3 posts tagged with "startups"

Every week someone publishes a new piece about how AI is coming for jobs. White-collar jobs, creative jobs, coding jobs, legal jobs. The tone oscillates between breathless excitement and low-grade dread, and the argument always comes down to the same thing: look at what these models can do.

And to be fair — the things they can do are genuinely impressive. Summarize a 200-page contract. Write production code from a vague description. Explain quantum entanglement to a ten-year-old. Pass the bar exam. These are tasks that humans find hard. Real hard. The kind of hard that takes years of training and still produces mistakes.

So I understand the anxiety. I really do.

But here is what the discourse consistently misses: the inverse is also true, and it is spectacular.

Here is a thing nobody tells you when you are preparing for your first investor meeting: the skills you need have nothing to do with finance.

They are the same skills you used to convince your partner to go to Tuscany instead of the beach. The same ones you deployed when you talked your team into a complete architecture rewrite. The same ones your ten-year-old uses when they want a later bedtime.

Every pitch is the same at its core. Someone has a resource — money, time, attention, credibility — and you are asking them to bet some of it on you.

There are roughly four ways a company tries to get AI into production. Most founders and executives default to one of the first three without realizing it — and each one has a predictable failure mode that shows up right around the time it matters most.

This post runs a SWOT analysis on all four. The goal is not to declare one model universally correct. It is to make the tradeoffs visible before you commit.