Trump Vows Tariffs ‘Much Bigger’ Than 2.5% and on Key Areas Trump says he wants universal levy to “protect our country”

“I have it in my mind what it’s going to be but I won’t be setting it yet, but it’ll be enough to protect our country,” Trump told reporters Monday night.

Asked about a report that incoming Treasury Secretary Scott Bessent favored starting with a global rate of 2.5%, Trump said he didn’t think Bessent supported that and wouldn’t favor it himself. He said he wanted a rate “much bigger” than 2.5%.

Trump spoke aboard Air Force One while he flew back to Washington, DC, from a Florida speech where he also pledged tariffs on specific sectors, including semiconductors, pharmaceuticals, steel, copper and aluminum. He also strongly suggested he could also impose them on automobiles from Canada and Mexico, countries he’s already threatened with 25% across-the board tariffs as soon as Feb. 1. (…)

“Remember, again, the word ‘tariff.’ We’re going to protect our people and our businesses, and we’re going to protect our country, with tariffs,” Trump added. He’s said to have earlier mused about tariffs of up to 20%. (…)

He repeated a call for Republicans to cut the corporate tax rate to 15%, from the current 21%, for companies that make their goods in America. (…)

Trump singled out several sectors in his speech to lawmakers. He complained at length about auto imports both from Canada and Mexico.

“They send us millions of cars; we don’t need them for that,” he said of Canada, America’s top export market. “We want to have the cars made in Detroit or South Carolina or many other locations.” Trump then added: “The auto workers voted for me and I have an obligation to do what’s right, and I’m going to do that.”

He sang the praises of steel tariffs that he implemented in his first term and said he’d be “placing tariffs on steel, aluminum and copper and things that we need for our military,” without elaborating. “We have to bring production back to our country.” (…)

BTW:

The heavy-handed tariff and financial sanctions threats, which would have been devastating for Colombia if imposed, also run a strategic risk: spurring other countries to look at the US threat of economic punishment and deciding to diversify toward non-US trade partners and payment systems, said Josh Lipsky, senior director of the GeoEconomics Center at the Atlantic Council.

While the US is historically Colombia’s top trade partner, Petro has already been strengthening ties with China. During the period on Sunday when US tariffs looked imminent, Petro called for deepening his nation’s connections with other markets. Having seen Colombia’s experience, other countries may follow suit.

“You have to wonder if there’s a Pyrrhic victory here, because the more countries see this threat, the more they start turning and looking at ways that they can diversify their own trading relationships,” Lipsky said. “There is a cost long term to the overuse of the threat. I think they broadly got the result they wanted here, but I do think there’ll be more cost throughout the region in the months to come.” (Bloomberg)

Already happening in Europe, Mexico and Canada. Every country now needs to examine how to reduce dependance on the USA.

The FT reminds us that “China has hugely expanded its trade and investment in Latin America this century, and Beijing is likely to view Trump’s unpredictable moves as an ideal opportunity to present itself as a more reliable partner, diplomats and analysts said. Shifter, of Inter-American Dialogue, said: “Celac is the platform for China in Latin America, so Thursday’s summit is a kind of proxy for showing [Washington] that if [it is] really going to punish us, then China’s willing to fill the gap and come in even more than it has already.”

AI CORNER

The DeepSeek AI Freakout The Chinese startup’s model stuns Big Tech—and Wall Street—with its capability and cost.

The WSJ Editorial Board:

(…) DeepSeek required far fewer chips to train than other advanced AI models and thus cost only an estimated $5.6 million to develop. Other advanced models cost in the neighborhood of $1 billion. Venture capitalist Marc Andreessen called it “AI’s Sputnik moment,” and he may be right.

DeepSeek is challenging assumptions about the computing power and spending needed for AI advances. (…)

CEO Mark Zuckerberg on Friday said Meta would spend about $65 billion on AI projects this year and build a data center “so large that it would cover a significant part of Manhattan.” Meta expects to have 1.3 million advanced chips by the end of this year. DeepSeek’s model reportedly required as few as 10,000 to develop.

