The answer is simple:
- Fire 5% of all staff to cover the AI costs
- Encourage everyone to use more AI to keep up
- Fire 5% of all staff to cover the AI costs
@IAmSandroSaric It’s actually the first move. The alternative is dumping your time (and money) into 1 on 1 outreach to get minimal traffic when your product is at its weakest.
Save those high value contacts for later and spend $100 a month on paid ads for brand recognition while it matters.
@DivTall@1Umairshaikh Holy LARP, CTOs do NOT get 50%. I’m happy with 5% if you can get distribution down. Give 30% to a CMO for all I care; just don’t fumble the bag.
parents: "move out"
girlfriend: “quit being such a loser”
boss: "work harder"
claude: "uber for dogs (the dogs are the drivers) is a great idea, you should absolutely pursue it"
@NTmoney Agree, but this is exactly what crypto people have been saying for the past 4 years to avoid holding each other accountable, which I'd argue is also loser energy.
This narrative is a big reason why scams in the space are running rampant.
The main problem with buying into the loudest narrative is the narrative can turn just as quickly the other way. There is no real belief.
Eventually we learn that a loud narrative is a countersignal. The guys pumping don’t really believe and they are trying to dump on you.
Then we learn to believe in something.
I am so thoroughly convinced that anyone who thinks AI 100x's their output is a liar or a lunatic.
You are telling me you can make 1 years worth of decisions in 3.65 days? Let alone describing those accurately and coaxing the result from the AI... (1.8 days european time)?
@sweatystartup Ai absolutely works in the right hands. Creating internal tools and automations now takes hours and can save you weeks. Not even considering compounding effects and the potential of freed up labor for other more important things.
Only real devs are making meaningful moves tho
Stop using Claude Code for git pull.
Stop using Redis as your primary database.
Stop using microservices for 3 backend endpoints.
Stop using RAG for 20 PDFs.
Stop using Terraform for a single VM.
Stop using Kubernetes for a todo app.
Stop using Kafka for sending emails.
Stop using event-driven architecture for everything.
Stop optimizing for 1 million users when you have 17.
Stop adding caching before measuring performance.
Stop choosing databases before understanding access patterns.
Stop adding an LLM when a search box would work.