The rising risk of the AI-versus-everything-else economy
More and more, it seems as if there’s the AI economy — and everything else.
The growing chasm between the breakneck growth of the AI world and the slower pace of the economy where most Americans work and live has huge implications, especially for the markets.
The most recent numbers on U.S. construction spending through August — shown above — paint the picture remarkably clearly.
Data: U.S. Bureau of Labor Statistics, FactSet; Chart: Axios/Matt Phillips; Note: Data center construction costs include labor, materials, profits of contractors, architectural and engineering work, miscellaneous overhead, interest and taxes. Does not include servers, racks, chips or memory.
According to U.S. Census Bureau data, private companies are building data centers at an annualized pace of $85 billion in 2026, representing a 73% increase from 2025. Apollo Global Management notes that AI investment is accelerating at 0.85 percentage points of GDP annually.
Apollo’s Torsten Slok said consensus forecasts imply hyperscaler capital expenditures will reach about 3% of U.S. GDP annually between 2027 and 2029, up from 0.3% in 2019 and 1.4% in 2025.
ING’s calculations reveal the sluggishness of private investments other than AI:
- If we group computing and peripheral, together with software investment, we see a cumulative 62% increase in real spending since the first quarter of 2022. Other non-residential business investment fell in year-on-year terms for six consecutive quarters between 4Q 2024 and 1Q 2026.
- ING’s best estimate is that AI is responsible for 44% of US GDP growth in 2026, reduced to 36% after taking into account AI-related imports.
- ING then considers the wealth effect on consumer expenditures: “$33tn of extra household equity wealth translates into roughly $500bn of cumulative extra spending since fourth quarter 2022. Simplistically, that is around an extra $36bn of consumer spending per quarter ($144bn annualised), equivalent to around 0.65pp of 2Q 2026 consumer spending and 0.44pp of GDP which was 2.2% in Q2 and is estimated at 3.0% in Q3. The AI wealth effect could be contributing 15% of GDP growth.
All these numbers are rough estimates but confirm that the AI boom is responsible for 40-50% of US growth this year. A non-AI US economy would currently be expanding at a sluggish pace of roughly 1.0% to 1.3% annualized.
Other indications that the US economy is largely being supported by AI investments:
- Employment for people who build the equipment and parts that data centers run on — including gas turbines to generate power, wiring, electrical switchboards and other specialized components — is one of just two categories of manufacturing jobs that have consistently grown in the past few years, according to an analysis by Jiaxin He, a research assistant at the bipartisan think tank Economic Innovation Group. (Washington Post)
The One Big Beautiful Bill Act (OBBBA) also provided a significant fiscal boost to short-term economic momentum. The official CBO Dynamic Estimate calculated that the OBBBA contribution to real GDP growth would peak at 0.9 percentage points in 2026. “Because the effects of changes in aggregate demand would subside quickly, the temporary boost from demand-side factors would diminish after 2026.”
Axios continues:
The same divide is an important theme in the markets as well. In corporate bonds, AI-related offerings accounted for more than half of net investment grade issuance for the year through the end of August, according to data from Apollo and Bloomberg.
Data: Apollo, Bloomberg; Chart: Axios/Matt Phillips
The weighting of the S&P 500 technology sector in terms of market capitalization is now roughly 40%. That’s higher than during the peak of the dot-com bubble.
- But that actually underplays the impact of AI.
- If you throw in the hyperscalers Meta and Amazon, which S&P doesn’t include in the tech sector, the share of the AI-pilled part of the blue-chip index climbs closer to 50%.
Taking the cue from Kevin Warsh and listening to what the market is saying (since the August highs):
- S&P 600 Small Caps: –8.6%
- S&P Banks: –11.0%
- BAC: –17.0%
- JPM: –9.0%
- GS: –23%
- KKR: –22%
- KBW Regional Banks: –12.8%
- S&P 500 Industrials: –10.0%
- Restaurants: –14.0%
- MCD: –15.2% (-38% since March)
- Retailers: –9.6%
- HD: –18.5%
Ed Yardeni calculates that 42% of S&P 500 stocks are down year-to-date. He also shows that only 33% of stocks have outperformed YoY. The bar has been set high, however, with the S&P 500 up 12% and 14% YoY in 2025 and YtD in 2026 respectively. We have been around 30% of outperformers several times in the last 2 years. Hopefully, three’s a charm.
