A market structure where there's opportunity for value creation at the level of individual companies and value destruction at the level of overall industries is a tricky situation.

Some of Microsoft's most profitable channel partners were pirates who ensured that Windows and Office would be standards even in developing-world countries where the sticker price for these products exceeded GDP per capita—at release, Windows 95 had a sticker price of $210 and Office '97, released in 1996, was priced at $599 if it wasn't an upgrade. China's GDP per capita crossed 800 in 1998; the market for full-priced software there was tiny

AI capabilities are improving far faster than our ability to intelligently reason about what these systems do, for example, but that sets a ceiling on productivity gains from using AI. If you can't reason about a system, it's hard to improve it.

It's exciting, but not especially fun. One big driver of this is the lag: the products that get announced now are the ones that have been in the works for months, and that's a long time in the AI world: "It seems like everyone is simultaneously extremely motivated and extremely close to burning out."

Microsoft/IBM deal.  Microsoft famously told IBM they had an operating system when they didn't in order to win IBM's business.  They then scrambled to find an OS they could quickly license from another software developer.  A number of lessor entrepreneurs would have said "sorry, we can't help you".  Of course, within a few years the OS franchise became the foundation of Microsoft's meteoric rise.

Google/Yahoo! deal.  Yahoo! outsourced search to Google and allowed Google branding on the Yahoo! homepage.  This lead to two outcomes: (a) Google had a massive spike in both traffic and data that is could use for analytics and to refine its search engine (b) The Google branding on the Yahoo! site caused non-early adopters to become aware of Google as a brand and drove significant traffic directly to the Google site.  Since Yahoo! paid Google for the service, it also partially funded Google as a business.

I think "waiting for a great idea" is absolutely the wrong way to "start" a company, and typically does not yield a startup.

Many "side projects" or internal tools are examples of this, as they have turned into great stand alone companies or products.  Blogger came out of Pyra Labs as an internal tool.   Github came out of an internal tool at a startup.  Yammer came out of Geni.   GroupOn came out of thePoint.

The next time you stop yourself to "wait for a good idea", don't stop yourself.  Go and build something that is a bad idea in a great market, or something small but is a product you want for yourself.  Iterate on it and keep pushing, and eventually you will find the bad idea has become a very good company indeed.

In previous posts I wrote about visionary entrepreneurs, and how to constantly ask how you can 10X your business.  However, many startups fail because the founding team thinks *too big* from day one, and doesn't take the time to really define the short term, immediate value their product or business provides.

Now, while it is crucial to think small and to build something that does not scale, you should always keep in mind the long term path for your business or product.   This path (and the end goal the path itself leads too) may continuously change as you learn more and as circumstances dictate.  However, you should constantly be thinking how to make what you have even bigger than what it is.

Implications: Why The YC Model WorksThis post was originally inspired by a question on Quora about whether the YC model can generate big successes, despite its emphasis on "small markets" and "low risk" products.  As I point out above, thinking small if often the key to building something truly huge.

A common VC question is "how does this scale?".  Ultimately, to build a large, long-term business you need to be able to take an approach and scale it.  However, it is OK to start with something that is manual and hands on.  The key is to have something that can be systematized and replicated over and over eventually.