January’s Jobs Report Moved in Only One Direction; AJSN Showed Latent Demand Up ¾ Million

The first month of the year might set the tone for the next 11 – and it will probably set expectations.  What happened this time?

The number of net new nonfarm payroll positions almost doubled the 69,000 estimate I saw at 130,000.  Seasonally adjusted unemployment fell 0.1% to 4.3%, with the unadjusted variety up a seasonally expected 0.5% to 4.6%.  The adjusted count of those unemployed dropped 100,000 to 7.4 million, with the number of long-term jobless, or those out for 27 weeks or longer, off the same amount to 1.8 million.  The two measures of Americans closest to the workforce, the employment-population ratio and the labor force participation rate, each gained 0.1% to 59.8% and 62.5%.  Average nonfarm hourly payroll wages added 15 cents, about the same as inflation, to $37.17.  Maybe best of all, the count of those working part-time for economic reasons, or keeping short-hours jobs while seeking full-time ones, ended its two-month bulge by falling 400,000 to a more normal 4.9 million.

The American Job Shortage Number or AJSN, the measure showing how many additional positions could be quickly absorbed if all knew they were exceptionally readily available, gained 752,000 to reach the following:

With most of the categories of marginal attachment above shrinking, and unemployment seasonally up over 900,000, the share of the AJSN from the latter rose 3.2% to 40.9%.  Compared with a year before, the AJSN was about 400,000 higher, but that was one of the smallest differences since 2024 – the largest contributor was again official joblessness.  In the comparisons between January’s data and that from a month and a year before, another large difference is from those non-civilian and institutionalized, which fell over 800,000 because of the Census Bureau’s newly reduced population estimates.

Just how good was January’s data?  Adding to the above the expected unadjusted 630,000 employment fall, the 143,000-lower number of those not in the labor force, and about 100,000 fewer people not interested, January was a fine month indeed.  There are always possible illusions and unpublicized affecting events, so we will need to see if February’s report reverses these results or continues them.  For now, though, the turtle took a big step forward.

A Couple of Months of Artificial Intelligence Infrastructure Events – A Crisis on the Way?

As AI moves along, the issues reporters and commentators are most likely to discuss have changed.  For a year or two, there was much talk about where AI would get its data, and if there was even enough to support future releases.  Lately, we have heard much more about power, water, and the public’s reaction to data centers being built near where they live.

First, we have “Data centers rapidly transforming small-town America” (Sumner Park, Fox Business, December 6^th).  The Newton County, Georgia one described here isn’t primarily even for AI, but “where data for Facebook, Instagram, WhatsApp and Meta’s other platforms is processed and pushed at record speeds.”  Despite “creating hundreds of jobs, supporting local contractors and generating long-term tax revenue for schools and public services,” “not everyone is thrilled about it.”  A county commissioner called it “all pie in the sky” with “lucrative promises,” making it “the biggest smoke-and-mirror thing you’ve ever seen,” especially considering “what happens years from now if the industry’s footprint shifts and the massive buildings are no longer needed.”  Other concerns from local people centered around water, electricity, and percussive damage from construction blasting.

Elsewhere, an “Arizona city unanimously rejects AI data center after residents’ outcry” (Alex Nitzberg, Fox Business, December 12^th).  The place, surprisingly, was Chandler, where locals gave blessings to other front-line technology in the form of autonomous vehicles, which I saw in 2019 developing capability on their streets.  This time though, its “city council voted unanimously… against clearing the way for construction of an AI data center,” and “cheers and applause erupted after the unanimous vote outcome was announced.”  According to a related story in Fox News, worries about water and energy use were the reasons.

On one of those factors, we saw “Senators Investigate Role of A.I. Data Centers in Rising Electricity Costs” (Ivan Penn and Karen Weise, The New York Times, December 16^th).  “Three Democratic senators” sent letters “to Google, Microsoft, Amazon, Meta and three other companies,” saying that “the energy needs of data centers used for artificial intelligence were forcing utilities to spend billions of dollars to upgrade the power grid,” and “tech companies are passing on the costs of building and operating their data centers to ordinary Americans,” which “has caused residential electricity bills to skyrocket in nearby communities.”  This issue does not seem likely to go away soon, as the “Data center boom powering AI revolution may drain US grids – and wallets” (Arabella Bennett, Fox Business, January 13^th).  Bennett mentioned water, but power prices and jobs, as “while about 1,500 workers may be needed to build a data center, fewer than 200 typically stay once operations begin,” were her main contention points.

More on that other resource came up in “Microsoft Pledged to Save Water.  In the A.I. Era, It Expects Water Use to Soar” (Adam Satariano, Paul Mozur and Karen Weise, The New York Times, January 27^th).  At that firm, while last year’s “internal forecasts… show(ed) the company expected its annual water needs for roughly 100 data center complexes worldwide to more than triple this decade to 28 billion liters in 2030,” more recent estimates, using “new water-saving techniques,” show 18 billion liters instead, though they fail to “include more than $50 billion in data center deals that the company signed last year.”  Other problems mentioned here include Amazon’s abandoning of “a planned Arizona complex over water concerns,” Google’s 2024 withdrawal of “plans for one in Chile,” and, possibly referring to the Newton County effort above, “residents have also blamed a Meta data center in Georgia for harming supplies of drinking water.”

Along with fossil products and conventional nuclear, per Bret Baier in Fox News on February 2^nd, “Artificial Intelligence helps fuel new energy sources.”  Chicago’s power provider Commonwealth Edison has asked for a $15.3 billion “grid update as potential data center projects total more than 30 gigawatts through 2045.”  Through Commonwealth Fusion Systems, it “is working to add a new form of nuclear energy to the grid – fusion.”  The piece also mentions geothermal energy as a possibility if current drilling research is successful.

Accordingly, the issues of finding both suitable and accepted locations, powering them, and fulfilling their water needs are no longer, for AI data centers, matters of straight logistics.  This large area will need plenty of attention this year.  If overall costs get much higher, it may aggravate another growing problem – a shrinking pool of venture capital.  If there are still enough places to build the capability artificial intelligence companies need at viable prices, they will get through this growing set of impediments.  If not, their impact could be severe.  We will see.