Looking forward, some economists argue that the rise in energy costs and short term interest rates will combine for a $600B hit to the American consumer, 2-3% of disposable income which was +4.6% in Q2’26.
Then the midterms could pit Congress vs the White House for 2 years. Impeachment? Government funding? Tax increases?
Will the war in Iran become the Forever War?
What about the whole Middle East boiling towards exploding?
Ned Davis monitors trends in supply vs demand of equities. We are at 50-50. I added the dotted verticals to NDR’s chart. Not definitive but a solid warning to be monitored.
The 13-34 week moving averages are still long-term bullish however.
(stockcharts.com)
My friend Hubert Marleau:
Meanwhile, chances of a 25-bps hike in the Fed’s policy rate at the October 27-28 FOMC meeting are only 22%, with odds of two hikes by December at just 3%, in part because the economy, in affordability terms, might not be in the best of shape to withstand any interest rate increases.
Efforts to tame the volatility of the 10-year Treasury yield, the most important number in global finance, have failed for two reasons:
1) A dramatic change in the composition of bond buyers from insensitive foreign reserve managers to sensitive private investors like hedge funds and carry-trade international players, has occurred, stemming from the weaponizing of the US dollar and out-of-control fiscal spending.
2) A huge amount of international money has flowed into the AI trade, virtually making it the new US capital market. The ratio of US net international investment position (NIIP) to the sum of all rest-of-world net creditor nations’ NIIP has surged to 80%- a clear gauge of the concentration and allotment of the world’s savings that has been sucked into dollar investments.
This has impacted the momentum of the bond market beyond what conventional theory would normally have figured, and it’s reflected in the bullish performance of the US dollar, which is perching at 102-ish, its highest level since last spring, even though the near-term hike narrative has become less threatening. (…)
Despite October’s gruesome fame, it’s actually one of the best months for stock returns. So far, the S&P 500 is green, but not immune to the rising cost of money. The economy has slowed down, but still remains in expansion territory as reflected by the September ISM index, which rose to 54.9 with higher prices received for goods and services, guaranteeing a 3% growth factor for Q3 and beyond and a positive outlook for corporate profits. (Incidentally, the latest third quarter GDP growth estimate, generated by the Atlanta Fed, is 3.6% and 2.8% for Q4.)
Without much else, a handful of AI mega-cap companies, firing on all cylinders, drove the S&P 500 to a new record high of 7,819 on Tuesday. Suddenly, all hell broke loose on Wednesday and Thursday. Oil prices surged as war was about to resume in Iran, the cost of money rose in response to the FOMC’s view that the current monetary policy is not restrictive, there was talk of a potential French debt crisis not being far off for Les Misérables, and the rapid weakening of the yen, combined with rising Japanese interest rates, accelerated the unwinding of the yen-carry trade, sparking fresh selling in both bonds and stocks.
Nonetheless, the S&P 500 held up remarkably well for the week as a whole, up 1.0% to close at 7811, despite this collection of forces, which is difficult to dismiss as mere background noise.
What is most important is that the AI boom remains powerful, accelerating the expected year-over-year increase in S&P 500 earnings per share for Q3 to 27%, after registering blockbuster gains of 30% and 25% in Q1 and Q2, respectively, while fabulous earnings are broadening across corporate America.
Analysts expect all 11 S&P 500 sectors to deliver positive y/y growth in both revenues (11.6%) and earnings (30.6%). And this phenomenon is not about to stop, according to Yardeni Research, which came out this week with its earnings-per-share prospects for the S&P 500 over the remainder of the decade, projecting $450 per share in 2028, $475 in 2029, and $500 in 2030, which supports a S&P 500 target of 10,000 before the end of 2029.
