Why scaling to 10 Billion health messages (our primary mission) may depend less on bigger models and more on smarter, simpler transformers.

In the world of artificial intelligence, it’s tempting to believe that bigger is always better. More layers, more parameters, more gates – surely that means more intelligence. But for those of us building Frontline-Aware AI for public health, simplicity may actually be the secret weapon.

A new wave of research is showing that simpler transformer designs can achieve higher capacity ratios – meaning they squeeze more efficiency out of the same training data and hardware. For DPE’s mission – scaling InfoAFYA™ to 10B health messages – this isn’t just academic. It’s survival.

Transformers in Plain Language

At their core, transformers are made up of repeating “mental muscles.” Each block helps the system process, store, and decide on information. Think of it as a stepwise process:

1. Normalize: Take a deep breath so nothing gets out of proportion, keeping responses stable.
2. Embed time and space: Remember when and where something happened.
3. Pay attention: Look at information from multiple angles at once.
4. Combine logic and intuition: Mix different signals into one insight without forgetting past knowledge.
5. Decide: Produce the next best action, whether that’s a word, a number, a health message, a clinical decision prompt, etc.

For public health, this means a transformer can decide not just what to say (“Take your malaria medicine”), but when and how to say it (“Your child’s next dose is due in 8 hours.”).

The Complexity Trap

At the heart of transformers are MLP (Multi-Layer Perceptron) layers, the workhorses that transform raw attention into usable insights. Many modern AI models add gates to these layers (like SwiGLU or GeGLU), which are meant to add expressiveness.

But recent findings in the Physics of Language Models papers highlight something counterintuitive:

• In low-data regimes (like most public health use cases), gated MLPs actually reduce the effective capacity ratio. In plain terms, fancier tools don’t always help when you don’t have enough raw material (data) to train on.
• Instead, standard MLPs can outperform gated ones, packing more useful knowledge into the same model size. Meaning, for smaller models, like those designed for low resource setting health use cases, leaner architectures perform better (chasing architectural complexity doesn’t always translate to impact.)

Why This Matters for Frontline Health

Health communication systems don’t have the luxury of infinite compute budgets. They need nimble, efficient models that can run on constrained hardware – sometimes even on a county server or a mobile device. For Interch™ and InfoAFYA™, this is more than an academic point:

• Low-resource training is the norm: We don’t have millions of annotated sickle cell or malaria dialogues. Efficiency is survival. What we need to do is unlock more knowledge per parameter for frontline assistants.
• Compute is precious: GPU cycles must be stretched further than in Silicon Valley. We must deliver cheaper, more scalable AI for Health systems and payers.
• SMS, Chatbots, Behavioral Copilots: These must run lean while still being life-saving. We must stay aligned with real-world constraints, local data, intermittent connectivity, smaller models.

Imagine a near future where every one of the 10 billion health messages we envision can be powered by lean, efficient transformers that are tuned for Kenya’s SHIF rollout, for caregivers in Kenya, and for sickle cell patients across SSA. You name it.

The benefits will be there for all to see:

• Scalability: More health campaigns run at once with less GPU demand.
• Accessibility: SMS and WhatsApp integration without lag.
• Resilience: Models can continue running in low-resource settings.
• Affordability: Lower operational costs mean donor funds and payer contracts stretch further.

That future won’t come from the flashiest models, but from right-sized architectures built for the frontline. This is the beating heart of our Frontline-Aware AI strategy: build models that match the environments they serve.