We're still missing several key ideas necessary for building AGI. How can we use a system's understanding of 'thing A' to learn 'thing B'? How do we build curious systems? How do we train a system to discover the deep underlying causes of all types of phenomena — to act as a scientist? But given enough hardware to run the relevant experiments in 7-10 days, history indicates that the right algorithms will be found, just like physicists would quickly figure out how the universe works if only they had a big enough particle accelerator.

To be in the business of building safe AGI, OpenAI needs to: 1. Have the best AI results each year. 2. Increase our GPU cluster from 600 GPUs to 5,000 GPUs ASAP. As an upper bound, this will require a capex of $12M and an opex of $5-6M over the next year. Each year, we'll need to exponentially increase our hardware spend, but we have reason to believe AGI can ultimately be built with less than $10B in hardware. 3. Increase our headcount: from 55 (July 2017) to 80 (January 2018) to 120 (January 2019) to 200 (January 2020). 4. Lock down an overwhelming hardware advantage.

2/3/4 all will ultimately require large amounts of capital. If we can secure the funding, we have a real chance at setting the initial conditions under which AGI is born. Increased funding needs will come lockstep with increased magnitude of results. We should discuss options to obtain the relevant funding, as that's the biggest piece that's outside of our direct control.