DeepSeek’s breakthrough means these tech giants may not have to spend as much to train their AI models. But it also means these firms, notably Google’s DeepMind, might lose their first-mover, technological edge. Google shares fell 4% on Monday. DeepSeek’s model is open-source, meaning that other developers can inspect and fiddle with its code and build their own applications with it.

This could help give more small businesses access to AI tools at a fraction of the cost of closed-source models like OpenAI and Anthropic, which Amazon has backed. There are advantages to such closed-source systems, especially for privacy and national security. But open-source can foster more collaboration and experimentation.

It’s notable that DeepSeek is a startup founded by Liang Wenfeng, a Chinese hedge fund trader. Americans think of China’s economy as run top-down, and much of it is. But its growth over the last few decades, especially in tech, has been spurred by entrepreneurs. Alibaba, Tencent and ByteDance were all once startups that now rival U.S. tech giants. (…)

DeepSeek should also cause Republicans in Washington to rethink their antitrust obsessions with big tech. Bureaucrats aren’t capable of overseeing thousands of AI models, and more regulation would slow innovation and make it harder for U.S. companies to compete with China. As DeepSeek shows, it’s possible for a David to compete with the Goliaths. Let a thousand American AI flowers bloom.

• DeepSeek Undercuts Belief That Chip-Hungry U.S. Players Will Win AI Race More AI competition will make it hard for Big Tech to generate the oligopoly-like profit margins that investors hope for

(…) Monday’s AI crash was led by the first part, the “chips and shovels” that supported the development of AI, not the developers of these fancy programs themselves. Nvidia stock was down 17%, losing more than half a trillion dollars of value, nuclear-power stocks Constellation Energy, Vistra, Oklo and NuScale Power were down 21%-28%, and data-center supplier Vertiv Holdings was off 30%. (…)

The moves in prices appear to show investors focused on fundamental issues of how DeepSeek’s approach will lead to lower power use and less demand for chips and data centers. (…)

A lot of what’s been going on is similar to when investors discovered the internet. They have grasped that AI is A Big Deal, but can’t yet see exactly how or when it will make money. (…)

What are the prospects for Microsoft-backed OpenAI, Alphabet, Amazon-backed Anthropic, Elon Musk’s xAI and all the others to make money when faced with a low-cost competitor? DeepSeek is, after all, available, like Meta’s Llama, to download and tweak free.

On Monday this wasn’t the main concern, with Microsoft off 2.1%, Alphabet 4.2% and Amazon slightly up. They have big, profitable businesses they are using to finance AI development, and will also be able to use the techniques DeepSeek shared to lower their own costs. But they just lost one of the biggest barriers to entry. If a new AI model can be produced for just a few million dollars by the tech arm of a Chinese hedge fund, maybe others can do the same. (…)

Investors have been encouraging companies to pour cash into building new data centers, power stations and anything related to the power grid for the past year. Monday’s price drops suggest less appetite for real-world investments and could persuade companies to invest less into such projects. (…)

• DeepSeek Won’t Sink U.S. AI Titans Panic fueling the selloff of Nvidia, Broadcom and other tech giants is overblown

(…) The high entry price of AI—and sanctions from the U.S. government limiting the sale of advanced AI chips to Chinese companies—also serve as a competitive moat for tech titans such as Microsoft , Amazon, Google and Meta Platforms. They are among the few companies with enough capital to build out expensive AI networks on a large scale. (…)

The selloff seems excessive. Much remains unknown about DeepSeek’s claims, including what sorts of chips the company had access to despite the effect of sanctions. Several chip analysts on Monday disputed the notion that DeepSeek built something on par with advanced U.S.-based AI models at such a low cost.

“DeepSeek DID NOT ‘build OpenAI for $5 million,’ ” wrote Stacy Rasgon of Bernstein. “The ‘DeepSeek’ moment is driving investors to shoot first and ask questions later,” wrote Joshua Buchalter of TD Cowen. “While DeepSeek’s achievement could be groundbreaking, we question the notion that its feats were done without the use of advanced GPUs to fine tune it,” wrote Atif Malik of Citigroup.