It should be added that the street is more bullish on earnings per share, forecasting $419 in 2027 and $489 in 2028. These elevated long-term growth assumptions are enough for me to keep my year-end target of 7,925 and 8,700 for 2027.
Equity prices are less demanding after higher oil prices and bond yields stalled the rally, while the outlook for profits is still healthy, the team led by Christian Mueller-Glissmann wrote in a note. They raised their three-month view on stocks to overweight from neutral, matching their bullish stance over a 12-month timeline.
“Earnings growth remains supportive for equities and the recent valuation de-rating has improved asymmetry into the third-quarter earnings season,” Mueller-Glissmann said. (…)
Analysts expect S&P 500 companies to post a 25% jump in third-quarter profits compared with a year earlier, among the best rates of increase on record, according to data compiled by Bloomberg Intelligence.
Other Wall Street strategists, including at Bank of America Corp and Barclays Plc, have also turned more bullish about the outlook for US stocks recently, citing among other things a robust earnings outlook. (…)
FYI:
Q3’26 earnings are another K-shaped trend per LSEG IBES: 5 sectors, all either commodity (war) or AI-related are expected to post earnings growth or 57.4%. The other 6 sectors, (consumer discretionary and staples, financials, health care, real estate and utilities) are seen growing earnings 4.2% on average.
For all of 2027, total S&P 500 earnings are indeed forecast to rise 15.9%. Only 3 sectors will beat that however, growing earnings 26.8% on average. The other 8 sectors are expected to post earnings up only 2.4% on average.
That’s in an economy still strongly supported by AI:
The Desperate Hunt for AI Computing Power Is Upending Silicon Valley
The WSJ paints the hunt for scarce compute power. The main points:
- Compute, the industry shorthand for computing power [i.e. GPUs, CPUs], is the lifeblood of the AI boom, and it’s in short supply. Now fights over how to get more of it—and what projects to give priority to—are sending a powerful industry scrambling. (…) “There’s a big gold rush happening at the moment. It’s hard to fully comprehend the scale.”
- Established suppliers of computing power are requiring customers to sign multiyear contracts with heftier down payments. Startups are increasingly inking deals with unreliable providers out of desperation, like ones lacking proper security or weaker infrastructure.
- With so much demand, prices are going up. The hourly cost of renting computing power from Nvidia H100 chips [2022 vintage] on a one-year contract has risen 60% in the past year, according to SemiAnalysis, a data and consulting firm.
- To hedge against the unpredictable nature of securing computing power, some companies are signing contracts for more capacity than they need. In other cases, startups have predicted surging demand for their AI products that never materialized, forcing them to resell leftover capacity. This confluence of factors coupled with rising wait times and skyrocketing costs of compute are crippling a generation of newcomers trying to compete with Google, Anthropic and OpenAI.
- Some venture-capital firms are now reserving clusters themselves and renting the capacity back to their startups, or offering to guarantee contracts with compute providers.
- “There’s a lot of capital floating around for these companies, but there’s not a lot of deep intelligence on how to build these things.”
The need for compute has exploded since “Claude” launch last spring. AI agents are quickly becoming ubiquitous. Wait till we get autonomous cars and robots constantly monitoring their environment and talking among themselves. It’s actually very, very hard to fully comprehend the scale.
Nathaniel Whittemore’s Oct. 6 AI Daily Brief episode discussed whether “Consumers will never pay for AI” (starts at 13 min.).
In 1992, I was explaining to an audience of smart, busy investors why my equity fund included fledging cellular-related companies. When I said that in the not-too-distant future everybody would have a small phone tucked to their belt or inside their purse and that we could all be reached 24/7, the crowd exploded: “No way!”, “Over my dead body”!
It took 6.5 years for smartphone penetration to grow from 2% to 50%. It’s now 91% of all US adults.
