# Research POV: Why Computational Efficiency May Be the Key to AGI

> Coverage of together-blog

**Published:** December 17, 2025
**Author:** PSEEDR Editorial
**Category:** stack

**Tags:** AGI, Hardware Optimization, AI Infrastructure, Compute Efficiency, Software-Hardware Co-design

**Canonical URL:** https://pseedr.com/stack/research-pov-why-computational-efficiency-may-be-the-key-to-agi

---

Together AI challenges the prevailing narrative of a hardware wall, arguing that rigorous software-hardware co-design is the missing link to achieving Artificial General Intelligence.

In a recent analysis, the research team at Together AI addresses the skepticism surrounding the feasibility of Artificial General Intelligence (AGI). Titled **"Research POV: Yes, AGI Can Happen - A Computational Perspective,"** the post offers a counter-narrative to the idea that AI progress is destined to plateau due to physical hardware limitations.

### The Context

As Large Language Models (LLMs) grow in parameter count, the technology sector has become fixated on the scarcity of high-end GPUs. This resource constraint has led some observers to speculate that the industry is approaching a point of diminishing returns, where the cost, energy, and physical space required to train smarter models become unsustainable. This pessimistic view often assumes that current hardware utilization is near-optimal, implying that the only path forward is the brute-force addition of more physical chips.

### The Signal

Together AI disputes this assumption. Their analysis suggests that the ecosystem is not currently hitting a hardware wall; rather, it is facing an efficiency gap. The post argues that existing silicon is significantly underutilized due to inefficiencies in the current software stack.

The central thesis is that the path to AGI lies in rigorous **software-hardware co-design**. Instead of treating hardware as a fixed commodity, the post advocates for optimizing the entire stack-from the model architecture down to the kernel level-to match the specific characteristics of the underlying silicon. By improving how algorithms map to hardware, researchers can potentially achieve performance gains equivalent to moving a generation ahead in hardware, without actually changing the physical infrastructure.

### Why It Matters

This perspective is significant for engineering leaders and investors alike. It implies that the "moat" in AI development may shift from pure capital expenditure (buying the most GPUs) to engineering excellence (getting the most out of those GPUs). If the next order of magnitude in performance comes from optimization, the timeline for AGI may be less dependent on supply chain logistics than previously thought.

For organizations building or deploying AI, this signals a potential pivot in strategy. While securing hardware remains important, the competitive advantage may increasingly belong to teams capable of writing efficient kernels and optimizing communication overhead in distributed training. This approach not only reduces costs but also accelerates iteration cycles, a critical factor in the race toward general intelligence.

We recommend reading the full post to understand the specific computational arguments supporting this optimistic outlook.

[Read the full post at Together AI](https://www.together.ai/blog/research-pov-yes-agi-can-happen)

### Key Takeaways

*   AI development is not currently limited by a hard hardware wall, but rather by software inefficiencies.
*   Existing chips are vastly underutilized, leaving significant room for performance gains on current infrastructure.
*   Software-hardware co-design is identified as the primary lever for achieving the next order of magnitude in performance.
*   Achieving AGI may depend more on computational optimization and engineering elegance than solely on hardware scaling.

[Read the original post at together-blog](https://www.together.ai/blog/research-pov-yes-agi-can-happen)

---

## Sources

- https://www.together.ai/blog/research-pov-yes-agi-can-happen
