Some lessons from Biotech Innovation for GenAI and AI more broadly
"#generativeai is going to change everything!” 🤯
It’s a frequent and regular opinion. A helpful way to evaluate a claim like this is to look at the alternatives, the competitors. What else might “change everything”?
Matthew Herper has a powerful view at STAT: "Why we’re not prepared for the next wave of biotech innovation":
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"It's biology's century...In the same way the 20th century belonged to physics, the 21st is biological. But while physics in the 20th century brought airplanes, personal computers, and posters of Albert Einstein, it also meant the atom bomb and a complete transformation of the social order."
"Now, we’re approaching a moment when changes in what we understand about biology are every bit as exhilarating and terrifying."
What do you think? 🤔
Whatever your perspective on these 2 areas of innovation - genAI and biotech - they share a problem:
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" ...the biggest looming problem is that we will simply become lost and confused as to what works and what doesn’t, scuttling our own progress, wasting money, and missing opportunities to save lives. That’s what happens when new technologies in biology outpace our ability to assess them."
The same goes for GenerativeAI. It's great that you can generate 1000s of unique images & text. But how do you know which person likes which image? The big datasets behind the Large Language Models have NOTHING to say about the specific customers your business serves.
Remember 'starving artists'? 👩🎨🤤
You used genAI to make an image YOU think is cool. There's no guarantee anyone else will like it.
Getty Images already has >477 million assets. As a marketer, is it just the price that stops you from using those assets? Or is it that you simply have no way to operationalise which asset to use for which instance and marketing surface?
What's the answer to this looming problem? Matthew puts it simply:
"The core technology is not the drug molecule but randomization, randomly assigning patients to get one thing or another. This is the only way researchers can be sure that people who get the drug live longer, and the outcome is not due to chance."
"At some point, collecting data to determine whether or not treatments work...should not simply be the results of studies. It should be a property of a ... system. An ideal system would collect data — and perhaps even conduct randomized controlled trials of different treatments — almost automatically."
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"So why don’t we have such a system? Part of the answer is that it’s harder than it looks."
Matthew is entirely right: it IS harder than it looks. The standard CRM and CEM and CDP platforms just haven’t cracked what it takes.
That’s why we set out to build Aampe. Aampe’s agents do the hard work FOR your overworked tech teams. Aampe lets you generate 1000s of messages and unbounded content in any format, and then rapidly and efficiently randomizes their assignment to your individual customers to learn what works and what doesn't ... for every single customer.
With Aampe you get automated, statistical experiment design, at scale, with automated follow-through optimisation - ready to deploy in your app or website.
In both consumer tech and biotech, the innovation is just getting started. To achieve the kind of ambition we see so broadly today, we have to learn the limitations of our tools as quickly as possible, so that we can use them the right way, and extend and further develop them efficiently and effectively.
Citation
https://www.statnews.com/2022/11/03/why-were-not-prepared-for-next-wave-of-biotech-innovation/