CONSUMER WATCH
Retail Sales Well Below Expectations
The facts:
- Retail sales declined -0.6% MoM in July vs a median forecast of +0.1% after +0.2% in June.
- Retail sales ex-autos: -0.3% after -0.2%.
- Retail sales ex-autos & gas -0.2% after +0.4%.
- Retail sales core/control -0.4% after +0.4%. The level of core retail sales was revised down by 0.4% in June, reflecting a -0.18pp downward revision to core retail sales growth in June and a -0.23pp revision to May.
Recall that prior months sales were likely boosted by the FIFA tournament and, certainly, by Amazon Day having been switched in June from July in 2025.
Two ways to analyze retail demand:
- On a YoY basis, total retail sales are still up 5.0%, down from 7.2% in May and 6.8% in June but well above Q1’s +3.9% growth rate. Not seasonally adjusted, retail sales rose 5.2% YoY. But sales in 2025 were the weakest in May-July helping the YoY comps.
- On a MoM basis, June-July aggregate sales were down 0.34% following 4 very strong months averaging +1.1% monthly. The post Amazon Day-FIFA setback is significant. Q2 annualized sales were up 11.9% after +5.4% in Q1 and +1.7% in Q4’25. Core sales (ex-gas) were flat in June-July.
All data are nominal, not inflation adjusted.
Bank of America’s latest Consumer Checkpoint has this great chart stripping gasoline and online purchases from its credit/debit card data. YoY sales growth were comfortably above 3% through the end of June, became erratic in early July before sinking well below 3.0% in the last 3 weeks of July, leaving little, if any, growth in real terms.
Americans’ “resiliency” will be tested in coming months as tax cuts, FIFA effects and the Prime Day switch are all behind us. Meanwhile, personal income growth at 3.5% (Q2) is no longer beating inflation (+3.8% in Q2 and +3.4% in June-July). The latest U. of Michigan survey revealed that “Across all consumers, only 8% expect their income growth to exceed inflation in the year ahead”. The 1-year inflation expectations is 4.3%.
Thankfully, AI keeps helping. Not so thankfully, the Middle East is still very messy.
BTW:
The UMich report noted that “Decreases in sentiment were seen across the political spectrum, with Republicans exhibiting the strongest month-to-month decline in August. Sentiment among Republicans is now 19% below readings just prior to the Iran conflict and the lowest since the 2024 election.”
ZeroHedge: Interestingly, it is Republicans’ fear of inflation that is flat to rising (admittedly from very low levels) while Democrats and Independents see inflation continuing to slow. (@neilsethinew)
About the AI risk
‘Big Short’ investor Steve Eisman sees an Achilles’ heel in the AI boom
Steve Eisman is warning that the artificial intelligence boom has become increasingly dependent on the fortunes of just two companies: OpenAI and Anthropic.
The investor, best known for his bet against the housing market ahead of the global financial crisis, said the two AI startups account for roughly 70% of AI-related revenue at Microsoft, Amazon, Alphabet’s Google and Oracle — and as much as 25% to 35% of their cloud revenue.
“The futures of these massive companies, in a sense, are a bet that OpenAI, Anthropic are going to succeed,” Eisman said late Tuesday on CNBC’s “Fast Money.”
“The Real Eisman Playbook” podcast host and former Neuberger Berman senior portfolio manager believes that the biggest revenue threat could come from China, as Chinese open-source AI models are significantly cheaper and appear to be gaining market share.
“The Achilles’ heel of this whole story … is if something bad happens to Anthropic and OpenAI … the Chinese open-end models, open-weight models are much cheaper. And if they start really taking a lot of market share and it sounds like, from what I’m hearing, that they’re starting to, you could have a big price war. And then we have a problem,” he said
Eisman’s warning adds another prominent voice to a growing debate over whether the extraordinary spending behind the AI boom can generate sufficient returns.
There are only 5 major AI frontier companies in the US and OpenAI and Anthropic are the 2 largest. No surprise then that they would represent a big chunk of hyperscalers’ revenues.
The risk is that, having no other sources of revenues, one or both no longer can get financing. Hence their rush to list.
Were that to happen, their data center capacity would be quickly gobbled up by others given the continuing demand/supply imbalance (see below).
Their creditors and shareholders are at risk but not the AI ecosystem.
