Quick Take
- AI infrastructure demand is pushing up computer prices as tech companies compete for memory chips and other components.
- Forecast data-center growth added $5.7 billion to modeled US electricity capacity-auction costs for 2028-29.
- Central banks are watching AI-related inflation pressures, while the technology's promised productivity gains have yet to reach consumers broadly.
Australia's latest inflation figures showed that prices in the category covering audio, visual, and computing equipment rose 6.1% in a single quarter.
That's by far the largest increase in the Australian Bureau of Statistics series, which began in 1980. Computers, TVs, and electronics in general have gotten cheaper and delivered more performance for the same money.
Customers around the world, including Australia, have (reasonably) expected this trend to continue.
This is why the third quarter came as such a surprise, as it interrupted a consumer expectation that has been in place for decades.
The Reserve Bank of Australia linked higher prices for some computers and other consumer electronics to rising memory-chip costs associated with demand for AI infrastructure.
The same components, or the manufacturing capacity needed to produce them, are now being pursued by companies whose shopping budgets look nothing like yours.
On Nov. 3, the RBA raised its cash rate to 4.85%, citing AI-related investment as one of several sources of price pressure. Higher fuel costs and domestic capacity constraints were also involved, and the bank said AI's effect on overall consumer prices remained modest.
The technology hasn't single-handedly taken over Australian monetary policy, although it has progressed from a promise in investor presentations to a consideration in decisions that affect ordinary borrowers.
We've also seen this in the US, where AI demand has reached much further into the system that determines the cost of electricity. PJM, the grid operator serving all or parts of 13 states and Washington, D.C., runs auctions that pay power plants and other resources to be available when the grid needs them, years before the electricity is actually used.
In its analysis of this summer's auction, PJM’s independent market monitor calculated that power providers would receive about $16.4 billion for making capacity available during the 2028-29 delivery year.
The monitor then reran the auction without the forecast growth in demand from new and expanding data centers, while leaving existing data-center demand in place.
Under that scenario, the total fell to roughly $10.7 billion, down $5.7 billion. The calculation shows how expectations of future data-center demand can affect the price of keeping power available.
Together, the Australian prices and the American auction show how big AI's effect on inflation is: the spending starts producing identifiable, quantifiable consequences long before we see any productivity gains.
The costs also reach people who aren't buying AI services, while the institutions assigning those costs are still working out how much responsibility the companies requesting the infrastructure should carry.
Your new computer has some very wealthy competition
Data centers are buildings packed with computing equipment, along with the cooling systems and electricity connections needed to keep it working.
However, AI demand extends well beyond the GPUs that attract most of the attention.
Filling a data center with expensive chips also requires the hardware that feeds them information and keeps them running, creating competition for parts and factory capacity that other electronics manufacturers depend on.
The RBA's account follows that competition through to Australian shops, where higher memory costs contributed to more expensive consumer electronics.
The ABS's 6.1% quarterly equipment-price increase covers a particular statistical category, so it can't be treated as the price increase for every computer, much less a precise measurement of AI's contribution. It does, however, give the central bank's explanation an observable consumer outcome.
Again, something similar has been happening in the US. The September consumer-price report recorded a 9.7% annual increase for computers, peripherals, and smart-home assistants, while smartphone prices were 9.9% lower than a year earlier.
CPI-U price changes from September 2025 to September 2026, not seasonally adjusted. The all-items index provides a benchmark for the two product categories. These movements do not isolate AI's contribution to prices.
CryptoSlateBut even though we've seen large increases in the affected categories, they've actually had a relatively small effect on the average household's entire spending basket.
The San Francisco Fed calculated that prices for information-processing equipment rose 13% over the year to August, but added only 0.2 percentage points to that month's 3.4% headline inflation rate under the personal consumption expenditures measure. Its economists found limited evidence that those pressures had spread broadly across the economy, where energy costs and tariff-related goods prices were doing considerably more of the damage.
The Bank of England was also monitoring demand for AI components as a source of global cost pressure, but judged the potential effect of these and other non-energy risks on UK inflation to be much smaller than the energy shock and little changed since September.
Its quarter-point increase in Bank Rate to 4% was justified by energy-driven inflation risks, alongside stronger-than-expected activity.
Another complication in software is that a subscription can become more expensive when it starts doing something its previous version couldn't.
Federal Reserve researchers have warned that parts of the inflation data may not adequately account for those improvements. Paying more can still hurt a household budget, even when part of the increase reflects better quality.
Electricity is another way costs can spread because both new and existing customers depend on the same system. Someone replacing a laptop can postpone the purchase, whereas households connected to a grid that accommodates substantial new demand have far less control over what gets built and how the resulting costs reach them.
The electricity bill starts with a forecast
PJM coordinates the power system across much of the eastern US, and one of its jobs is to make sure enough electricity can be supplied when demand is high.
Alongside markets for delivered electricity, it runs auctions that pay suppliers to commit capacity for a future year, meaning they agree to be available under the market's rules when the system needs them.
