Page 284 - Binder2

Critics point to it as a reason plant-based platforms “can’t
scale.” Why decentralized manufacturing is “too
inconsistent.” Why anything that grows, rather than
ferments, is seen as too risky for precision therapeutics.

But that critique is rooted in an old assumption:

That we can only manage what we can measure.

AI changes that.

From Environmental Chaos to Predictive Control

Traditionally, environmental factors were monitored with
static thresholds. If the temperature strayed too far, an alert
was triggered. If growth slowed, someone checked the
nutrient tank. These were coarse corrections—analog
responses to digital problems.

AI systems turn those corrections into continuous, dynamic
adaptation.

By integrating environmental sensors, imaging systems,
and multi-modal process data, AI models learn how
specific variables affect biologic outcomes. They don’t just
react to change—they predict it.

For example:

• A plant’s leaf thickness or chlorophyll density can
now be monitored in real time using multispectral
imaging—and correlated with downstream protein
yield.
• Shifts in humidity can be tied to glycosylation
profiles, allowing for mid-cycle adjustments before
a batch drifts off spec.

282