<img src="https://media.daily.dev/image/upload/s--O4uPjmaw--/f_auto/v1760983256/ugc/content_060ff87e-ad57-460d-a0a7-8c340c055685?_a=BAMAK+ZW0" alt="image">
yesterday morning started like every other chaotic sagea day. i was staring at a server log, trying to figure out why one of our experimental MoE models kept failing halfway through inference. it’s holiday season, so the guys and i are relatively less active for the week. the error messages didn’t make sense. the code made sense. the GPU was fine. the team was scattered across time zones. and me,sitting at my desk, sipping cold coffee that had been reheated twice, wondering how a single misconfigured batch could ruin three days of work. it was absurd. at one point i laughed at how small things can feel like life-or-death crises when you’re a tiny ai company trying to move fast.
moments like that are constant reminders of the reality of building small. every byte of compute, every gig of storage, every microsecond of latency matters. it’s messy, frustrating, exhilarating, and illuminating all at once. in those hours, i was acutely aware that the big labs; openai, meta, anthopic, google, have the luxury of scale (totally not jealous). they throw extra GPUs, extra engineers, extra time at problems. a small mistake is just a blip in their system. for us, a small mistake can ripple across the entire team, burn so much money. but the upside is that we move fast. every iteration teaches us something meaningful. every fix, every tweak, every small victory compounds. we have clarity of ownership, responsibility, and execution that massive labs simply can’t replicate.
<a href="https://basab.bearblog.dev/why-the-next-big-thing-wont-come-from-silicon-valley/" target="_blank" rel="noopener nofollow">read full post here</a>

Basab Jha

A founder of a small AI company reflects on the challenges and advantages of building at a smaller scale compared to major tech labs. While large organizations like OpenAI and Google can absorb mistakes with abundant resources, smaller teams face higher stakes with every error but benefit from faster iteration cycles, clearer ownership, and meaningful learning from each decision. The piece explores how resource constraints force efficiency and agility that massive labs cannot replicate.

why the next big thing won’t come from silicon valley 20 Oct, 2025