They’re also too big, and too American, to fail…
David produced this great snapshot demonstrating that the AI boom has gone from Models to Infrastructure and now, importantly, to Products::
MODELS
- 4 weeks ago the frontier conversation was Anthropic-only; now Grok Bot ships and Alibaba’s Qwen3.8-Max (2.4T parameters) rivals Kimi K3 — the ecosystem is rich and growing everywhere.
- DeepSeek V4-Flash runs at $0.14/M input tokens — 100x cheaper than Claude Fable 5 — the cost curve is collapsing while capability keeps climbing.
️ INFRASTRUCTURE
- CoreWeave Q2: $2.575B revenue, +112% YoY, 59% adj. EBITDA margin.
- Nebius Q2: $575M revenue, +514% YoY; $27B Meta contract on top.
- Anthropic paying SpaceX $1.25B/month for compute AND nearing first quarterly operating profit.
- Google paying SpaceX $920M/month for 110K GPUs — demand still outrunning supply.
- AI infra spending: $89.7B in Q1 alone, +33% YoY; full-year forecast $497B.
PRODUCTS
- Cursor (SpaceX-owned): ~$4B ARR, up from $2B in Feb — fastest SaaS ramp ever ($100M → $4B in 18 months).
- Cognition (Devin): ~$1B ARR run-rate, up from $73M in Apr — fastest enterprise software ramp on record.
- Grok Bot (xAI + Cursor): launched Aug 11 — always-on agents with their own cloud computers that sign into your apps and finish tasks unsupervised.
- ChatGPT: 1B monthly active users (May) — fastest app to 1B ever.
- Gemini: 1B monthly active users (Aug) — Google’s fastest-growing product ever
Bottom line: revenue, users, and compute consumption are all compounding simultaneously across products, models, and infrastructure.
Goldman Sachs adds:
Our economists have noted that academic studies and company anecdotes show a 20-30% uplift in labor productivity in the limited areas where generative AI has been deployed, and they find that industries with higher AI adoption rates are showing a slight acceleration in productivity growth over the past year in official US data.
During the Q2 earnings season, 11% of S&P 500 companies quantified the impact of AI productivity on a specific use case, such as coding or customer support, and 2% quantified the impact of AI productivity on earnings. Both of these were similar to the shares in Q1 2026. Earnings results showed a modest and statistically insignificant difference in earnings growth between the companies quantifying AI productivity gains this quarter and other S&P 500 companies.
However, the recent acceleration in enterprise spending on AI suggests that the impact of corporate AI adoption should become increasingly clear in coming quarters.
The Ramp AI Index shows that the monthly AI spend per employee for the median company has increased from $5 at the start of the year to $12 in July. The distribution of corporate AI spend is wide, with the top decile company spending $650 per month per employee in July (vs. $240 at the start of the year).
This pattern mirrors the acceleration in the revenues of AI model providers this year. During the Q2 earnings season, roughly 7% of S&P 500 companies discussed the expenses associated with implementing AI. While there was some evidence that AI impacted corporate expenses in Q2, most companies also noted that these costs remain relatively small, that managements are taking a disciplined approach to AI spend, and/or that the benefits of AI use are outweighing the costs.
We estimate that AI inference expenses represent less than 0.5% of S&P 500 revenues, much smaller than other corporate expenses such as COGS, SG&A, and labor. To estimate AI inference expenses for companies, we combine the estimated revenues of leading AI model providers during H1 2026 with the estimated share of those revenues attributable to S&P 500 companies. The estimate excludes other costs potentially associated with implementing AI, such as spending on skilled labor and technology infrastructure.
Our equity analysts’ latest IT Spending Survey also suggests that AI inference costs remain modest, with 89% of respondents indicating that AI expenses represent 1-5% of total IT budgets.
The GS IT Spending Survey showed that roughly two thirds of companies fund AI inference costs by reallocating from other sources, including budgets for software (18%) and labor (11%). Reallocation from software or other existing technology budgets would drive a redistribution of earnings within the market but likely have a limited impact on aggregate earnings.
In contrast, many investors are concerned about the potential economic impact of a reallocation from labor budgets.
Supporting the recent rebound of many Software stocks, there is limited evidence that companies are shifting spending from external software providers to fund AI. The threat of disruption has weighed on the share prices of many Software stocks, and some recent announcements have validated these concerns.
