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OpenAI hardware chief Richard Ho’s point is that AI compute demand can keep growing even as smaller models become cheaper to run. The work is shifting: alongside frontier-model training, post-training and inference-time reasoning can consume substantial compute by generating more tokens. That means continued demand for accelerators and the memory, networking, software and datacenter capacity around them.

What did Richard Ho mean by “scaling laws will continue”?

At a Synopsys SNUG keynote, Ho said, “It does appear that scaling laws will continue to grow [compute needs] to provide extra capabilities.” In this context, “scaling” means using more computing resources to develop or operate AI systems that can deliver additional capabilities; it does not mean that every model must grow larger.

The change Ho described is where the compute goes. Demand is extending beyond frontier training into post-training and test-time compute. In particular, reasoning workloads can use more compute by producing more tokens while solving a problem. A smaller or cheaper model therefore does not, by itself, establish that total compute demand will fall.

How fast has AI compute grown?

EE Times reported Epoch AI estimates that training compute grew 6.7× per year through 2018 and more than 4× per year after 2018. These are historical growth rates attributed to Epoch AI, as reported by EE Times in 2025—not forecasts that the same rate will continue.

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The article describes several contributors to this growth: Moore’s law, reduced-precision computation, larger systems and the ability to run jobs for longer. Ho’s argument about continuing demand is also tied to the shift toward post-training and inference-time workloads, not solely to ever-larger frontier training runs.

Why does continued scaling affect more than GPUs?

Accelerators are important, but usable performance depends on a full stack: the model, compiler, chip, system and kernels. A chip’s advertised peak performance does not guarantee the same throughput in a real workload. Bottlenecks elsewhere in the stack can limit how much of that peak is achieved.

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Ho’s emphasis on full-stack co-design also helps explain why custom accelerators matter. A chip must fit the model workload and work effectively with its compilers, kernels and systems. Choosing hardware by peak specification alone can miss constraints that determine real throughput, latency or efficiency.

What infrastructure does growing AI compute require?

Ho described AI computing as moving toward utility-like services and globally scaled infrastructure. The source characterizes current systems as warehouse-sized and points to larger infrastructure ahead. It also describes training jobs spanning clusters in different geographies. At that scale, compute growth involves the facilities and connections around accelerators, not just the chips themselves.

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  • Memory and bandwidth: Workloads need enough memory capacity and bandwidth to keep computation supplied with data.
  • Networking: Larger systems rely on fast links within and between systems, including die-to-die and chip-to-chip connections.
  • Reliability: Synchronous training can stall when a component fails, making resilience and uptime consequential.
  • Power management: Expanding compute capacity also makes power a practical infrastructure concern.

Hardware roadmaps face a timing mismatch, too. The source describes chip design cycles of roughly 18–24 months, while AI research can move faster. That puts pressure on teams to shorten the path from architecture decisions to tape-out so hardware does not lag behind changing model workloads.

How should AI hardware options be compared?

For datacenter accelerators and systems, a useful comparison looks beyond a single peak-performance number. The relevant priorities depend on the workload, but these dimensions help expose trade-offs:

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  • Throughput and latency: How much work can the system complete, and how quickly does it respond?
  • Memory: Does capacity and bandwidth fit the model and workload?
  • Power efficiency: How much useful work is delivered for the power consumed?
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These criteria matter together: a fast chip can be constrained by memory, interconnects, software or reliability, and the best balance depends on the model and the job.

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What this means for GPU demand

Ho’s remarks support continued demand for accelerators, with GPUs remaining a major part of the mix. They do not establish that every AI workload must use GPUs, or that consumer graphics cards are equivalent to datacenter accelerators. The broader implication is that scaling depends on the complete computing system: accelerators, memory, networking, software, facilities and operational resilience.

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