Working at the cutting edge of AI is unfortunately expensive. For example, DeepMind's operating expenses in 2016 were at around $250M USD (does not include compute). With their growing team today it might be ~$1.5B/yr. But then Alphabet in 2016 reported ~20B net income so it's still fairly cheap even if DeepMind had no revenue of its own.

I also strongly suspect that compute horsepower will be necessary (and possibly even sufficient) to reach AGI. If historical trends are any indication, progress in AI is primarily driven by systems — compute, data, infrastructure. The core algorithms we use today have remained largely unchanged from the ~90s.

It seems to me that OpenAI today is burning cash and that the funding model cannot reach the scale to seriously compete with Google (an 800B company). If you can't seriously compete but continue to do research in open, you might in fact be making things worse and helping them out 'for free', because any advances are fairly easy for them to copy and immediately incorporate, at scale.

The most promising option I can think of would be for OpenAI to attach to Tesla as its cash cow. Using a rocket analogy, Tesla already built the 'first stage' of the rocket with the whole supply chain of Model 3. The 'second stage' would be a full self-driving solution based on large-scale neural network training, which OpenAI expertise could significantly help accelerate. I cannot see anything else that has the potential to reach sustainable Google-scale capital within a decade. —Andrej

Elon Musk forwarded this to OpenAI leadership, adding: 'Andrej is exactly right. Tesla is the only path that could even hope to hold a candle to Google.'