ChatGPT went from 900 million weekly active users in early 2026 to 1.2B now. There were 35M paid subscribers in July 2025. That jumped 43% to 50M in February 2026 and OpenAi targets 122M by the end of 2026 when AI apps will only get started.
My son Danny has been experimenting with the recently launched ChatGPT DOT personal agent. Often quick to find flaws in new apps, yesterday he wrote: “A real game changer. The biggest evolution since the arrival of ChatGPT.”
According to The Information, only about 4.5% of eligible U.S. consumers currently pay for a premium AI subscription (across ChatGPT, Claude, or Gemini).
Most are using it for simple queries. Soon, they will start using agents for diverse tasks.
I recently queried Gemini to give me trends on Chinese export data for a few specific commodities. It gave me the data and built the charts. Then, to my surprise, it casually offered to create an agent for me that would monitor these trends and alert me if some pre-specified events occur. That agent would be constantly working in the background, monitoring the data for me, and pinging me on certain events.
Imagine…
Examples of surging, unmet demand keep coming:
Nvidia-Backed Lumentum Sold Out of AI Server Parts Till 2029
Lumentum Holdings Inc.’s optical components are “completely sold out” through early 2029 on demand from tech companies clamoring for faster AI data centers, according to its chief executive officer.
Lumentum is unable to meet about 70% of demand for some products through next year, and won’t be able to address 30% of demand for some other products through 2028, Michael Hurlston said in an interview in Tokyo Friday. Customers are already starting to lock in orders for 2030, he added.
The company, which received an investment of $2 billion from Nvidia Corp. earlier this year along with rival Coherent Corp., supplies advanced indium phosphide devices that help enable high-speed cloud computing and data transmission. Analysts on average expect Lumentum’s sales to more than double this year.
“We are really trying to catch up, add capacity as fast as we can, but we are very, very far behind what customers want,” Hurlston told Bloomberg News. “We are completely sold out, so there’s no end in sight.” (…)
Hurlston shrugged off fears of a pullback. The buildout remains intact, and the sector is on the cusp of a bigger opportunity when data centers adopt optical technologies at shorter distances to AI accelerators, he said.
Silicon photonics, which uses light to transmit data, is expected to replace copper-based interconnects in data center clusters as advanced AI hardware demands faster transmission. Optical connections, which generate less heat and consume less power, are also moving progressively closer to the chips. In addition to connecting data-center racks to one another, they are increasingly eyed as ways to link tiny components on the same circuit board.
Lumentum offers a type of laser that’s used to encode data onto light near the chip. Since that means high-speed electrical signals only have to travel a few millimeters, that cuts power loss and heat emission even further.
“This market can be our biggest by far,” Hurlston said, adding that demand for lasers in next-generation AI systems is expected to gain traction around 2029. “It’s another order of magnitude in terms of size.”
The sector is also bracing for further supply constraints as the US reportedly considers a ban on new Chinese optical transceiver models. Lumentum and Coherent would not be able to fill the vacuum left by Chinese makers such as Innolight, according to Counterpoint Research. Roughly two-thirds of the world’s high-speed AI optical transceivers come from Chinese makers. China also remains a big source of indium-phosphide substrates used to make lasers and other optical components.
The US needs to balance national security concerns with the risk of disrupting the supply chain, Hurlston said.
To meet demand, the San Jose, California-based company has ramped up capacity 12-fold at its Sagamihara facility — one of the world’s most advanced indium-phosphide device production sites — in the Tokyo metropolitan area over the past two years. It plans to invest at least $350 million in the site and a neighboring facility, of which it’s deployed $75 million so far. It’s also expanded capacity at its plant in Caswell, England. (…)
It takes three to five years to build new production capacity (…).
One factor that allays Lumentum’s concerns about fast-paced ramp ups is the willingness of hyperscalers to take on some of the risk that suppliers take on when they build new production facilities.
“These hyperscalers are willing to put money behind it,” Hurlston said.
