I didn’t come to Solana looking for speed.
What caught my attention was something quieter: the way it treated execution as a systems problem rather than a bottleneck to be tolerated.
Up to this point, most of my intuition had been shaped by Ethereum. Not just its tooling or ecosystem, but its underlying assumptions. Execution is scarce. Ordering is global. Latency is something you design around, not something you remove.
Solana questioned those assumptions directly.
Ethereum Taught Me to Respect Scarcity
Ethereum’s model is careful by design.
Transactions are ordered, executed one by one, and replayed identically by every node. This makes the system easier to reason about. State is shared. Composability is implicit. If something executes, everyone agrees on how and when it happened.
But the cost of this simplicity becomes visible as usage grows. Congestion isn’t an edge case — it’s structural. Latency isn’t a temporary inconvenience — it’s part of the model.
As a developer, you internalize this quickly. You build defensively. You expect delays. You assume contention. Execution becomes something you compete for rather than rely on.
For a long time, that felt like the natural shape of blockchains.
Solana Starts From a Different Premise
Solana doesn’t begin with the assumption that everything must happen in a single line.
Instead, it asks a different question: what actually needs to be ordered, and what doesn’t?
If two transactions don’t touch the same state, forcing them through the same execution lane is unnecessary. Modern systems don’t work that way. CPUs don’t. Operating systems don’t. Databases increasingly don’t.
Solana treats transaction execution more like scheduling than serialization.
That one shift reframes the entire problem.
Parallelism Changes Developer Intuition
Once you allow parallel execution, speed becomes a consequence rather than the goal.
Lower latency follows naturally. Confirmation times shrink. Feedback loops tighten. On-chain interactions start to feel less like submitting requests to a distant system and more like interacting with a live one.
This doesn’t just improve performance — it changes how you think.
Instead of designing around retries and uncertainty, you start designing around responsiveness. Instead of assuming congestion, you assume throughput unless proven otherwise.
That’s a subtle but meaningful change in mindset.
Explicit State Becomes a Feature, Not a Burden
There’s a trade-off here.
Parallel execution requires clarity. You have to be explicit about what your transaction reads and writes. Ambiguity limits concurrency. Carelessness creates conflicts.
At first, this feels like friction. Ethereum largely abstracts this away.
But the more I look at it, the more it resembles how high-performance systems are built elsewhere. Explicit dependencies aren’t a weakness — they’re how systems scale predictably.
Solana makes state visible. That visibility becomes part of the contract between the developer and the runtime.
This Isn’t About Replacing Ethereum
It’s tempting to frame this as a competition, but that misses the point.
Ethereum and Solana are optimizing for different things.
Ethereum optimizes for minimal assumptions and universal composability.
Solana optimizes for execution efficiency and throughput under load.
Neither approach is free. Both encode values.
What matters is that Solana demonstrates something important: congestion and latency are not inevitable properties of on-chain systems. They are design outcomes.
A Shift in Expectations
What ultimately pulled me toward Solana wasn’t raw performance. It was the realization that execution doesn’t have to feel scarce by default.
If blockchains are going to support real applications — not just proofs of concept — execution has to be treated as something to engineer, not something to endure.
Solana doesn’t solve every problem.
But it opens a different design space.
And once you see that space, it becomes hard not to explore it.
