Jim Simons, the legendary mathematician and founder of Renaissance Technologies, just passed away. He lived a fascinating life, moving from academia to building one of the world’s greatest money-making machines in Renaissance’s flagship hedge fund.
I really enjoyed The Man Who Solved the Market as well, thanks for recommending it a while back!
I agree it would take a complete market transformation for Quant to work in Private Markets (that 50.1% edge you mentioned). I'm not sure it will ever come, but I'd like to see it! Could bring about a more just/fair PE environment.
Another provocative post. I would love to see a thoughtful analysis showing the kinds of problems where AI is likely to be useful.
As you note, the AI answer must be timely and clearly worth its cost or no one will pay. And the client—not the AI nerd—decides what counts as “useful.”
Useful compared to what? If existing methods are pretty good and the consequences of being wrong are small, then it’ll be hard to get much traction for an AI solution.
For example, say you would like to predict the solubility of a new small molecule drug candidate. I’m sure you could find a big data set of chemical structures and their solubility constants, and you could train an AI model to “discover“ the underlying causes of solubility. Quickly you would zero in on an AI model that successfully predicts the solubility of new molecules.
However, chemists already have molecular modeling software to do that, and more. And they also have beakers and water and if they want to, they can synthesize the new drug and drop it in the beaker, stir it, pour off the liquid, let it evaporate, weigh what’s left, and find the solubility that way! So they might pay something for your AI solution, or they may not depending on price and depending on how quickly you can deliver it and how accurate it is in relation to alternatives. Even if it’s better as you point out in your post, they still might not buy the AI if its answer isn’t timely.
A further constraint, and I would like to see proper research on this, is the size of the data that you need for AI to be able to pull out anything useful. I believe that there are problems in physical science where huge data sets are available,, but much in economics and business just doesn’t have the requisite scale. Activity data sets such as you had in your old company are very, very small in relation to those in physical science. For example, someone in my old company estimated that the number of synthesizable new drugs (using common chemical fragments and using common methods of synthesis) was about 10^26th potential molecules. Now that’s a dataset!
In contrast there are 3000 NASDAQ companies, so 20 years of closing price data is only 3000*20*250 is only 15 million data points. It speaks to how inefficient markets were that Renaissance was able to make any money at all!
I really enjoyed The Man Who Solved the Market as well, thanks for recommending it a while back!
I agree it would take a complete market transformation for Quant to work in Private Markets (that 50.1% edge you mentioned). I'm not sure it will ever come, but I'd like to see it! Could bring about a more just/fair PE environment.
Another provocative post. I would love to see a thoughtful analysis showing the kinds of problems where AI is likely to be useful.
As you note, the AI answer must be timely and clearly worth its cost or no one will pay. And the client—not the AI nerd—decides what counts as “useful.”
Useful compared to what? If existing methods are pretty good and the consequences of being wrong are small, then it’ll be hard to get much traction for an AI solution.
For example, say you would like to predict the solubility of a new small molecule drug candidate. I’m sure you could find a big data set of chemical structures and their solubility constants, and you could train an AI model to “discover“ the underlying causes of solubility. Quickly you would zero in on an AI model that successfully predicts the solubility of new molecules.
However, chemists already have molecular modeling software to do that, and more. And they also have beakers and water and if they want to, they can synthesize the new drug and drop it in the beaker, stir it, pour off the liquid, let it evaporate, weigh what’s left, and find the solubility that way! So they might pay something for your AI solution, or they may not depending on price and depending on how quickly you can deliver it and how accurate it is in relation to alternatives. Even if it’s better as you point out in your post, they still might not buy the AI if its answer isn’t timely.
A further constraint, and I would like to see proper research on this, is the size of the data that you need for AI to be able to pull out anything useful. I believe that there are problems in physical science where huge data sets are available,, but much in economics and business just doesn’t have the requisite scale. Activity data sets such as you had in your old company are very, very small in relation to those in physical science. For example, someone in my old company estimated that the number of synthesizable new drugs (using common chemical fragments and using common methods of synthesis) was about 10^26th potential molecules. Now that’s a dataset!
In contrast there are 3000 NASDAQ companies, so 20 years of closing price data is only 3000*20*250 is only 15 million data points. It speaks to how inefficient markets were that Renaissance was able to make any money at all!