In case you wonder, Lumentum is also expanding in the US:
- In March 2026, Lumentum announced the acquisition and multi-hundred-million-dollar retrofit of a 240,000-square-foot production facility in Greensboro, NC. Acquired from Qorvo, this fab is dedicated to manufacturing advanced indium phosphide (InP)-based lasers and optical devices. It is expected to fully ramp up production in mid-2028.
- Lumentum maintains an active semiconductor facility at its headquarters in San Jose, CA. In August 2025, the company funded a massive capacity expansion here to produce ultra-high-power (UHP) lasers used in next-generation Co-Packaged Optics (CPO) platforms.
- A $2 billion investment and purchasing agreement with NVIDIA has financially anchored Lumentum’s U.S. fabrication roadmap. NVIDIA is a primary customer backing these U.S. facilities to ensure it has domestic access to critical optical components.
How the AI Boom Ends Demand isn’t about to let up, but prices will start plunging by factors of 10.
Andy Kessler in today’s WSJ:
I can tell you how the artificial-intelligence data-center boom will probably end, but I can’t tell you when. (…)
AI token prices have been dropping but are set to plunge faster than most anticipate. (…)
Last week, according to OpenRouter, OpenAI and Anthropic were charging a premium for some of their models—$1 to $2 per million input tokens. Comparable Chinese open-weight models like Z.ai’s GLM and Alibaba’s Qwen charged 6 to 12 cents, around 90% less—that factor of 10 inflection point. (…)
AI architecture is evolving. Instead of a trillion parameters, efficient small language models might use 70 billion and run on an Apple Mac. These local models have no token costs beyond the cost of hardware and electricity.
But they must be less capable than the large language models, right? Not really. Stanford researchers show that a locally run small language model “can successfully answer 88.7% of single-turn chat and reasoning queries.” No question we’ll always have high-end models running in data centers. IBM still sells mainframes. But growth will happen elsewhere.
Where? Another Silicon Valley saying I’ve lived by: Intelligence moves out to the edge of the network. The real breakthrough will happen when robust AI models run on smartphones. It’s already starting. In September, California-based PrismML released a compressed version of the popular open-source Qwen3.8 27B AI model. The company got it down to under 6 gigabytes. That means it fits not only on PCs but also on high-end smartphones. The memory size is reduced by roughly 9 to 10 times. There’s that factor of 10.
Technology doesn’t stand still. Qualcomm recently announced a new Snapdragon neural processing unit, or NPU, for smartphones. Using what the company calls a “mixture-of-experts” model, it could run a 30 billion parameter AI model in 3 gigabytes of memory. According to the Deep View, the largest AI model on a phone has been four billion parameters on an Apple A20 Pro chip. Qualcomm provides a 7.5 times boost.
NPUs, like Qualcomm’s new Snapdragon and Google’s Tensor chips in its Pixel phones, are multiplying. Same for the latest Apple M- and A-series chips, engines that can run AI models locally on iPhones—maybe why Apple hasn’t joined the AI data-center spending delirium.
A Nvidia-backed startup, Reflection AI, claims it matches leading Chinese open-weight AI models with “3-4x less inference compute.” That’s it? Only 3 or 4 times? Wake me up when it’s 10 times. At the pace AI is moving, that could be year-end.
Agents are all the rage, from Meta’s Muse to xAI’s Grokbot. They run on virtual machines in data centers but raise all sorts of privacy concerns. Startup Perplexity recently teamed up with Nvidia to run agents on PCs. Zero token costs. And you own your data.
I often hear, that if tokens get cheaper, demand will go up. That’s Jevons Paradox. Sure, elasticity works. But if prices drop 90%, demand must increase 900% just to stay flat. Remember when internet demand grew while debt-laden Global Crossing spectacularly failed because prices dropped too fast? Historically, semiconductors got 30% cheaper every year. When prices dropped by more than 50%, disaster struck with fabrication plant closings and company failures.
Will 2027 see peak data center? Again, timing is dicey. Current demand is gangbusters, but spending is a game of chicken until multiple factor-of-10 improvements kick in and prices collapse. This could set off a cascade of data-center halts and debt defaults, even as token demand increases.
