidentally.

The irony is that Coase’s logic still applies: a network expands until the cost of coordinating one more decision exceeds the cost of spinning up a new one.

Coase described the firm as an economic structure. But over time it became something deeper: a social technology for minimizing collective error. Firms exist not just to cut transaction costs, but to give a group of humans a shared model of the world, a rhythm, a sense of who decides what.

Even if code can settle value instantly, humans still need slower systems for context, accountability, and trust. The firm endures because it optimizes for judgment, not just execution.

This is why most DAOs still look suspiciously like companies. They may route capital through tokens, but their structure—small cores, delegated authority, decision bottlenecks—echoes the same patterns Coase was describing in 1937. It turns out coordination is a harder problem than trust.

What has changed is where the boundary lies.

The minimum viable firm used to require offices, payroll, and legal scaffolding. Now it can exist as a wallet, a few passkeys, and a group chat. The cost of coordination has collapsed — not to zero, but low enough that the firm can shrink to its essence: a system for allocating capital and attention toward a shared goal.

That collapse in cost doesn’t end the firm; it atomizes it. The future looks less like one monolithic organization and more like a mesh of smaller, temporary ones.

In that sense, blockchains didn’t abolish Coase’s world; they made his boundary dynamic.

Coase saw transaction costs as economic. What he couldn’t see from 1930s London was that information processing itself would one day be the scarce resource. The true constraint on coordination is no longer contract enforcement, but rather comprehension.

A modern firm isn’t just a bundle of contracts; it’s a bundle of cognition. Its size is limited not by the cost of managing people, but by the bandwidth of shared understanding among them.

Technology keeps lowering the cost of transaction, but it doesn’t raise the ceiling of comprehension. We can move money instantly, but aligning meaning still takes time. That’s the paradox of the digital firm: infinite speed, finite sensemaking.

When I wrote that early essay, I thought of the firm as an object, a structure bounded by cost. Now I think of it as a living organism bounded by cognition. The question is no longer why firms exist, but how fluid they can become before they stop being coherent.

Coase explained why we built firms. Crypto reminded us why we still need them.