Trends to Avoid When Founding a Startup

To elaborate on what the piece touches on but doesn't specifically say:

> You will need to make hard decisions for the sake of > the business. You can’t actually offer people anything > remotely close to lifelong loyalty or security, and it’s > dishonest to implicitly do so.

To be clear(er): You will have to fire people, and firing someone who thinks of themselves as a family member or who you think of as similar makes the whole thing much more painful. Further, it can make you, as a leader, hesitate when it's an action you really need to take ("but this person is like my brother - we'll make it work!").

At my most recent company, we took the opposite approach -- we all liked each other a lot, we worked well together, and we ate lunch together as a team, but at 6pm everyone went home to their own lives and families. The lines were clear, the understandings were there, and I think it was a much better way to run things.

The irony is that when Google was young, there was a different set of "destructive trends". They still included taking VC, but it was also common knowledge that:

1. You had to sell a physical product for money.

2. You had to build out all your infrastructure before you could bring a product to market.

3. Sales & marketing were more important than engineering.

4. (When they were hiring on their hypergrowth trajectory, 2001-2004:) The web is dead, and programming is a terrible career because it's all about to be outsourced to India.

Perhaps the meta-lesson is to do what other people are not doing. The point of markets is that they reward unexpected successes, where an entrepreneur serves a population with little competition, and that necessarily means thinking for yourself and ignoring the common wisdom. Fast.ai does an admirable job at that - their deep learning course is excellent - but taking their advice at face value is just as dumb as cargo-culting Google.

Regarding AI / ML as a service specifically. Many startups may find themselves facing the same issues plaguing IBM Watson. Deep learning considers trillion dimensional spaces. Most enterprise prediction problems are simply not of an astrophysical scale. And where domain specific expertise is required. Such as Google Maps use of CV in rendering highres satellite image data to SVG for the browser. Simply having grad student level familiarity with OpenCV might not be sufficient.

A Year of Google & Apple Maps (warning: bandwidth & cpu intensive)

https://www.justinobeirne.com/a-year-of-google-maps-and-appl...

It's mind-boggingly to me that this can be an option that people have never considered but I totally believe you. I've seen people have the "epiphany" that companies can spend revenue in much the same way they spend VC money. I guess their model is that only after that money is distilled into an up-and-to-the-right graph and put in front of VCs and they give you money do you actually get something you can spend.

A better lesson might be: stumble upon a phenomenal river of gold and dive in head-first.

Unfortunately, rivers of gold are rare. Which is why the rest of us are stuck with mundane stuff like marketing units of product or service to sell at a profit.