Bloomberg: "Virtually every major category of costs associated with building a data center is ballooning...The cost of construction materials such as steel and cement has skyrocketed because of trade wars, fuel expenses and geopolitical conflicts. The AI boom itself has driven up expenses for everything from chips to copper. The price of short-term memory used in data centers is more than five times what it was just a year ago, data compiled by Bloomberg Intelligence show. Demand for equipment that generates and carries electricity is so high because of the building frenzy that suppliers have backlogs stretching beyond 2030." It is important to keep in mind that only $492 billion of the $2.3 trillion data center development pipeline is currently under construction. The remaining $1.8 trillion is tied to planned and precommitted projects. And those projects are facing rapidly inflating costs. This inflation is something I dove deep on in my report on "The AI Trade" (sageroadresearch.com/product…). To quote the report: "The question remains, can hyperscalers enable agents at a price enterprises will pay that’s margin positive for hyperscalers? That’s clearly not the case today. As Nvidia VP of applied deep learning Bryan Catanzaro acknowledged in June: “The cost of compute is far beyond the cost of employees.” And the cost of compute is inflating. As the FT noted in late July, over the past two decades, spending on semiconductors averaged 0.47% of world GDP per year. This year, total spend on semiconductors is projected to equal 1.25% of the world economy. And next year, that spend may exceed 1.5% of global GDP—roughly three times higher than three years ago. From demand increasing far faster than supply has come “chipflation”. “On-demand GPU capacity is effectively sold out,” as Apollo noted in mid-June. TSMC, which owns roughly 72% of the global chip foundry market, is reportedly sold out until 2028. Yet, it’s not just “chipflation” that matters. IT equipment, which includes chips, accounts for 64% of the total cost of building a data center. Infrastructure accounts for another 29%, which includes electrical systems, cooling, cabling, and fire prevention. Those costs are also inflating, up an estimated 40% over the past two years. Then there’s the cost to actually build the physical structures, which accounts for 7% of data center building costs. Inflating data center construction costs have been the primary driver of the PPI for non-residential construction spiking since 2021. Overall, the average per megawatt cost of building a data center has increased from roughly $7 million pre-pandemic to $11.3 million this year. For AI-optimized data centers, the average cost per megawatt is expected to land between $15 million and $20 million this year. According to iRecruit.co, 50% of data center executives report project bids rising by 6% to 15% over the past year. We believe the unit economics of agentic AI are one of the challenges most neglected by AI optimists today. As has been made clear this year, enterprises will not spend heavily on agentic capabilities unless the models improve at the same time costs come down. Maybe in an ideal world, hyperscalers would slow down the buildout as they wait for model efficiency breakthroughs and chip advancements to drive down token costs and agentic token load. However, competition won’t allow that. So, they’re racing to build out the data center capacity needed to improve the models, but that effort is only increasing the cost of building data centers, which is increasing the agentic price tag, at least in the near to medium term." Bloomberg link: bloomberg.com/features/2026-…

There's not a viable path to frontier lab profitability unless there's a viable path to pricing power: "The LLM Token Expenditure Index, a key gauge of daily prices from intelligence firm Silicon Data, fell to 97 cents on Monday. That marked the index’s lowest reading since its creation late last year and has fallen by more than half from the high recorded earlier this summer...'Token deflation compresses the revenue line while compute commitments stay fixed. The strategic response is visible: the moat must shift away from raw model capability — where the open-weight gap is now measured in months — toward distribution, memory and context.'" Four years since ChatGPT debuted, trillions built up in frontier lab valuations, trillions in spending commitments by hyperscalers, and we're still waiting on the right strategic pivot that can establish LLM pricing power. I remain dubious it'll come on an adequate timeline, if ever. I've warned about the LLM pricing power challenge since December and dove even deeper on it in my September report on "The AI Trade" (sageroadresearch.com/product…). To quote the report: "In December, we warned that market participants were neglecting that AI was forcing a fundamental shift for tech giants. They were transitioning from the most profitable business models in history to business models far more capital intensive and that intensity was in perpetuity. To quote @verdadcap: “The big tech companies have gone from having about a third of the capital intensity of US industrial companies to three times the capital intensity of your typical US industrial company.” Even more troubling, that capital intensity couples with an erosion of their moats. As we wrote at the time: “While there’s a lot of speculative fear about how a single LLM could rise to dominance and what that could mean for economic, societal, and political stability, we believe the bigger concern for investors today is how relative model parity could compromise pricing power. Tech giants have thrived on monopolies and duopolies for a decade or more. Now, they’re in an LLM arms race where it’s unclear when or even if ever leadership will be sustainable”... “Commoditization” has long been a specter looming over AI enthusiasm. This year has only provided reason to fear commoditization is a near inevitability. Hyperscalers have been increasingly shifting from subscriptions to usage-based, metered billing, business models more akin to utilities than the per-seat models SaaS companies thrived on over the past decade. This shift makes sense given how fast token consumption can escalate as AI capabilities are applied to more complex workflows and the cost to hyperscalers of delivering those tokens. However as RBC warned in July: “Oil, natural gas, and electricity are all important. Entire economies depend on them. But importance alone does not guarantee strong economics or durable profitability. When supply expands aggressively and is increasingly interchangeable, competitive forces tend to compress returns over time.” Chart link: cnbc.com/2026/09/01/ai-token…