Yes, model training, drug discovery and winning prizes like the recently solved Navier-Stokes equations require hot chips in data centers. But chatbots and agents and other tasks may increasingly be handled on PCs and phones and smart glasses and whatever new gizmos Silicon Valley invents over the next decade.
I’ve heard this called the “Great Migration.” Mr. Jurvetson’s factor of 10 is visible now, as it often is, but ignored. Change doesn’t happen overnight. It happens gradually, then suddenly.
Had Andy used AI to criticize his own article he might have read things like this:
- While the piece correctly identifies strong historical tech patterns (like downsizing and cost deflation), its core thesis suffers from several structural flaws, economic misinterpretations, and questionable technical assumptions.
- The author attempts to invoke Jevons Paradox but completely flips its economic meaning. The author claims that if prices drop 90%, demand must increase 900% just for revenue to “stay flat,” framing this as a steep hill to climb. However, historically in digital tech, a 90% drop in computing or data costs triggers a 10,000% or greater explosion in demand. When data became cheap, we didn’t just send more text emails; we started streaming 4K video. Lower token prices don’t collapse the market; they make embedding AI into every micro-interaction economically viable.
- The article pits edge computing (on-device) against data centers as a zero-sum game, assuming edge growth will cannibalize data center demand. On-device AI and cloud data centers handle fundamentally different workloads. Running a 30-billion-parameter compressed model locally is great for basic text tasks, predictive typing, or localized voice assistants. However, it cannot replace data centers for massive multi-modal workloads, heavy synthetic data generation, real-time complex reasoning (like multi-step coding agents), or continuous foundational training. The “Great Migration” is actually a hybrid model: edge devices will act as filters for simple tasks, while data centers handle the complex, heavy-lifting cognitive tasks.
- About the Stanford 88.7% figure: The study covers only single-turn chat and excludes agent and long-document tasks, where local models trail by up to 45 points.
- Apple isn’t skipping data centers; it’s renting them. Its new Siri runs on a custom 1.2-trillion-parameter Gemini model, partly on Google Cloud Nvidia Blackwell GPUs. Apple’s restraint moves the spending to Google; it doesn’t remove it.
- Perplexity’s “zero token costs” is great for simple tasks. But it sends harder steps to the cloud with a subscription.” Owning your own data” will only encourage usage of AI agents.
- The article assumes data centers are built primarily to serve the current level of chatbots. The data center boom is primarily driven by an aggressive arms race to train next-generation frontier models and run large-scale simulation environments. Even if inference completely migrates to the edge, the computing power required to train the next generation of models continues to grow exponentially, meaning data center demand will remain robust.
- The author selectively picks historical tech cycles to fit a narrative of impending doom. While Global Crossing defaulted during the dot-com crash, the physical fiber-optic cables they laid down laid the foundation for the modern internet boom. Similarly, even if some current data center debt structures face turbulence, the physical infrastructure (gigawatts of power, fiber, and cooling) will remain highly valuable assets, not useless relics.
FYI: Google’s monthly token volume went from 480 trillion to 3.2 quadrillion in one year, a 7x jump, and 330x over two years. That was only Google, before agents, autonomous cars, robots, etc.
China Passenger Vehicle Retail Sales Drop 24% in September
China’s passenger vehicle retail sales fell 24% in September from a year earlier to 1.702 million units, while new-energy vehicle sales declined 12% to 1.141 million units, according to data released by the China Passenger Car Association.
Cumulative retail sales for 2026 reached 13.42 million units, down 21% from the same period a year earlier, the association said. New-energy vehicle retail sales increased 14% from the previous month, with year-to-date sales totaling 7.816 million units. They were down 12% on year.
The association said new-energy vehicles accounted for 67.1% of passenger vehicle retail sales in September, while their share of wholesale volumes reached 66.1%.