More important, such a technical breakthrough is unlikely to cool the AI race or even cut down the funds being poured into it. Addressing the comparison of DeepSeek to Sputnik, Edward Yang of Oppenheimer said the Space Race didn’t result in less money going out the door. “Increased competition rarely reduces aggregate spending,” he wrote in a note to clients. Pierre Ferragu of New Street Research noted that more advanced “frontier models” will still need to push the technical edge and use the most advanced computing resources, while smaller “lagging edge” models will push to develop more cost-efficient AI features. 

“DeepSeek is not a game changer, and on the contrary fits very well with the way we have seen the industry evolving in the last three years,” Ferragu wrote. (…)

Microsoft, which reports quarterly results on Wednesday, will be the first big tech company with the opportunity to signal whether DeepSeek’s breakthrough will deep-six its investment plans. The company is expected to drop about $84 billion in capital spending for the fiscal year ending in June and $94 billion in the next year, according to consensus estimates from Visible Alpha. (…)

“As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of,” wrote Microsoft CEO Satya Nadella in a post on X on Monday morning.

The AI spending war might just be entering a new phase.

Not a game changer?

Almost overnight, DeepSeek has upended many of the assumptions inside Silicon Valley about the economics of building AI, as well as the best technical methods for developing the technology and the extent of the US lead over competitors in China. For much of the past two-plus years since ChatGPT kicked off the global AI frenzy, the industry has bet that the path to better AI depends largely on spending heavily on more advanced chips from companies like Nvidia Corp. and increasingly massive data centers to house them. (…)

“It’s a paradigm shift,” said Ali Ghodsi, CEO of Databricks Inc. “These models that can reason are so much cheaper to produce that you will see it be democratized. You’ll see innovations from unexpected corners of the world.” (…)

Like some of the latest models from OpenAI, Google and Anthropic, R1 is intended to parrot the ways humans sometimes ruminate over problems by spending time computing an answer before responding to user queries. DeepSeek’s version differs, however, in its efficiency. The team behind it came up with some simple but key innovations, such as finding ways to get more use from the computer chips they did have access to. Another breakthrough: leaning heavily on a technique known as reinforcement learning that rewards a system for correct answers and punishes it for those that are incorrect. (…)

Mehdi Osman, CEO of software company OpenReplay, said his company traditionally used services from OpenAI, Anthropic and Mistral, and that DeepSeek’s reasoning skills seemed on par with OpenAI’s. “If OpenAI doesn’t reduce their prices, I think many developers will jump to DeepSeek in the coming months,” Osman said. (…)

Much of the spending by the largest cloud-computing companies is going towards Nvidia graphics processing units. Amazon, Google and Microsoft are also building custom chips designed for AI, work that could be less useful in the long term if developers are able to build and run models on less-specialized hardware, Stefan Slowinski, an analyst with BNP Paribas Exane, wrote in a research note on Monday.

DeepSeek’s app proved popular with US users, thanks in part to an affable, somewhat awkward-sounding chatbot that shows in great detail how it plans to respond to a person’s question before diving into the results. The approach includes far more detail than, say, OpenAI’s latest reasoning models. And unlike OpenAI, which charges as much as $200 a month for unlimited access to its most advanced reasoning models, among other features, DeepSeek is currently offering its service for free. But DeepSeek also censors topics that would be sensitive in China. Asking about the Chinese Cultural Revolution, for instance, may provoke the response: “Sorry, that’s beyond my current scope. Let’s talk about something else.” (…) (Bloomberg)

But censoring only happens at data centers. The DeepSeek model open version is not censored when run locally, which most Chinese companies do.

Via John Authers:

As Keith Lerner, chief market strategist at Truist argues, if DeepSeek’s claims are true — that it offers a robust AI tool utilizing low-level Nvidia chips, open-source code, and can be deployed at a fraction of the cost — then it could inspire hundreds of copycats, especially in the US. That’s where the damage will be felt (…).

Also, as experts unravel DeepSeek’s capabilities, they will also get to examine the lofty tech valuations and concentration risks that have grown since ChatGPT’s introduction. An efficient and cost-effective open-source artificial intelligence model should be good news for the economy, but not necessarily for the stock market. (…)

Data centers that support the supercomputers currently thought necessary to run complex AI models also stand to lose.

(…) if DeepSeek’s claims are proven, an aggregate reduction in demand for the chips would damage high expectations for both unit sales and prices (…).