For example, Starbucks is reportedly developing in-house tools via AI to replace some of its external software applications from companies such as Microsoft and IBM. However, the GS IT Spending Survey showed that just 17% of respondents expect to build more software relative to buying from packaged vendors. Reported Software fundamentals have also shown little sign of AI disruption, with annual recurring revenue growth for the median stock in GS analyst coverage accelerating slightly in recent quarters.
Our economists continue to see a visible but narrow impact of AI on the labor market. Recent labor market data show employment drags in specific industries where AI-use cases have been established such as marketing, graphic design, customer service, and some tech occupations.
However, this impact has been offset by construction job growth in data center-exposed categories. Our economists expect eventual AI-driven labor displacement but think the impact will be temporary and smaller than many investors fear.
The limited earnings impact of AI adoption so far helps explain why investors have continued to favor AI infrastructure stocks over potential AI productivity beneficiaries. Investors have rewarded companies involved in the AI infrastructure boom given the large and visible near-term earnings impact of that spending.
In contrast, both our client discussions and market performance indicate that investors want to avoid speculating about which companies will be most effective at implementing AI and where long-term profit gains will accrue. A basket of stocks that have discussed AI productivity initiatives has roughly matched the broader S&P 500 during the past few years with limited volatility.
Goldman does great research:
Global AI Investment Is Forecast to Exceed $1 Trillion in 2026
The most common estimate for investment in artificial intelligence (AI) is the projection for capital expenditures by US hyperscalers. These tech companies are forecast to spend about $800 billion this year, according to the consensus of analyst estimates.
But those estimates have several drawbacks, according to Goldman Sachs Research. They do not include investment in AI by private companies and by companies outside the US. Not all capex by hyperscalers is necessarily related to AI. And the major US tech companies operate globally, indicating some of their investment takes place outside the US.
Goldman Sachs Research adjusted the widely used measures of US hyperscaler capex to produce a more comprehensive estimate. That projection points to $1.019 trillion of AI-related investment around the globe in 2026, including $581 billion in the US, writes Joseph Briggs, who co-leads the Global Economics team, in a report.
These augmented estimates indicate that the commonly cited forecast for hyperscaler capex of $794 billion likely understates the total amount of global AI capex by around $200 billion. At the same time, the $794 billion figure likely overstates the amount of US investment in AI by $200 billion. (…)
Goldman Sachs Research extrapolated its preferred estimates for AI investment to forecast total AI investment as a share of US GDP through 2028. Those estimates imply that AI capex will rise from 1.8% of GDP in the US (with global AI investment totaling 0.9% of global GDP) in 2026 to 2.5% of GDP in the US (1.3% globally) in 2027, with a further increase to 2.8% (1.4% globally) in 2028.
“These levels are consistent with the 2%-5% of GDP peak investment impulses observed in prior general-purpose technology buildouts,” Briggs writes. “And while our US portfolio strategy team has flagged that consensus capex projections for 2027 are likely too conservative, even significant upward revisions would leave the level of AI investment as a share of GDP comfortably within the historical range observed in prior technology cycles.”
To estimate when the growth in AI-related capex will slow, Goldman Sachs Research says a “dashboard approach is most appropriate.” Our economists compiled a broad set of leading indicators—including semiconductor manufacturing equipment imports in Taiwan and South Korea, relevant Purchasing Managers’ Index (PMI) indicators and components, import prices, and memory purchase and Graphics Processing Unit (GPU) rental prices—to check whether a slowdown is imminent. Our economists find that their selected indicators provide leading information about US AI capex growth.
“The good news for the capex outlook is that all leading indicators rank near the top end of their range since 2022,” Briggs writes. “This pattern suggests a robust near-term growth outlook.”
- Open-Weight AI Won’t Crimp Demand for Picks and Shovels Some of the artificial intelligence boom’s biggest beneficiaries can cash in whether open models proliferate or not
A wave of cheap and powerful Chinese open-weight AI models is causing a scare among investors in the AI boom. For a decent chunk of the tech universe, however, there is less to worry about than meets the eye. (…)
If Chinese companies can do leading-edge AI better and more efficiently with open-weight models, the reasoning goes, plans to spend untold sums on data centers and chips might need reworking.
