Why Smarter Chips Are the Key to AI’s Next Leap

By The Non-Von Team

For centuries, the global economy inched forward, just 0.1% growth a year until 1700, doubling only once in a millennium. Then the Industrial Revolution rewrote the rules pushing growth to 2.8% annually in the 20th century  doubling output every 25 years. Now, AGI (Artificial Growth Intelligence) could be the next inflection point.

Today’s AI leaders believe we’re on the edge of something bigger than the Industrial Revolution. They see AGI driving global GDP growth to 20–30% a year or more, which is a  pace that would have been unthinkable in any other era. It sounds extreme, and by historical standards it is. But so did the idea of sustained economic growth… until steam, steel, and machines made it real.   (Section 1; The Economist, 2025)


AI could push GDP growth to unprecedented levels.

What could fuel growth on this scale? AGI’s ability to accelerate innovation without adding more people to the equation. In the past, more population meant more ideas. With AGI, the dynamic shifts. Progress can compound on its own, with technology refining and advancing at speeds far beyond human limits.

Some projections go well past simple automation. As AI approaches AGI, whole segments of the global economy could shift to machine-led production. But the real tipping point isn’t about replacing human labor in predictable, repetitive work, it’s when AI starts improving the very technology it runs on. 

AGI isn’t just about speeding up routine tasks.  It can take on the big, messy work humans spend years untangling. End-to-end project management. Driving complex research forward. Even running entire labs without a human in sight. That kind of capability sets up a chain reaction: breakthroughs building on breakthroughs, each one laying the groundwork for the next. Economists call it the “ideas create more ideas” effect - a compounding loop with no real ceiling on how fast progress can move. (Section 2; The Economist, 2025)

This leap only happens if hardware can keep pace.

Transformative growth comes with real-world limitations. A highly automated economy can’t run on ambition alone,  it needs “sufficient energy and infrastructure,” and that means heavy investment. For AGI to manage projects and drive scientific discovery, it will take a step-change in computational power and efficiency.  (Section 2; The Economist, 2025)

As economist Tyler Cowen puts it, “the stronger the AI is, the more the weaknesses of the other factors bind you,” with energy and data often topping the list. Hardware is squarely in that crosshairs. Models from Epoch AI estimate that optimal investment in AI for this year could hit $25 trillion,  roughly 50 times today’s spend.(Section 3; The Economist, 2025)

Most AI still runs on GPUs, chips built for graphics work, not AI workloads. They draw huge amounts of power and demand massive electrical budgets. That inefficiency is a growing restriction-one that stands between today’s capabilities and the kind of economic acceleration AGI promises.

But potential alone doesn’t guarantee progress. To turn AGI’s promise into reality, the hardware behind it has to scale just as fast and that’s where the real barriers begin to show.

How smart chips solve key scaling limits

This is exactly where advanced chip design steps in. Our novo architecture isn’t a retrofit. It’s built for AI from the start. By rethinking how data moves and how power is used, these chips deliver a leap in processing performance while slashing electrical consumption.

Memory Access:
Traditional computing keeps hitting the same wall: the von Neumann bottleneck. Non-Von’s novo architecture removes it from the equation. By giving each core its own dedicated memory instead of forcing them to share a single, centralized pool, it eliminates memory blocks, cuts power demands, and strips out the extra computational and electrical overhead that slows conventional designs.

Energy Efficiency:
Our architecture is engineered to cut energy use without compromising performance. It matches the throughput of a full-scale data center while running on the power footprint of an edge device-delivering up to 100x greater power efficiency on workloads ranging from deep-learning vision models to transformer-based chatbots. That kind of efficiency lets entire server farms run faster while drawing only a fraction of the power GPUs consume today. (See Hokenmaier et al., 2024)

In addition, the SCULPT toolset automatically adapts and streamlines models for novo hardware, removing the inefficiencies that slow GPU execution and unlocking significant cost reductions at scale.

Scalability:
Our chips are purpose-built for the latest wave of AI architectures. Sparse and quantized models like BitNet, HNet, sparse transformers, and FP4 design approaches that are quickly gaining traction across the industry. Because the architecture anticipates where software is headed, performance advantages grow as models get newer and more advanced.

A key differentiator is the ability to handle unstructured sparsity, an inherent feature of the development process for most AI models. This gives engineers the freedom to tap into any form of sparsity for substantial efficiency gains. This is a capability that has only recently become practical at scale. The novo platform also offers broad compatibility, able to take an existing trained model and automatically convert it into a sparse, highly compact form directly in silicon, with full support for major frameworks like PyTorch, ONNX, Keras, and TensorFlow. This flexibility ensures our  hardware stays aligned with evolving model complexity and compute demands.


Without these chips, growth projections remain theory.

If computing hardware doesn’t evolve to overcome today’s power demands, memory constraints, and scaling limits, the economic boom envisioned by AGI optimists may never materialize. The shift toward fully automated global production and the massive build-out of data centers it requires, depends on chips that can deliver exceptional processing performance while sharply reducing energy use.

Our chip is built to meet those exact challenges, positioning it as purpose-made hardware for advanced AI systems and a potential catalyst for realizing AGI’s full economic impact. 

​History offers a hint of what’s possible: AI’s progress has repeatedly outpaced industry predictions, and if the hardware keeps pace, the next wave of breakthroughs could arrive far sooner than expected.

Sources:

The Economist. (2025, July 24). What if AI made the world’s economic growth explode? The Economist. https://www.economist.com/briefing/2025/07/24/what-if-ai-made-the-worlds-economic-growth-explode

Hokenmaier, W., & Jurasek, R. (n.d.). Co-design of a novel CMOS highly parallel, low-power, multi-chip neural network accelerator. Semiconductor Hardware Development, Green Mountain Semiconductor Inc. https://arxiv.org/pdf/2409.19389