No doubt that AI-related costs, prices and margins will be coming down across the eco-system. Will AI uses (compute) explode as a result (the Jevons Paradox)? How much locally vs via data centers?

• Pat Gelsinger (former Intel CEO) wrote on Linkedin: “The market reaction is wrong: lowering the cost of AI will expand the market. DeepSeek is an incredible piece of engineering that will usher in greater adoption of AI.”
• “DeepSeek is an excellent AI advancement and a perfect example of Test Time Scaling,” Nvidia said, referring to AI systems that consume more computing resources after a user poses a question or sets a task by “reasoning” or taking multiple linked steps to respond. “Inference requires significant numbers of Nvidia GPUs and high-performance networking.” (FT)

That said:

The sky hasn’t fallen just yet. And parallels with the dot-com bubble aren’t quite right. As John Canavan, lead analyst at Oxford Economics, points out, the dot-coms were built on froth. The question was whether they could ever make a profit, and most didn’t. This time, the leading companies are making huge profits, and the question is whether they can be sustained. If they can’t, that will hurt shares in the tech sector, but not necessarily the broader financial markets, or the economy.

Even China’s Property Stalwart Isn’t Immune From the Crisis Troubles at one of China’s largest developers raise questions about the continued spread of the real-estate crisis

China Vanke warned on Monday of a loss of 45 billion yuan, equivalent to around $6.2 billion, for 2024, and said Chairman Yu Liang and Chief Executive Officer Zhu Jiusheng will resign. (…)

Analysts and investors have long seen Vanke as a barometer of how much pain the Chinese government can tolerate amid the country’s epic real-estate bust. The company had been regarded as among the more conservative real-estate companies, eschewing the sort of risky investments that laid low the likes of Evergrande.

Investors will watch to see whether and how the government helps Vanke, as it would indicate the willingness of Chinese authorities to help other struggling state-backed developers to draw a line under the national property crisis.

While many of Vanke’s privately owned peers have suffered liquidity crises and defaulted on their debts in recent years, Vanke has so far managed to stay afloat by making last-minute bond payments and securing fresh loans from banks. By September last year, Vanke’s total liabilities amounted to $136 billion, according to its most recent quarterly report.

In late 2023, when Vanke’s liquidity crunch first emerged, Shenzhen city officials and representatives of Shenzhen Metro voiced their “substantive support” for the company, easing investors’ concern that the company was on the verge of default.

Last April, Vanke said the government of Shenzhen, one of China’s wealthiest cities, was coordinating with state-owned companies to support the developer, including collaborating with it on projects and helping Vanke dispose of properties and equity investments. (…)

Vanke’s sales plummeted 35% in 2024 compared with 2023, to around $51.3 billion, the company said in January. The decline is steeper than that of China’s overall housing sector, where home sales tumbled 18% in 2024, after a 6% decline in 2023. Fitch and S&P Global Ratings downgraded Vanke last week by two notches to B-, citing persistently weak sales and looming debt maturities.

In a separate Hong Kong stock exchange filing, Vanke said the record losses were due to the declining profit margin of real-estate development projects, disappointing sales in 2024 and impairment provision. (…)

Despite efforts by the Chinese government to stimulate the property market, Vanke’s sales in the second half of the year remained sluggish. Its net loss widened from $1.4 billion for the first six months of the year to $6.2 billion for the full year.

AI CORNER

Tech stocks tumble as China’s DeepSeek sows doubts about AI spending Start-up’s model raises questions about need for huge western hardware investment

Thanks to David’s research, this blog has been documenting the stealth advances of Chinese AI models. And it’s not only DeepSeek.

David two weeks ago:

With the advent of chatgpt a few years ago, the research community was locked out of making progress because only hyperscalers could provide the compute necessary and the funds.

Chinese are demonstrating how it is possible to train models that rival the reasoning capabilities of the o1 series by using very small models at the very low cost of 450$ to train. Sky T1 was trained with synthetic data generated by Alibaba’s QwQ reasoning model which was then used to fine tune another Alibaba model: Qwen 2.5

Moreover, it will now be possible to do so on a 3000$ machine called “DIGITS” which was just announced by Nvidia (No cloud required).