The opposite is just as likely to be true if open-weight models take off, though. Should AI become far cheaper to deploy, the so-called Jevons paradox, named after a 19th-century economist, would likely take hold. That would mean people respond to lower costs simply by using the technology more, eating up just as much if not more computing power.
That is one reason chip companies have largely welcomed open-weight models. Andrew Feldman, the chief executive of the Nasdaq-listed AI chip company Cerebras Systems said open-weight and closed-weight models pushing each other would benefit consumers, but wouldn’t hurt chip demand. “There is no reason for chip stocks to go down when open-source models come out,” he said.
Nvidia Chief Executive Jensen Huang has made himself an open-weight poster child in the past month, leading a consortium of companies championing the approach and drawing somewhat-dubious connections between open-weight models and the highly successful open-source software movement.
Beyond the chip industry, cloud-computing leaders like Amazon, Microsoft and even Google would also benefit handsomely if open-weight models expand demand for computing power. Open-weight models might be cheaper and more efficient, but while they might be free for anyone to pick up and use, they aren’t cost-free to run. That is a win for anyone in the computing-infrastructure game.
As Morningstar analyst Malik Khan said in a recent note, “if an enterprise were to consolidate its entire AI stack on open-weight models, it would still need cloud infrastructure to run those workloads, store data, manage security and access to resources, et cetera, all tailwinds to cloud infrastructure companies.”
A large number of corporations are already using open-weight models. They often tune them to excel at narrow tasks, like summarizing documents or answering customer questions on company-specific topics. (…)
Yet there are reasons to doubt advanced open-weight models—particularly those from China—will make major inroads in the AI race. While there is plenty of demand, there is no obvious profitable business model for them. Which is one reason open-weight model developers haven’t attracted a lot of venture-capital interest.
Open models can make sense for big tech companies for whom they are loss leaders, costing them money but drawing users to other profitable parts of their businesses. It is less clear whether independent open-model developers can make enough revenue to pay for the gargantuan cost of training a cutting-edge model.
Cheaper pricing for Chinese models was “a business-model choice, not necessarily a hardware-cost read-through,” BofA Securities analyst Vivek Arya said in a note recently. Despite its efficiency gains, Moonshot’s Kimi K3 model still needs around the same amount of high-cost memory as an advanced OpenAI open model, he said.
The economics might not matter as much in China, given that all of its leading AI developers have state support and AI is seen there as a strategic priority. But a large-scale migration to China’s open-weight models, at least among large Western companies, seems unlikely even if they do remain cheaper.
DeepSeek, the Chinese AI developer that shook markets last January after releasing its own advanced model, quickly faded as a threat to the AI boom. This time could easily be similar, with the possibility of U.S. restrictions on the use of Chinese open models serving as a deterrent for prospective customers—even if the curbs never come.
The disruptive possibility of open-weight models appears likely to linger, especially for OpenAI and Anthropic. But for others, their impact looks far less severe.
The big AI money will be in applications.
China’s Chip Industry Is Having a Breakout Moment Perseverance and government support gave rise to Huawei, CXMT and other competent chipmakers.
(…) in the past couple of years, China has surprised the skeptics. Huawei Technologies Co.’s artificial intelligence chips are now advanced enough to train sophisticated large language models, operate self-driving cars and direct the movements of humanoid robots.
Chinese companies are developing methods to build some chipmaking tools domestically and to goose the performance of older ones purchased abroad, spurred in no small part by US efforts to block sales of the newest foreign gear.
And in July memory-chip maker ChangXin Memory Technologies Inc., or CXMT, soared past hulking oil producers and state-run banks to grab the title of most valuable publicly listed company in China after its shares surged 466% following an initial public offering. It was the third-largest IPO in the world this year, behind those of SpaceX and SK Hynix Inc.
These advances reflect how the necessary ingredients are coming together to deliver a viable homegrown semiconductor industry. Historically, chipmakers that have attained breakout success benefited from the confluence of two factors: an effective national industrial policy campaign and at least one powerful, committed customer that helped push them to achieve innovative breakthroughs.
