3.21.25
AI in education, value of granular data, forward deployed engineers as GTM decision, over-estimation of human in loop for roll-ups, precision diagnostics, extra cup of coffee problem with AI
AI ubiquitous in education: the shift is obvious, but how do you take "bad" markets and make them good? Is it even possible?
The value of granular topography/earth data -- so much value to be unlocked, why haven't there been bigger businesses built here? Might be a now is the time argument with AI being able to unlock this. Feels like value is in how you structure the applications on top for usage
Forward deployed engineers as a strategic GTM decision. Rarely done, but when does it make sense to have dedicated resources to a singular customer? ACV + Product as the indicators of this.
So many VCs have a "roll-up" thesis, but I think there is an overestimation of the human labor component of many of these businesses (I seriously considered buying a septic service business). Can only abstract so much. Also, not convinced venture is the right capital structure for these.
Precision diagnostics as the future given advances in AI
Getting better data for the future of robotics
The "extra cup of coffee" problem with AI innovation - cost/time savings aren't translating into bottom line because the improvements aren't yet big enough to fully replace a worker, but it does make them more efficient at their job so they work the same amount but just do less (i'll use this free 15 mins for a cup of coffee vs. revenue driving or cost savings)