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The evolution of competitive advantages: 1900s: Economies of Scale 2000s: Network Effects 2020s: Data Advantages 1/14
Economies of Scale: In the 1900s, the dominant companies benefited from scale. Standard Oil's size enabled Rockefeller to negotiate railroad rebates, acquire early tank cars, etc. He could profitability sell oil at a price point lower than his competitors could produce it. 2/14
Economies of scale aren't as powerful in software because the underlying infrastructure components - internet, servers, etc. - are generally shared resources. The cost of compute isn't that much different for BigCo and SmallCo. And scale is available instantly. 3/14
Network Effects: A study by @nfx found that 70% of value creation in tech is driven by network effects. 4/14
A network effect exists when one user creates value for many other users in a system (think Facebook, Twitter, etc.). The larger the network, the more value it has, and the harder it is to replicate. This exponential value creation can be modeled with Reed's Law: 5/14
Data Advantages: Contrary to common belief, most data is useless or of little value. But in a few circumstances, data is gold. As products embed AI, large quantities of high-quality training data are needed to train models. 6/14
The companies with the best training data will generally produce the most performant models. Those companies will attract more customers who will generate even more data. 7/14
This creates a flywheel effect where the robustness and performance of a system increase exponentially over time. The result is a competitive moat that strengthens with scale. 8/14
E.g. in autonomous driving, companies that are already collecting real-world data will likely have the most performant models in the short-term. That performance will probably attract more customers who collect more data, improve the models, and widen the competitive moat. 9/14
Two things to consider when thinking about data advantages: 1) Is the data hard to acquire? 2) Where is the data-value asymptote? 10/14
Autonomous driving data is hard to acquire - you need many cars on the road operating in various conditions. Facial images (for face recognition) are easy to acquire from public datasets (labeled faces in the wild, etc.), so the value of facial data is implicitly lower. 11/14
Some AI problems, like autonomous driving, require massive amounts of training data. It will probably be a long time before autonomous driving data reaches a point of diminishing returns. 12/14
Other AI problems require little training data. The classic "handwritten number recognition" problem can probably be solved with just a few thousand labeled images. In this case, the data-value asymptote is low. Adding more data would only marginally increase performance. 13/14
Will data advantages drive the next 70% of value creation in tech? Maybe. 14/14

My Notes:

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