Take Texas Instruments Inc., the industry’s first dominant chipmaker, which enjoyed a golden era through the 1960s and ’70s. Its rise coincided with the US-Soviet space race, with hefty federal contracts and grants securing its finances and funding its ambitious research and development program. Also crucial for TI was its role as a key supplier to International Business Machines Corp.’s early mainframe computers. IBM committed to massive orders before it was even clear if TI could actually deliver, and the two companies’ engineers worked together for months to overcome the technical obstacles of fabricating the transistors.
Next was Intel Corp. When IBM began a stealth project to build its first personal computer in 1980, it chose the Californian company over TI as its supplier. This head start into the PC era helped secure Intel’s status as the world’s leading chipmaker for the next several decades. In the second half of the ’80s, Intel also became one of the major beneficiaries of a federal subsidy program to help American chipmakers survive the brutal competition against new, low-cost Japanese rivals.
More recently, Intel has seen its star eclipsed by Taiwan Semiconductor Manufacturing Co., which was built with the help of Taiwan’s government and its decades of focused and determined industrial policy. After forging close symbiotic relationships with Apple Inc., Nvidia Corp. and other companies, and helping them develop generations of new products, TSMC has gained a near-monopoly on global advanced-chip manufacturing.
Both of these factors—focused governmental support and collaborations with innovative customers—are now buoying China’s chipmakers. In response to US bans on certain high-tech exports, Beijing has been refining its goals for semiconductor development, with the priority today to surmount several specific technological bottlenecks that could hinder the nation’s AI aspirations.
At the same time, Chinese tech giants Huawei and Tencent Holdings Ltd. have reached a level of scale and technological sophistication at which they can provide meaningful support as anchor customers and R&D partners to help local chipmakers overcome technical obstacles. These Chinese companies had previously preferred to work with leading international chipmakers to develop their most advanced products, but in the new era of US-enforced isolation, they no longer have that option. (…)
CXMT is now racing to develop “ high-bandwidth memory,” a type of memory chip that’s a crucial part of Nvidia’s AI systems but that China isn’t yet able to produce commercially. The company’s customers—which include Alibaba, ByteDance, Lenovo, Tencent and Xiaomi, according to its prospectus—have a vested interest in helping domestic suppliers achieve this capability.
Neil Shah, co-founder of tech research firm Counterpoint Research in Hong Kong, notes that though there’s still a “massive technical generation gap” between CXMT and industry leaders, the company is aggressively narrowing the distance with the help of the domestic ecosystem. “While sanctions initially threatened to stifle growth, they ultimately pushed the company to drive micro-innovations for self-sufficiency,” he says. (…)
Meanwhile the US government is continuing its efforts to thwart China’s advancement in the field, with American officials warning that these technologies will be used not only for civilian purposes but also to upgrade China’s military. In 2018 the prospects of CXMT rival Fujian Jinhua Integrated Circuit Co.—which started out looking more promising, with more startup capital and bigger construction plans—were derailed when the US indicted it on allegations of conspiring to steal trade secrets from Micron Technology Inc.
The Chinese chipmaker was acquitted of the charges in 2024 but has yet to regain its footing. (…)
In some ways, China’s semiconductor industry is becoming more cloistered after decades of greater international partnership. (…) But the country is far more capable of advancing technologically on its own than it’s ever been. After many years of honing its industrial policy, engaging in international exchanges and bolstering its technical competencies up and down the supply chain, China has a complete enough ecosystem that it may be able to continue innovating, even in isolation.
China’s Economic Woes Mount With Disappointing Start to Half
Industrial production expanded 4.5% in July from a year earlier, slowing for the first time in three months and missing estimates, according to data released by the National Bureau of Statistics on Monday. Retail sales growth slowed to 0.6%, also underperforming expectations.
Fixed-asset investment fell more than forecast at a pace of 6.7% year-on-year in the first seven months, after shrinking 5.7% in the first half. The surveyed urban jobless rate climbed to 5.2% from 5% in June.
