How we’re automating the wrong things and ignoring the true source of disruptive value—science.
Every few months, a new AI tool drops, and the internet reacts as if someone just discovered fire. We hear the same buzzwords: faster, cheaper, smarter. But the question I keep asking is: to what end?
AI, for all its brilliance, is still largely a productivity engine. It helps us write emails faster, generate code, summarize documents, and optimize operations. And that’s valuable—until it isn’t.
Here’s the uncomfortable truth: AI is great at iterating on what we already know. But it’s not creating truly new knowledge. And unless we fix how we handle deep scientific discovery, we’re just automating ourselves into a smarter version of stagnation.
The Real Bottleneck Is Discovery
Universities around the world generate brilliant, high-potential scientific insights every day. But up to 85% of academic research never gets commercialized, not because it’s bad science—but because there’s no system to translate it into reality. It dies in PDFs, hidden behind paywalls, in a language no investor understands.
So while we obsess over prompt engineering and fine-tuning models, we’re ignoring a much bigger opportunity: building technology that starts with real innovation—the kind that could cure diseases, reverse climate change, or reinvent materials.
Where AI Actually Becomes Valuable
AI becomes exponentially more valuable when it’s pointed at what truly matters.
Not email, not slide decks, but unlocking scientific knowledge that would otherwise stay stuck in academic purgatory.
Take this example: a breakthrough in using ultrasound imaging and simulation modeling to assess the risk of preterm birth. The technology generates personalized 3D models of a pregnant woman’s cervix and uterus, then applies Finite Element Analysis to predict complications early. It can be integrated into routine appointments and could dramatically reduce the 15 million preterm births that occur each year, it’s science-enabled predictive healthcare with life-saving implications.
That’s the kind of application where AI shines as a translator and accelerator of deep knowledge.
Reframing Automation: From Efficiency to Emergence
The narrative around AI is deeply rooted in efficiency: do more with less.
But what if the future isn’t about doing more faster, but doing better from the start?
Scientific IP represents a form of raw potential that no amount of UI polish or GPT wrapper can replace. It’s the difference between building yet another AI meeting assistant and building a personalized antimicrobial cream that targets specific pathogens in a hospital’s ecosystem—based on real clinical research.
What Businesses Can Do Differently
If you’re a founder, investor, or decision-maker wondering where to aim your AI strategy, consider this:
- Partner with research institutions. There is untapped value sitting in every major university. You don’t need to invent; you need to translate.
- Automate insight discovery, not just execution. Use LLMs to extract latent value from scientific literature, patent databases, or trial results.
- Redefine what “MVP” means. In science-driven sectors, the minimum viable product isn’t a prototype—it’s a validated hypothesis with a pathway to IP and market fit.
- Involve scientists early. Many founders think science is a back-end function. It isn’t. If you’re building anything novel, your science team should be at the table from day one.
The Real Innovation Stack
The next wave of transformative startups won’t just use AI—they’ll be built on top of scientific IP, with AI as the enabler. Think CRISPR, think climate tech, not another AI calendar app.
True disruption starts at the level of discovery, and if we can finally connect that to the engines of venture and execution—then maybe, just maybe, we’ll stop solving the wrong problems faster.
