You provide pretty conclusive evidence that AI can't make better decisions than the current system, but isn't this also evidence that AI (perhaps with slightly-cynical prompting) can also do not-worse than the current system? And if this is the case, then AI might still be adopted eventually. A pretty substantial fraction of the expenses (both cash and equity) of a typical biotech will go towards the people who make such decisions. And at least some of the development time goes towards making such decisions. So adopting AI, even if not transformative, could still help reduce costs. (This does mean that making such an AI isn't a viable startup, since one can't charge more than the expected cost savings.) I guess the counterpoint is that having smart, respected, and experienced people making these decisions is necessary to gain confidence from the investors and the company staff. But the counter-counterpoint is that, depending on how public perception of AI evolves, people might eventually trust AI even more for these decisions.
Engage in the kind of deep level analysis described. I am reasonably confident though that AI will find near-term use in asset prioritization and indication selection by aggregating data from multiple sources and perhaps helping to rank the academic literature thereby aiding the selection process.
An excellent post. I have a couple of thoughts. First off, while it’s clearly true to say that either a drug works or it doesn’t, the consequences of the former do not translate in a simple 1:1 fashion with commercial success. In other words, technical success while clearly a prerequisite for commercial success is insufficient. This doesn’t invalidate your argument but it’s nonetheless important to appreciate. Secondly, I’m aware of a number of consultants who are attempting to develop AI to help support clinical and commercial development but at this point in time, the software is perhaps too rudimentary to
You provide pretty conclusive evidence that AI can't make better decisions than the current system, but isn't this also evidence that AI (perhaps with slightly-cynical prompting) can also do not-worse than the current system? And if this is the case, then AI might still be adopted eventually. A pretty substantial fraction of the expenses (both cash and equity) of a typical biotech will go towards the people who make such decisions. And at least some of the development time goes towards making such decisions. So adopting AI, even if not transformative, could still help reduce costs. (This does mean that making such an AI isn't a viable startup, since one can't charge more than the expected cost savings.) I guess the counterpoint is that having smart, respected, and experienced people making these decisions is necessary to gain confidence from the investors and the company staff. But the counter-counterpoint is that, depending on how public perception of AI evolves, people might eventually trust AI even more for these decisions.
Engage in the kind of deep level analysis described. I am reasonably confident though that AI will find near-term use in asset prioritization and indication selection by aggregating data from multiple sources and perhaps helping to rank the academic literature thereby aiding the selection process.
An excellent post. I have a couple of thoughts. First off, while it’s clearly true to say that either a drug works or it doesn’t, the consequences of the former do not translate in a simple 1:1 fashion with commercial success. In other words, technical success while clearly a prerequisite for commercial success is insufficient. This doesn’t invalidate your argument but it’s nonetheless important to appreciate. Secondly, I’m aware of a number of consultants who are attempting to develop AI to help support clinical and commercial development but at this point in time, the software is perhaps too rudimentary to