Sunday Curations – Andrej Karpathy, AI & Talent
Deep Dive into AI and Talent
Hey there! After a bit of a hiatus, we are adding Sunday Curations to our Blog to avoid having too many links flying around (and Substack is getting really good!).
This week, we have some highlights from from across AI and Talent. Enjoy!
1. Fascinating discussion between Andrej (Ex-Director of AI at Tesla) and Lex.
The whole conversation is worth your time. Here are our highlights, with some great thoughts and frameworks around AI, building organisations, and productivity:
“The best part is no part”:
[on whether to use more than visual sensors for autonomous driving]
Andrej:
You would think that these sensors are an asset to you. But if you fully consider the product in its entirety, the sensors are actually a liability. […] these sensors aren’t free, you need to have supply chain, people procuring it […] they can hold back the line in production.
Elon is really good at simplify, simplify, simplify. The best part is no part. And he always tries to throw away things that are not essential because he understands the entropy in organisations.
“Fight Entropy”:
Lex: What did you learn […] from Elon Musk?
Andrej: I think the most I learned is about how to run organisations efficiently […] and how to fight entropy in an organisation.
[…]
He hates meetings, he tells people to skip meetings if they are not useful. He basically runs the worlds biggest startups.
[…]
Lex: What do you think is the secret to maintaining the startup culture in a company that grows?
Andrej: I do think you need someone in a powerful position with a big hammer. Like Elon, who is like a cheerleader for that idea and ruthlessly pursues it. If nobody has a big enough hammer, everything turns into committees, democracy within the company, process, talking to stakeholders, decision-making, everything just crumbles.
If you have a big person, who is also really smart and has a big hammer, everything moves quickly.
“10x Problems”:
Andrej: I wouldn’t say that setting impossible goals is a good idea but setting ambitious goals is a good idea. I think there is what I call sublinear scaling of difficulty, which means that 10x problems are not 10x hard. Usually a 10x harder problem is 2-3x harder to execute on.
“Make revenue along the way”:
Andrej: How can I set up the product roadmap so that I’m making revenue along the way? I’m not setting myself up for a zero : one loss function where it doesn’t work until it works. You don’t want to be in that position.
You want to make it useful almost immediately and then you want to slowly deploy it at scale. […] You wan’t to improve the product incrementally and you want to make revenue along the the way. That is extremely important. Otherwise you can not build these large undertakings, it just doesn’t make sense economically and also from the point of view from the team working on it, they need the dopamine along the way.
“Load problems into your working memory”:
Andrej: You need to build some momentum on a problem […] You need to load your RAM, your working memory, with that problem. And then you need to be obsessed with it when you are taking a shower, when you are falling asleep. You need to be obsessed with the problem, and it’s fully in your memory, and you are ready to wake up and work on it.
[…]
There is always a huge fixed cost to approaching a new problem. You are not at a point where you can be productive right away, you are facing barriers. So its about removing barriers and having the problem fully loaded into your memory.
(for more see the discussion in the Discord)
2. More AI rabbit-holes: Full Stack vs Infrastructure as a Service, Start-Ups vs Incumbents
Machine learning: go full stack or go home by Nathan Benaich
ML is a relatively nascent industry with rudimentary tooling that is subject to rapid iteration. When this is the case, it’s often more efficient from a value creation standpoint to go the extra mile to become full-stack and control the value chain. To quote Henry Ford: “If you want it done right, do it yourself”. In contrast, when a market is mature, buyers are educated enough to outsource non-core functionality to third parties. In exchange for tolerable subscription fees, the buyer gains operational agility and improved overall product performance. This led to the success of API-first platforms like Twilio (communication), Stripe (payments) or Algolia (search). For ML this is likely to be many years away. Let’s not forget that It took the automobile and computer industry decades to disaggregate their supply chain.
AI: Startup Vs Incumbent Value by Elad Gil
this wave of AI applications seems to do best in markets where:
There are highly repetitive, highly paid tasks (code, marketing copy, images for websites etc)
Imperfect fidelity is fine, as you have a human in the loop who wants to review the items (which creates a nice feedback loop or future training set).
Human in the loop is not necessary, but seems to be a common feature to date.
Workflow tools do not exist or are weak for the use case, so the AI features become a core and useful part of a broader workflow tool Summarization or generation of text or images is useful for the product application - this is enabled in a high fidelity way by new AI tech in a way that did not exist before.
3. Talent: How to identify outliers
The book Talent by Tyler Cowen and Daniel Gross is fantastic. For anybody interested in hiring, finding outlier people and good conversations in general it should be a must read. Here is a snippet of one just one page, packed with great interview questions:
Here are some questions that not only will elicit stories but also might yield relatively interesting answers:
• "How did you spend your morning today?"
• "What's the farthest you've ever been from another human?"
• "What's something weird or unusual you did early on in life?"
• "What's a story one of your references might tell me when I
call them?"
• "If I was the perfect Netflix, what type of movies would I recommend for you and why?"
• "How do you feel you are different from the people at your
current company?"
• "What views do you hold religiously, almost irrationally?"
• "How did you prepare for this interview?"
• "What subreddits, blogs, or online communities do you enjoy?"
• "What is something esoteric you do?"
Happy curating!