That availability has a separate price from the electricity eventually generated. Power stations need sufficient revenue to remain in service or justify investment, while the operator has to plan before households and businesses switch on equipment that will require supply.
Forecasting how much power customers will need therefore becomes part of a financial transaction.
PJM's July auction for the 2028-29 delivery year produced approximately $16.41 billion in auction revenue. In a recent analysis, its independent market monitor, Monitoring Analytics, reran the auction without the forecast growth in demand from new data centers and expansions of existing facilities, while leaving other assumptions unchanged.
The resulting revenue fell to approximately $10.7 billion, a modeled difference of $5.7 billion for that delivery year.
Revenue for the 2028–29 capacity delivery year. The counterfactual removes forecast growth from new and expanding data centers, retains existing load, and holds other assumptions fixed. The gap is a modeled capacity-market effect, not a household surcharge or an estimate of AI alone.
CryptoSlateThe calculation shows how the forecast growth affects revenue inside the monitor's model, using the auction's rules and its existing assumptions about available supply.
While these numbers are only an estimate of generative AI demand, the financial exposure is still substantial, and it starts with demand expectations rather than requiring every proposed building to be operating.
Planning too little capacity creates an obvious risk of shortages, while planning for projects that consume much less power than expected can leave expensive infrastructure with fewer customers than its financing assumed.
Those decisions reach households through utilities and electricity suppliers, with the eventual effect depending on contracts and local regulation.
Capacity payments are one part of power costs, so an increase in that market can't simply be relabeled as the same percentage increase in a residential bill. Customers whose suppliers have already secured power under contracts may see a different result from customers more directly exposed to new market prices.
Virginia regulators have established a separate rate class for Dominion Energy Virginia's large users and requirements intended to make them cover more of the costs associated with serving them, including minimum payments and contractual safeguards.
The requirement to take and pay for electricity for at least 14 years applies to new large-load customers contracting for service on or after Jan. 1, 2027.
Those eligibility rules determine which projects the safeguards cover and whether other customers remain exposed if a developer reserves substantial capacity and subsequently needs less of it.
Tech companies can pay their own invoices and still affect prices elsewhere, because increased demand can require more expensive supply or new shared infrastructure.
Equally, a large customer making reliable long-term payments can help spread existing costs across more consumption, which is why the evidence doesn't support treating every data center as an automatic surcharge on the neighborhood.
The AI savings still have to reach somebody
The MIT working paper revised in October estimated that data-center entry raised residential electricity rates by 2.1%, with larger effects among investor-owned utilities, using data from 2010 through 2024.
Researchers behind another working paper, using a different method and covering 2015 through 2024, estimated a modest average reduction in retail rates and warned that future supply constraints could reverse that effect.
However, both studies analyzed different units and used different methods to identify the effect. Also, both studies largely precede the current scale of AI expansion and data center construction.
Their disagreement on the data is a good reason to look at local systems and the rules governing them, rather than choosing whichever national average makes the most satisfying claim.
Someone using AI to complete a task faster has experienced a real benefit, but turning that saved time into lower prices across an economy will take a lot more time.
Businesses have to organize work around the technology, produce more with the resources they have, and pass some of the resulting savings to customers rather than retaining all of them.
We are yet to see that transition happen. A survey of nearly 6,000 executives across four countries found that 89% reported no effect on their firm's productivity over the previous three years, while other Fed-linked research found evidence consistent with productivity gains among adopters.
In its Nov. 5 preliminary release, the Bureau of Labor Statistics reported that nonfarm business labor productivity rose 3.6% in the third quarter, while unit labor costs fell 0.7%, both at seasonally adjusted annualized rates. Businesses produced more per hour worked, with labor compensation per unit of output declining.
However, the release doesn't identify how much AI contributed to those gains or establish how much of the savings reached customers.
Nonfarm business sector, percent change from the previous quarter at a seasonally adjusted annual rate. Figures use the November 5, 2026 release; Q3 2026 is preliminary. The series measure output per hour and compensation per unit of output, without identifying AI's contribution or savings passed to consumers.
CryptoSlateEven widespread productivity improvements wouldn't automatically produce an equally large fall in inflation.
Fed Governor Lisa Cook explained in September that expectations of higher future incomes and returns can encourage more spending today, adding demand alongside the technology's eventual increase in supply.
Companies are already investing in anticipation of those gains, while households holding appreciated assets may also be more willing to spend.
The RBA expects data-center investment to support business activity, but estimates that roughly 70% of the additional investment will be imported, limiting the direct boost to domestic production even as local construction requires labor and materials.
Its November decision addressed a broader inflation problem, with AI contributing to particular pressures in an economy already facing plenty of others.
The companies building AI are making private bets on future demand, while regulators and utilities must decide how much of the infrastructure required to meet those bets other customers should pay for.
While greater productivity could eventually benefit households and businesses far beyond the technology industry, those future gains don't settle who should pay for grid expansion, or who pays if a project requests more power than it needs.
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