Andrej is exactly right. We may wish it otherwise, but, in my and Andrej 's opinion, Tesla is the only path that
could even hope to hold a candle to Google. Even then, the probability of being a counterweight to Google is
small. It just isn't zero.
Begin forwarded message:
From: Andrej Karpathy <aka1J.1athY..@tesla.com>
Date: January 31, 2018 at 11:54:30 PM PST
To: Elon Musk <erm@1:1pacex.com>
Subject: Re: Top AI institutions today
Working at the cutting edge of Al is unfortunately expensive. For example, DeepMind's operating expenses in
2016 were at around $2S0M USD (does not include compute). With their growing team today it might be
~o.sB/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. In addition to DeepMind, Google also has Google Brain, Research, and Cloud. And
TensorFlow, TPUs, and they own about a third of all research (in fact, they hold their own Al conferences).
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 Al is primarily driven by systems - compute,
data, infrastructure. The core algorithms we use today have remained largely unchanged from the ~gos. Not
only that, but any algorithmic advances publish ed in a paper somewhere can be almost immediately re-
implemented and incorporated. Conversely, algorithmic advances alone are inert without the scale to
also make them scary.
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.
A for-profit pivot might create a more sustainable revenue stream over time and would, with the current
team, likely bring in a lot of investment. However, building out a product from scratch would steal focus from
Al research, it would take a long time and it's unclear if a company could "catch up" to Google scale, and the
investors might exert too much pressure in the wrong directions.
The most promising option I can think of, as I mentioned earlier, would be for OpenAI to attach to Tesla as its
cash cow. I believe attachments to other large suspects (e.g. Apple? Amazon?) would fail due to an
incompatible company DNA. Using a rocket analogy, Tesla already built the "first stage" of the rocket with the
whole supply chain of Model 3 and its onboard computer and a persistent internet connection. The "second
stage" would be a full self driving solution based on large-scale neural network training, which OpenAI
expertise could significantly help accelerate. With a functioning full self-driving solution in ~2-3 years we could
sell a lot of cars/trucks. If we do this really well, the transportation industry is large enough that we could
increase Tesla's market cap to high O(~100K), and use that revenue to fund the Al work at the appropriate
scale.
I cannot see anything else that has the potential to reach sustainable Google-scale capital within a decade.
-Andrej
From: Elon Musk <erm@rnacex.com>
Sent: Wednesday, January 31, 2018 2:07:15 PM
To: Andrej Karpathy
Subject: Fwd: Top Al institutions today
fyi
What do you think makes sense? Happy to talk by phone if that's better.
Begin forwarded message:
From: <erm@.~P-acex.com>
Date: January 31, 2018 at 2:02:37 PM PST
To: <gdb@.oP-enai.com>, <llyasu@.oP-enai.com>, Sam Altman <shg£.@)_ycombi nator.com>
Cc: <steller@.~P-acex.com>, <Shivon@~P-acex.com>
Subject: Fwd: Top Al institutions today
OpenAI is on a path of certain failure relative to Google. There obviously needs to be immediate and
dramatic action or everyone except for Google will be consigned to irrelevance.
I have considered the ICO approach and will not support it. In my opinion, that would simply result in a
massive loss of credibility for OpenAI and everyone associated with the ICO. If something seems too good to
be true, it is. This was, in my opinion, an unwise diversion.
The only paths I can think of are a major expansion of OpenAI and a major expansion of Tesla Al. Perhaps
both simultaneously. The former would require a major increase in funds donated and highly credible people
joining our board. The current board situation is very weak.
I will set up a time for us to talk tomorrow. To be clear, I have a lot of respect for your abilities and
accomplishments, but I am not happy with how things have been managed. That is why I have had trouble
engaging with OpenAI in recent months. Either we fix things and my engagement increases a lot or we don't
and I will drop to near zero and publicly reduce my association. I will not be in a situation where the
perception of my influence and time doesn't match the reality.
Begin forwarded message:
From: Andrej Karpathy <akaq;1athy_@tesla.com>
Date: January 31, 2018 at 1:20:42 PM PST
To: Elon Musk <erm@tesla.com>
Cc: Shivon Zilis <shivon@tesla.com>
Subject: Top Al institutions today
The ICLR conference (which is the top deep learning - specific conference (NIPS is larger, but more diffuse))
released their decisions for accepted/rejected papers, and someone made some nice plots that show
where the current deep learning/ Al research happens at. It's an imperfect measure because not every
company might prioritize paper publications, but it's indicative.
Here's a plot that shows the total number of papers (broken down by oral/poster/workshop/rejected) from
any institution:
- rb_poster
- rtJ_roj,oct
80 : - ro_oral
,, - ri>_worioshop
70
60
50 :
40
30 •
20
Long story short, Google is dominating with 83 paper submissions. The academic institutions (Berkeley/
Stanford/ CMU / MIT) are next, in 20-30 ranges each.
Just thought it was an interesting snapshot of where all the action is today. The full data is here:
httR:1/webia. Ii P-6.fr /~Qgjot/dataviz. htm I
-Andrej