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Adventures in Improving AI Economics

a16z.com

1–10 of 80 posts

Re: Adventures in Improving AI Economics

#3
Good analogy about discovery of Pharma molecules.

It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working...

Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.

Re: Adventures in Improving AI Economics

#4

Good analogy about discovery of Pharma molecules. It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working... Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.

Perhaps. It seems it’s still an open question whether AI is just about memorizing your data, or can it actually make reliable decisions during previously unseen scenarios.

Have we already observed, or collected, all that is possible in the “driving” world?

Re: Adventures in Improving AI Economics

#6
a16z has a podcast where they explored gross margins a month back. The panel called out AI as an example of a software business that has a high likelihood of not having standard SaaS margins (Most of the panel thought this could be a limitation).

The podcast is nice because I think it holistically explores gross margins in a way that you start to understand how it might impact AI as a viable primary business model and valuations related to companies who that is the case for. Quite complementary to the article.

Might be interesting to people who are interested in this article: https://open.spotify.com/episode/79lJCrHB3nBn1qXCxKA5s7?si=R...

Re: Adventures in Improving AI Economics

#7
post #4

Good analogy about discovery of Pharma molecules. It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working... Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.

Perhaps. It seems it’s still an open question whether AI is just about memorizing your data, or can it actually make reliable decisions during previously unseen scenarios. Have we already observed, or collected, all that is possible in the “driving” world?

Most self-driving companies use simulations to see how the model performs in unseen scenarios.

Re: Adventures in Improving AI Economics

#8

Good analogy about discovery of Pharma molecules. It’s really fun to think about the fact that Tesla has more than enough data to unlock autonomous vehicles, but all that is missing is the correct AI architecture to get it working... Who will figure out how to code that? Will it be a breakthrough, or can sub-optimal architectures eventually reach equilibrium with 10x or 100x the amount of time/data processing.

I'm sorry, but is that true, that Tesla has enough data to unlock autonomous vehicles? My experience is that until you get an ML model to do X, you never know if you have enough data to train it to do X. Or is that just your opinion, that they don't need more data?

Re: Adventures in Improving AI Economics

#9

"Andreessen Horowitz (known as "a16z") is a venture capital firm in Silicon Valley, California" In case anyone was as confused as I was about what a16z means - it's just the company not a new abbreviated term related to AI.

Also for anyone too young to remember the dot com boom, the founding partners (Marc Andreessen and Ben Horowitz) are some of the legendary techies from that cycle (of Netscape and LoudCloud/Opsware fame, way ahead of their time)

Re: Adventures in Improving AI Economics

#10
> This is the crux of the AI business dilemma. If the economics are a function of the problem – not the technology per se – how can we improve them?

The article focusses on the costs of resources to build a model (annotated data + compute) but the economics are also affected by the ongoing cost of making a prediction error. False positives and false negatives usually have a different cost and each user might have their own preferences:

e.g. "show me all the content that's a bit relevant" vs "show me just the content that's really relevant".

If you can write out the loss function in $$$ terms not just accuracy, then you're closer to either abandoning the problem or finding a profitable AI model.

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