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Frustrating part of AI models for bio is lack of clarity in what types of data we should be collecting and the sampling rate that provides biological significance. If you look at autonomous driving, progress in that field wasn’t just due to algorithms. Companies had a lot of continuous training data. Imagine training a self driving car with some snapshots from intersections. This is close to the types of data we currently have in biology (sparse snapshots and yet ppl are trying to dynamic systems). Need more/different types of data (more tech?tools?), better sampling rate in many cases (including more informed ways of modeling it).

TradFi Terminal trade recap: $SPCX IPOP by @tradexyz Users had a 15 second (!) window to enter this trade with one click before a 4.2% decline. Trade it yourself:

with $CBRS listing in less than 12 hours, its currently trading at a 57% premium to the $185 open price on @tradexyz funding has mostly normalized open interest is at $19,139,131 @ $56.4b FDV at offering it ranks as the largest IPO since 2022, and the 10th most expensive IPO of all time $CBRS at $185 trades at 1/95 the full valuation of NVDA, 1/17 of both MU and SKHX, but trades at over a 4x price to sales multiple of $NVDA, more than 7x compared to $MU, and 17x compared to $SKHX below is some relevant data as well as a list of some previous IPO's, their first day MCAP, and their first day performance tomorrow should be a window of how far we are into the bubble, or the AI supercycle whichever way you want to describe it