AI is bottlenecking on reads, not writes — and it's hitting Brown founders hard

Stack Overflow has a new post on the real bottleneck in AI workloads: it's reads, not writes. When you're pumping data into an LLM, you're writing tokens fast. But once the model is generating, you're reading those tokens back — and the memory subsystem chokes. The fix is simpler than people think: cache the outputs, batch the reads, and stop treating RAM like it's free.

For the folks running side hustles on a shoestring, this matters. The cousin who built a Shopify plugin for his wife's boutique, the auntie running a translation service from her kitchen — they're all hitting this wall now that AI is baked into everything. The big cloud providers sell you 1TB of RAM at a premium; the rest of us are learning to work around it. Aiven's acquisition of Flow AI is part of the same trend — people are realizing that storing and retrieving data efficiently is where the money goes.

Why this matters for us: the folks who can't afford the big cloud bills are the ones who'll figure out how to run AI on a $20 VPS, and they'll share those tricks with la gente.