He prompted Perplexity.ai on the significance of Chinese models:

Perplexity Pro costs $20/month. By comparison, ChatGPT Pro costs $200/month and Sam Altman claims they are losing money at that price. Understandable when your capital cost is in billions:

Perplexity allows users to chose among a number of reasoning models. Last week, David asked ChatGPT o1 model if Perplexity should replace it with DeepSeek r1. As David said, “gotta appreciate the honesty”:

All this to realize that the costs of AI models and its future applications is dropping fast. David says that “Alibaba is building opensource tools to develop these new agents at 50x less inference cost than western providers.”

• Alibaba Cloud’s Price Cuts: A Game Changer for AI Adoption

Alibaba Cloud, the cloud computing arm of Alibaba Group Holding Ltd., has announced significant price cuts on its large-language models, a move that is set to revolutionize the AI landscape. The company has reduced the price of its Qwen-Long model by up to 97%, making it the most affordable large-language model in the market. Additionally, the Tongyi Qianwen series visual understanding models have seen an overall decrease of over 80%, with the Qwen-VL-Plus model’s price reduced by 81% and the Qwen-VL-Max model’s price slashed by 85%. These price cuts are expected to have a profound impact on user adoption, market share, and revenue growth for Alibaba Cloud’s AI services. (…)

The price cuts are also expected to stimulate market growth by encouraging more businesses to adopt AI technologies. This increased adoption can, in turn, drive innovation and the development of new AI applications, ultimately benefiting the entire market. The aggressive pricing strategy by Alibaba Cloud may prompt other cloud service providers to lower their prices as well, leading to a more competitive market landscape. This increased competition can result in better services and more affordable pricing for customers, fostering a healthier market environment.

Game changers!

Now it’s all mainstream:

Silicon Valley Is Raving About a Made-in-China AI Model DeepSeek is called ‘amazing and impressive’ despite working with less-advanced chips

(…) “Deepseek R1 is one of the most amazing and impressive breakthroughs I’ve ever seen,” said Marc Andreessen, the Silicon Valley venture capitalist who has been advising President Trump, in an X post on Friday. (…)

DeepSeek’s development was led by a Chinese hedge-fund manager, Liang Wenfeng, who has become the face of the country’s AI push. On Jan. 20, Liang met China’s premier and discussed how homegrown companies could narrow the gap with the U.S.

Specialists said DeepSeek’s technology still trails that of OpenAI and Google. But it is a close rival despite using fewer and less-advanced chips, and in some cases skipping steps that U.S. developers considered essential.

DeepSeek said training one of its latest models cost $5.6 million, compared with the $100 million to $1 billion range cited last year by Dario Amodei, chief executive of the AI developer Anthropic, as the cost of building a model.

Barrett Woodside, co-founder of the San Francisco AI hardware company Positron, said he and his colleagues have been abuzz about DeepSeek. “It’s very cool,” said Woodside, pointing to DeepSeek’s open-source models in which the software code behind the AI model is made available free. (…)

“The only strike against it is some half-baked PRC censorship,” said Woodside, referring to the People’s Republic of China, but he said this could be removed because other developers can freely modify the code. 

DeepSeek said R1 and V3 both performed better than or close to leading Western models. As of Saturday, the two models were ranked in the top 10 on Chatbot Arena, a platform hosted by University of California, Berkeley, researchers that rates chatbot performance. A Google Gemini model was in the top spot, while DeepSeek bested Anthropic’s Claude and Grok from Elon Musk’s xAI. (…)

While DeepSeek’s flagship model is free, the company charges users who connect their own applications to DeepSeek’s model and computing infrastructure. An example is a business that wants to tap the technology to give AI answers to customers’ queries. 

Early last year, DeepSeek cut its prices for this service to a fraction of what other vendors charged, prompting the industry in China to start a price battle.

Anthony Poo, co-founder of a Silicon Valley-based startup using generative AI to predict financial returns, said his company moved to DeepSeek from Anthropic’s Claude model in September. Tests showed DeepSeek performed similarly for around one-fourth of the cost.