The July figures suggest growth in gross domestic product likely decelerated to around 4.1%, below the 4.3% level Beijing needs in the second half to reach its annual growth target, according to Jacqueline Rong, chief China economist at BNP Paribas SA. Declines in overall investment and capital spending on infrastructure accelerated from June by slumping at double-digit rates last month, she said. (…)
Heavy rainfall and strong winds swept through large swathes of China last month, temporarily shutting down factories and ports, leading to power outages and forcing tens of thousands of people to evacuate. While the disruption may have only a temporary impact, policymakers are likely monitoring the data closely as they assess whether more aid is needed for the economy to ensure their growth goal is within reach. (…)
The NBS said that while the economy has remained “stable” so far this year, the external environment is “complicated and volatile” and domestic demand has stayed weak. “Some companies are facing operational difficulties, and the foundation for the economy to stabilize and improve still needs to be consolidated,” it said in a statement accompanying the data release. (…)
New-home prices slumped at a faster clip in July while real estate investment plunged 19.2% on year in the first seven months, a fresh record low. (…)
Passenger vehicle purchases fell 21% in July, a worrying sign for the broader auto sector that represents the single biggest goods component in total retail sales with a share of about 8%. Meanwhile, Chinese carmakers have continued to see margins being squeezed by high raw material costs and persistent discounting amid fierce price competition with rivals.
And in a sign of further deterioration for sales at home, a gauge of new orders in July’s official manufacturing purchasing managers’ index swung back into contraction by dropping to the lowest in more than three years, boding ill for industrial production. (…)
(…) China’s property downturn has weighed on the economy for five years, hampering efforts by officials to spur consumer spending and reduce reliance on exports. (…)
Signs of a recovery in the real estate market have largely been confined to big cities and pockets of the market such as old apartments in good locations.
While analysts at firms including Citigroup Inc. and Bank of America Corp. have begun to assert that the sector is finally stabilizing, others are doubtful. Fitch Ratings in late June revised down its forecast for new home sales, saying persistent weakness in lower-tier cities continues to outweigh a recovery in a small number of stronger markets. (…)
Still, local governments have rolled out marginal steps to shore up sentiment. Earlier this month, Beijing further eased homebuying rules for non-residents, as the capital city joined Shanghai in lifting decade-long restrictions. It followed another incremental step from about eight months ago, when the city relaxed rules for non-resident homebuyers.
Beijing is easing homebuying rules for non-residents, as the Chinese city joins Shanghai in lifting decade-long restrictions to help revive the nation’s battered property sector.
The capital will allow non-resident homebuyers who have paid social security or individual tax for a year to buy homes in core residential areas within the Fifth Ring Road, according to a municipal government statement on Friday. Previously, it required those without a local household registration to pay social security or individual tax for two years before becoming eligible.
Children receiving homes as gifts from their parents will be exempt from homebuyer eligibility checks, according to the statement. Beijing will also increase the amount of mortgage loans buyers can borrow based on their housing provident funds. All the measures will take effect Aug. 8. (…)
These are significant steps to increase demand in Beijing and Shanghai.
Through the fog of the trade war, Canadian business investment is stirring Trump wanted Canada in a weakened state to wring as much from a vulnerable trading partner as possible. It hasn’t gone entirely according to plan
(…) Investment intentions by domestic businesses have risen to their highest level since Mr. Trump returned to office. Meanwhile, import volumes of machinery and equipment recently hit a decade high. Same goes for imports of computer equipment, which jumped by nearly 90 per cent in the month of June alone – possibly a sign that investment in artificial intelligence is starting to gain traction.
Just maybe this is the beginning of the end of Canada’s investment drought.
It is “possible that businesses are beginning to feel the effects of the federal government’s investment-stimulating efforts,” said Jocelyn Paquet, a senior economist at National Bank of Canada, in a recent note.
At the heart of those efforts are the infrastructure and defence projects that aim to heavily favour local manufacturing by awarding contracts to domestic companies. (…)
According to the Bank of Canada’s latest business outlook survey, a net of 30 per cent of companies say they will increase spending on machinery and equipment (M&E) over the next year. The long-term average is around 16 per cent. (…)
“Although the Canada-U.S.-Mexico Agreement is now subject to annual reviews, more businesses report they are finding ways to navigate through the uncertainty.”
Much of the recent uptick in investment, as Mr. Macklem noted, can be attributed to the energy sector. The endless drama over the Strait of Hormuz has elevated oil prices and restricted global supply, thus triggering increased production in Canada. This could easily prove to be a fleeting boost for Canadian oil and gas.
