I was scrolling X until 2 AM last night — Silicon Valley's AI scene blew up

I was editing an AI customer service prompt for a client when X suddenly exploded with Silicon Valley folks shouting "we're done for."

What this 'awkward moment' actually is — three real people in three real places crying out

Short version: AI's months-long sprint just slammed into a few walls — tighter chip export controls, several big players cutting AI projects, and the whole industry is recalibrating how fast they burn cash.

One indie dev I follow, Daniel, posted on Discord that his client service's API costs jumped 40% this month, and he's debating whether to raise prices. There's also Sarah from a 3-person AI resume optimization team in San Francisco — she wrote on Indie Hackers: "Our biggest risk this year isn't the product, it's the vendor."

What this means for your today

Money: $0. Time: 1-2 hours. Barrier: being able to read a credit card statement. First step: pull up last month's bill and circle the total spend on usage-based AI tools (OpenAI, Anthropic, that kind) — no tech jargon needed.

I've made this mistake too: last year one of my projects was heavily dependent on a single tool, and when it suddenly shut down I spent a week scrambling to swap it out. Ever since then I've kept one habit — at least one backup for every core function.

If all your AI workflows are stacked on one vendor right now, this is the moment to spend an hour asking "how long can I survive if they raise prices 30%?"

Advice by stage: just starting / have 1-2 clients / scaling up

Just starting (no clients yet): I wouldn't rush. I'd wait until your first client lands before picking tools — get the business running first.

Have 1-2 clients (monthly AI cost under ~$30): if you're worried about single-vendor risk, I'd list out a "tool dependency inventory" and note how long you can survive each one raising prices 30%. 1 hour is enough.

Scaling up (monthly cost over ~$140 or more than 10 clients): I'd start keeping 2 backup vendors and run at least one A/B test on a core function. It's keeping an escape hatch — not overengineering.

Last thought: this isn't that scary — what's scary is pretending it doesn't exist. Our small-team advantage is we can turn fast — when big players can't keep burning, demand actually flows our way.