Scaling AI Inaccuracy
Posted on December 13, 2025 • 4 min read • 651 words
The following is a post I asked Google Gemini to write to summarize a conversation we were having regarding who profits from LLM hallucinations and inaccuracies. (Hint: It’s Big Oil and data center providers.)
The Hidden Cost of AI Inaccuracy: Why More Lanes Won’t Solve the Compute Congestion
We’re in an era of unprecedented AI growth, with large language models (LLMs) like the one you’re interacting with becoming increasingly integral to our daily lives. From drafting emails to generating code, these powerful tools are reshaping how we work and live. But as we marvel at their capabilities, it’s crucial to examine the hidden costs of their current limitations, particularly when it comes to accuracy.
A fascinating parallel can be drawn between the challenges of scaling AI infrastructure and a familiar urban planning dilemma: traffic congestion.
The “Build More Lanes” Fallacy in AI
Think about how cities often try to solve traffic congestion: they build more lanes. The immediate relief is palpable, but inevitably, those new lanes fill up. This phenomenon, known as “induced demand,” demonstrates that simply expanding capacity without addressing the root causes of demand only pushes the problem further down the road. More people drive, new bottlenecks emerge, and the cycle continues.
The same logic, surprisingly, applies to the world of LLMs and their insatiable appetite for computational resources.
The Compute Congestion Problem
Consider an LLM that frequently “hallucinates” or provides incorrect answers. When an LLM makes a mistake, the user often has to:
- Ask clarifying questions.
- Provide corrections.
- Request multiple iterations until an accurate answer is reached.
Each of these additional interactions, though seemingly minor, consumes valuable computational cycles. If a query that could have been resolved in one accurate exchange now takes four, the system has effectively performed four times the work for the same desired outcome.
This isn’t an isolated incident. Across millions of users and billions of queries, these “wasted cycles” due to inaccuracies accumulate into an enormous, unnecessary computational load.
The True Cost of Inaccuracy
- Massive Energy Consumption: More compute cycles mean more electricity. Data centers already consume vast amounts of energy. When an LLM requires multiple attempts to get an answer right, it drives up electricity demand, directly benefiting energy providers at an increased environmental cost. It’s like having every car on the highway drive an extra three miles just to correct a wrong turn.
- Accelerated Infrastructure Expansion: Just as perpetually adding more lanes to a highway eventually requires buying up more land and building new bridges, constantly dealing with “compute congestion” drives the need for more physical data centers. These expansions are incredibly costly, both in terms of financial investment and environmental footprint (land use, construction materials, and the energy required to run ever-larger facilities).
The Sustainable Solution: Invest in Accuracy
The most sustainable and profitable path forward for AI development isn’t simply to “build more data centers” to accommodate current inefficiencies. Instead, it’s to invest heavily in making LLMs more accurate and reliable.
- Optimizing the “Traffic Flow”: Improving model accuracy is akin to optimizing public transit, implementing smarter traffic light systems, or encouraging carpooling. It reduces the demand for unnecessary travel (i.e., wasted compute cycles) on the existing infrastructure.
- Financial & Environmental Wins: A more accurate LLM requires fewer cycles per query, leading to significant savings in operational energy costs. This increased efficiency boosts profitability and, crucially, delays or even negates the need for rapid data center expansion.
- Enhanced User Experience: Beyond the environmental and financial benefits, greater accuracy directly translates to a better, more trustworthy user experience, fostering deeper adoption and utility.
Just as urban planners are realizing that a holistic approach to managing traffic is more effective than simply paving more land, the AI industry must recognize that true scalability and sustainability lie in enhancing the intelligence and precision of the models themselves, rather than continuously expanding the infrastructure to house their current imperfections.