Still, it seems like there is more to the story than a temporary disturbance in the oil market. The surge in imports of computer equipment used in data-centre construction, for example, is also encouraging. “After a slow start, AI investment finally seems to be taking off in Canada,” Ms. Paquet said. (…)
GDP growth bounced back from negative territory to an estimated 3.4 per cent annualized in the second quarter. It also helps that the Canadian stock market has held its own this year, with a solid 16-per-cent gain in the S&P/TSX Composite Index year to date.
All of which strengthens Canada’s hand in trade negotiations.
FYI: Over the latest 3 months, Canada created 181,100 jobs, while the United States created 163,000 nonfarm payroll jobs. Population-adjusted, Canada’s labor market has been much stronger since April.
It seems that various Trump policies are now backfiring…
Markets Are Serene in Their Summer Sargasso Sea How becalmed are they? Among other things, the VIX is at its lowest this year.
(…) Exhibit A) is Bloomberg’s financial conditions index, which gauges whether it is relatively easy or hard to raise finance from a range of market metrics. Initiated in 1990, it has never shown conditions easier than they were at the close on Friday:
(…) The VIX index, gauging volatility by investors’ positions in the options market to guard against turbulence in the S&P 500, is at its most relaxed for 2026. It’s only closed lower than this on a handful of occasions since Donald Trump was reelected:
So it’s evident that something beyond the direct impact of the AI investments is buoying equities. That would be corporate fundamentals in the form of earnings per share and revenues, which enjoyed a phenomenal quarter and seem set fair for the rest of the year. Semiconductors and financials have raised earnings far faster than revenues as they expand margins, which may yet create its own problems, but strong earnings growth on the back of solid sales repeats across many sectors:
Earnings like this go a long way to calm nerves. Beyond that, can we attribute the calm to the summer doldrums? With earnings season winding down, and most of the key economic data for the month already published, the diary for this week has no potential market-moving events that we can see coming. The closest approach could be the publication of the minutes to the last Federal Open Market Committee meeting, due Wednesday. Nvidia’s results and the Jackson Hole central banking conference, both potential market movers, aren’t until next week. People are on vacation. It’s hot. No wonder nothing much happens in the dog days of August. (…)
This year, the spasm of nerves around the AI trade briefly had the potential to turn into such an event but has desisted. Japanese currency intervention hasn’t sparked another major carry trade unwind. And by being rather disappointing, data have calmed the notion that the Federal Reserve will be tightening straight away. The economy is strong enough for companies to make money; and the negative surprises suggest it’s not so strong that the Fed will need to hike rates, which counts as good news:
(…) Whether markets are right to be this confident is another question. Meanwhile, the greatest risk, also diminished slightly by the slowdown in the recent data, comes from bonds. The notion that Treasury investors might finally revolt against ever higher fiscal deficits and force yields far higher has been around for years. Real 10-year yields have threatened to break out but remain just below 2.5%. If they rise to take out the post-crisis high of 2.516%, set three years ago, that could shatter the calm:
But as it stands, Great Earnings + Goldilocks Economic Data + Absence of News = Continued Calm. Just like eels must leave the Sargasso, this cannot go on indefinitely, but with any luck these vintage doldrums have another couple of weeks left in them.
Private credit under strain as troubled loans swell FT analysis shows signals of stress in the market are back to levels last seen in 2017
Strain is spreading across private credit portfolios, with some of the largest funds taking writedowns and warning about problem loans as the industry faces its biggest challenge in almost a decade.
The value of troubled loans held by some of the biggest private debt investors has reached levels last seen in 2017, when the industry was dealing with a hangover from an oil price crash, an FT analysis of figures from fixed-income data provider Solve has found. Loans placed on non-accrual status by the 20 largest publicly traded business development companies (BDCs) — listed funds that invest in private credit loans — climbed to a median 2.8 per cent of their cost in the second quarter, up from 2 per cent at the end of March.
The non-accrual demarcation is one signal of stress in the private credit industry, indicating borrowers have either stopped making payments on their loans or that a fund believes a borrower may soon default on its obligations. (…)
Analysts at Fitch Ratings last week warned that private credit defaults had hit a new record in July. Separate data from PitchBook LCD showed the biggest publicly listed BDCs shrank again in the second quarter as funds were hit with impairments and as sales and repayments of loans outpaced commitments on new deals.
