The Fast Lane to Mediocrity

Sometimes speed is just impatience disguised as ambition.

Nobody in Silicon Valley cares what you did a decade ago. Even more so now, when a month in AI time can feel like years in SaaS time.

This ecosystem runs on obsession with the future. It doesn’t matter if you started a company twenty years ago and took it public. If you’ve stopped building, you’re forgotten. There’s an egalitarianism here which I haven’t found anywhere else in the world. That quality is Silicon Valley’s superpower.

But lately, I’ve watched it curdle into something destructive.

The average tenure at startups keeps compressing. I see brilliant researchers cycling between labs every eighteen months, never staying long enough to push past the frontier they wanted to explore. Founders wind down companies after a year, not because they tested the idea and lost conviction, but because the initial excitement wore off and something shinier appeared. I see engineers treat career progression like a video game. Collect the title, vest the cliff, move to the next stage.

To be clear, there’s good speed and there’s bad speed.

Good speed is table stakes. It’s strategic velocity, when you take fast signals seriously and pivot from stagnant ideas. Learning cycles get compressed and execution compounds, getting you from insight to iteration before the world has caught up.

Bad speed is impatience that pretends to be ambition. It optimizes for novelty over depth and doesn’t let you sit long enough inside the unglamorous stretch where the real edge is formed. Because the truth is that the frontier rarely yields itself in the first six months.

The danger today is that AI’s pace can make constant reorientation look like progress. Every week brings a new model, a new lab, a new interface, a new consensus. Reorientation at good speed can be strategic. But when everyone is perpetually restarting at bad speed, nobody stays long enough to discover something non-obvious. And increasingly, that’s what bad speed looks like: an ecosystem optimized for perpetual beginnings, but allergic to the endurance required for real breakthroughs.

The harder skill now is discernment, knowing when a pivot reflects genuine new information and when it’s simply fueled by fear, impatience, or the seduction of momentum itself.

AI tools make it dangerously easy to generate forward motion. You can prototype faster, test faster, ship faster, and convince yourself faster that you’re on the right path. But lower friction can also mean accelerating false starts and finding yourself in a local maxima.

Finding the right footing at the very beginning matters more than ever. In a world where it’s cheap to build almost anything, the real edge is choosing what is actually worth building and staying with it long enough to learn something the market doesn’t know yet.

Why Depth Always Beats Bad Speed

You don’t really understand a problem until you’ve lived inside it for a while. Trust with a team isn’t built in a quarter. Neither is the kind of institutional knowledge that lets you see second- and third-order consequences before they happen. The most valuable professional assets (judgment, relationships, domain expertise) compound. Cut the timeline short and you never reach the part of the curve where the returns become extraordinary.

When I visited hard-tech companies in El Segundo, where teams are building electric boats, nuclear reactors, and space habitats, what struck me wasn’t just the ambition. It was the time horizon. Conversations there stretch decades, not quarters. Nobody talks about disruption. They talk about what needs to exist and the painstaking work of figuring out how. There’s a seriousness to that kind of building that you lose when you’re optimizing for eighteen-month cycles. It reminded me of old-school Silicon Valley, before everything became a show, when people built things because someone had to build them.

If you only spend a short amount of time on a project, the most you can do is modify an existing solution. You can make something faster, cleaner, cheaper, more polished. But genuine novelty demands a different kind of compounding. You have to compound toward creation, not toward endless modification of what already exists.

In a world where AI is compressing the time it takes to execute, compounding matters more, not less. When powerful tools can handle more of the building, the bottleneck shifts to knowing what to build and why. That kind of judgment doesn’t come from a weekend with a new model. It comes from years of accumulation.

We live in fast times. AI is rewriting the assumptions underneath entire industries. The temptation to optimize for optionality is completely rational. But grit, resilience, and loyalty will be eternal traits of the best people. No amount of speed changes that.

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