new delhi [India]April 22 (ANI): The rapid expansion of artificial intelligence (AI) is currently constrained by the availability and cost of computing power, rather than model quality, according to a Goldman Sachs report.
The report highlighted that the need for computing infrastructure, such as high-performance chips and data centers, is growing faster than supply as demand for AI applications accelerates, particularly in real-world deployments and “inference” use cases. This makes access to reliable and cost-effective computing a key differentiator for companies operating in the AI space.
“Compute is emerging as a binding constraint in scaling AI. As demand for inference grows faster than available capacity, the key differentiator is no longer just model quality, but the ability to reliably access and finance compute in the most performant way.”
The company noted that the industry’s focus is shifting from solely building better models to how these models run efficiently in production. Factors such as cost efficiency, reliability, performance, and the ability to route workloads between systems have become important in determining competitive advantage.
According to the report, the value of the AI ecosystem is increasingly shifting to the execution layer, which includes technologies such as managing model deployment, optimizing compute usage, and ensuring consistent performance. Companies that can secure and efficiently utilize computing resources are in a better position to scale their AI offerings.
The report also points to broader structural changes in the software industry. Rather than competing directly with traditional Software-as-a-Service (SaaS) companies, AI-native companies are targeting gaps between existing solutions. While SaaS platforms have traditionally focused on systems of record and functional silos, AI-native companies are building systems of action that deliver end-to-end outcomes.
These AI-driven solutions are deployed faster, often up and running within weeks, and are seeing improved conversion rates from pilot projects. Unlike traditional models that charge users based on their seats and features, AI-native companies are increasingly monetizing based on business impact and productivity gains.
Goldman Sachs also noted that applied AI is approaching an inflection point similar to the generative AI boom seen in 2022. Although large-scale commercialization may still be five to 10 years away, advances in fundamental models, simulations, and edge technologies are enabling AI systems to move beyond decision support and into real-world execution.
This includes applications across logistics, labor automation, and defense where AI systems can continuously operate, learn, and improve. Companies that continue to expand into the real world are expected to gain an advantage by building unique data ecosystems that improve performance over time.
The report therefore outlined that as AI continues to evolve, control of computing resources and the ability to efficiently deploy models will play a central role in shaping the next phase of the industry’s growth. (Ani)
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