Listed vehicles managed by KKR and Blue Owl, as well as one run by Apollo Global known as MidCap Financial, were among the funds in which repayments outstripped new lending in the quarter, with executives at KKR pointing to limited dealmaking and its push to exit certain loans. The firm’s listed fund, FS KKR Capital Group, reported that 7.1 per cent of its loan book was troubled in the second quarter, a slight improvement from the prior quarter but still far above the industry average.
The figures underscore the challenge facing the private investment industry, which wagered heavily on private credit as a major source of growth as it looked to invest money for insurers, retirees and wealthy individuals. (…)
Industry titans have acknowledged that after a long period of relatively muted defaults, bankruptcies and restructurings were beginning to move back towards their long-term average. “We are . . . conserving our capital, maintaining ourselves in a more defensive and risk-averse posture,” said Armen Panossian, the co-chief executive of Oaktree’s credit arm. “We really want to be able to lean into the market on the back of what we think will be more volatility . . .
Beneath the surface, there’s cause for concern.” But many executives across the $2tn asset class believe that the alarmism surrounding private credit’s troubles is overblown, with several blaming the media — including the FT — for the outflows weighing on the asset class. (…)
Much of the pain already seen has been centred on investments the funds helped finance between 2020 and 2021, when interest rates were near zero and private equity groups went on a buying binge while valuations were elevated. Many of those companies are now struggling to service their debt as interest rates have climbed, with executives on earnings calls repeatedly pointing to that cohort as the source of trouble.
Higher borrowing costs have “starved some businesses from investing”, said Bryan High, head of Barings’ global private finance team. “They are using all the cash they are generating to pay interest to lenders and so growth for some businesses wasn’t as strong as it could be.” (…)
This other growing problem:
BofA’s Hartnett:
- US national debt set to surpass $40T in coming days, on course for $50T by ’29;
- cost of servicing debt $1.4T in past 12 months, will keep rising until 5-year UST yields drop below 3¼%, reinforcing Anything But Bonds asset allocation;
- US stocks storming to new highs same day government selling 30-year USTs at highest yield (5.126%) in 25 years “tracks” as the kids say. (@neilsethinew)
Nobody cares:
Equity Allocations: thanks to a combination of rising stock prices and rising confidence (because asset allocators always have the option to rebalance, if market movements drift them higher that is effectively an active decision to maintain higher equity allocations) — equity allocations are up to pre-financial crisis levels.
Source: State Street Markets via Daily Chartbook
Frustration with Trump grows among U.S.’s Persian Gulf allies, officials say
Frustration with Washington is growing in the Persian Gulf region, where U.S. allies are increasingly concerned that President Donald Trump is unable to manage the diplomacy necessary to secure peace with Iran, Arab and Western officials say.
(…) three officials and a former U.S. diplomat told The Washington Post, the governments of Saudi Arabia, the United Arab Emirates, Qatar, Kuwait and Bahrain are now united in their aggravation with the administration. Some have begun to debate the utility of continuing to host large U.S. military installations on their territory.
“Trump started this war,” one Gulf official said, “and we are paying the price.” (…) Anger with the United States, the official said, was at its highest point since the U.S. and Israel attacked Iran in February. (…)
A mutual defense agreement signed by Saudi Arabia, Turkey and Pakistan last week was “a signal to the U.S.,” a senior European official said.
The pact shows the three powerful Sunni Muslim states “are trying to diversify their security and defense partnerships,” said the official, who is in regular contact with Gulf leaders. “In their mind, the U.S. is not enough.” (…)
Silliman, now the president of the Arab Gulf States Institute, described the feeling in the region as “kind of this odd contradiction of being mad at the United States for starting the war and being dependent upon the United States” for equipment, munitions and intelligence.
If the conflict continues, the economic pressure on the Gulf will grow. Even during peacetime, the region quiets over the summer, tourism slows and residents hole up indoors to escape 100-degree days.
But in autumn, temperatures fall and activity resumes. This year, the conferences, summits, festivals and Formula One races that pack the calendar from October through December are all in limbo.
Saudi Arabia’s 10th annual Future Investment Initiative Institute in late October, the “Davos in the Desert,” could be an early test of whether senior officials and top CEOs are willing to visit the region, the Western diplomat said. (…)
“A quick end is obviously something we want,” he said. “The Gulf War was much more straightforward than today.”
Other frustrations:
FYI:
