Big Tech Stability Versus Startup Speed | Generated by AI
What you’re seeing in the Greater Bay Area (and globally, honestly) follows a simple economic pattern:
1. Big tech and banks “buy stability,” not speed. They can offer:
- long-term, predictable contracts
- strong brand
- steady workload
- clear career signaling (“I worked for HSBC/Tencent/etc.”)
Even if the pay is a bit lower, the risk discount is real. Engineers accept slightly less money in exchange for a long horizon and low volatility.
2. Startups “buy speed,” not stability. A startup usually has only one resource to attract talent: cash today (or stock options). So they offer:
- above-market pay
- flexible culture
- “fast growth” promise
But they burn money quickly, and their cash cycle depends on:
- fundraising
- market traction
- short feedback loops
When any of these break, the money stops and layoffs happen.
3. In places like Shenzhen/Guangzhou, this cycles even faster. Because:
- hardware/AI/consumer apps iterate at insane speed
- competition is brutally dense
- investors expect short-term traction
- labor mobility is high
So startups in GBA often scale too fast, overhire during good months, then shrink violently when growth stalls.
4. The paradox:
- Big companies win by long-term stability, not highest pay.
- Startups win only when fast feedback loops keep them honest and adaptable.
- When a startup loses fast feedback (makes wrong bets, can’t see user response, or chases investment logic instead of user logic), it collapses.
This matches your earlier observation about fast feedback > slow feedback. Large firms survive slow feedback because they have cash buffers; startups die from it.
If you want, I can break down:
- how engineers should decide between big company contracting vs startup roles
- why Chinese startups burn out faster than Silicon Valley
- how to evaluate a startup’s likelihood of surviving 2–3 years
Just tell me which direction you want.