Geely Automobile Holdings Ltd. plans to launch in Canada next year, positioning it be the first Chinese mass market electric vehicle maker to establish a presence in the North American market.
The carmaker has begun setting up a retail and service network in the country ahead of the 2027 start, according to a company statement Friday. Geely, which owns premium EV brands including Zeekr and Lynk & Co as well as more affordable offerings, didn’t specify a start date for sales or which models would be available.
The move marks a significant step after Ottawa and Beijing reached an agreement in January to allow a limited number of Chinese EV exports to Canada. As trade disputes deepen with the US, Canadian Prime Minister Mark Carney’s administration is looking to forge new alliances, including by allowing Chinese automakers in and hoping to attract investments from them. (…)
Chinese rivals BYD Co. and Chery Automobile Co. are also wooing dealers and setting up distribution. (…)
Canada Employment Falls by 68,300 Amid Public Sector Decline
The unemployment rate ticked up to 6.5% in September from 6.4% in August, Statistics Canada reported on Friday.
The job loss reflected a decrease in public sector employment, which fell by 70,000. Education led the decline, with 35,300 fewer people working the sector. Employment also fell in healthcare and social assistance by 23,100.
Manufacturing saw a decrease in employment of 12,700.
While the job losses last month were driven by the public sector, the cumulative decline in employment since the start of the year paints a sluggish picture of the labor market. Employment fell in Canada by 41,200 since December 2025, the largest year-to-date loss since the 2009 financial crisis, excluding 2020 during the Covid-19 pandemic.
The September data also gives the first glimpse of the impact of so-called Section 338 tariffs on the Canadian economy. New 50% US tariffs on $20 billion of Canadian goods took effect on Aug. 22 after the breakdown in trade negotiations between the two countries, while retaliatory tariffs on US goods kicked in on Sept. 8. Job losses in August and September total 110,000. (…)
Friday’s report also shows the participation rate declined by 0.2 percentage points to 64.8%, marking the lowest level since December 1997 — excluding 2020 during the pandemic.
StatCan says the decline in the participation rate can be attributed largely to population aging, and says this trend is expected to continue, despite older Canadians participating in the labor market at higher rates compared to previous cohorts. (…)
The average hourly wage for full-time permanent employees rose by 2.3% from a year ago, matching economists’ expectations, and up from 2% the previous month.
The decline in employment was driven entirely by a significant drop in public-sector employment. (@RichardDias_CFA)
Deadly Attacks on Saudi Arabia Risk Wider War That Draws In US
This weekend’s deadly attacks on Riyadh’s main airport have demonstrated the ability of Yemen’s Houthis to strike at the heart of Saudi power, raising the risk of a dangerous escalation that could draw in the US. (…)
The attacks put de facto ruler Crown Prince Mohammed bin Salman under mounting pressure to escalate a war the country has spent years trying to escape, against an opponent that’s arguably more capable than it’s ever been. They also present difficult choices for the US and the kingdom’s new defense partners, Turkey and Pakistan, over how far to go in helping Saudi Arabia respond. (…)
Saudi Arabia’s Foreign Minister Prince Faisal bin Farhan and his Iranian counterpart Abbas Araghchi discussed the latest escalation in the region in a phone call, according to a report Sunday by the state-run Saudi Press Agency. (…)
The group has survived years of Saudi and US bombing campaigns, and the latest strikes demonstrate how difficult it may be to stop it from inflicting further damage on the kingdom. (…)
“It is very unlikely that the Saudis can stop the attacks if the US could not — short of sending in ground forces or dramatically improving the capability of the Yemeni fighters it is supporting,” Pettyjohn said. “Suppression requires constant air patrols searching for launchers, which is difficult. And the Yemenis seem to have used the time since the ceasefire with the US to rearm.” (…)
The escalation also poses a test for Turkey and Pakistan, which signed a defense pact with Saudi Arabia in August, known as the Mecca Defense Alliance. Both have signaled willingness to send troops or weapons to help defend the kingdom, but Turkey has ruled out joining offensive operations against the Houthis. (…)