“OpenAI’s model is the best in performance, but we also don’t want to pay for capacities we don’t need,” said Poo. (…)

DeepSeek said in a technical report that it used a cluster of more than 2,000 Nvidia chips to train its V3 model, compared with tens of thousands of chips for training models of similar size. A few U.S. AI specialists have recently questioned whether High-Flyer and DeepSeek are accessing computing power beyond what they have announced. (…)

DeepSeek said its model, designed to solve tricky math word problems and similar challenges, was comparable to OpenAI’s reasoning model o1 even though it omitted supervised fine-tuning and focused on reinforcement learning—essentially directed trial and error. 

Jim Fan, a senior research scientist at Nvidia, hailed as a breakthrough the DeepSeek paper reporting the results. He said on X it reminded him of earlier pioneering AI programs that mastered board games such as chess “from scratch, without imitating human grandmasters first.”

Zack Kass, a former executive at OpenAI, said DeepSeek’s advances despite American restrictions “underscore a broader lesson: Resource constraints often fuel creativity.”

Bloomberg: (…) “DeepSeek shows that it is possible to develop powerful AI models that cost less,” said Vey-Sern Ling, managing director at Union Bancaire Privee. “It can potentially derail the investment case for the entire AI supply chain, which is driven by high spending from a small handful of hyperscalers.” (…)

Will DeepSeek Sink The Unsinkable Mag-7?

(…) On balance, we expect that the Mag-7 will deliver solid earnings, as suggested by the record high in their combined aggregate forward earnings of $500.1 billion during the week of January 24. The forward earnings of the S&P 493 was $1,819.3 billion that same week.

The new issue for the Magnificent-7 is whether DeepSeek will deep-six their AI aspirations.

This Chinese company recently shocked the tech industry when it reportedly spent only $5.6 million over two months to develop its latest LLM, which outperformed rival US LLMs from Meta and ChatGPT. The company kept costs down by using less powerful and cheaper Nvidia H800 GPU chips. Its LLM is available on an open-source basis.

This might be bad news for the Mag-7 that have plans to dominate the AI market with their (expensive) AI services. On the other hand, it might mean that AI systems will be more accessible and cheaper. If so, the best way to play AI might be the S&P 493 companies that will be cutting their costs and boosting their productivity using this new technology.

It might be good news for the Mag-7 that can learn from DeepSeek to design AI systems with cheaper GPUs. That would reduce their capital spending and boost their profits. It might not be a happy development for Nvidia.

U.S. Flash PMI: Output growth slows in January and price pressures rise, but employment jumps higher on sustained optimism

The headline S&P Global US PMI Composite Output Index fell from 55.4 in December to a nine-month low of 52.4 in January, according to the preliminary ‘flash’ reading, which is based on approximately 85% of usual survey responses.

The slowdown was centered on the services economy, where output rose at the slowest rate since last April, albeit sustaining the sector’s expansion into a twenty-fourth successive month. Manufacturing output meanwhile rose marginally, representing an improvement on the declines recorded over the previous five months.

Changing activity levels reflected varying demand conditions. While inflows of new business into the service sector remained robust, the rate of increase waned to a three-month low amid the first fall in overseas (export) orders since last June. Especially adverse weather was reported as a dampener of activity in some companies.

Manufacturers meanwhile reported the first, albeit very modest, rise in new orders for seven months, reflecting improved domestic demand and a softening rate of loss of export orders.

Optimism about the coming year continued to run at an elevated level. Measured across goods and services, firms’ expectations of their output in the next 12 months was unchanged in December, thereby remaining the joint-highest since May 2022.
Service sector confidence lost some of the shine from December’s one-and-a-half year high, but remained the second-highest recorded over the past year. Manufacturing confidence meanwhile surged higher, reaching the highest since March 2022 after posting the largest monthly improvement since November 2020.

Uncertainty in the lead up to the Presidential Election has been replaced with optimism about the future, notably among manufacturers, according to anecdotal evidence provided by survey respondents. Looser regulation, lower taxes and heightened protectionism were all widely cited, alongside a broader sense of improving economic conditions in the year ahead under the new administration.

However, some companies express concern over the potential for policies such as tariffs to disrupt supply chains and impact sales, or stoke inflation. Others cite concerns over the strong dollar, high prices and the possibility of policymakers taking a more hawkish stance toward interest rates than previously anticipated.

Optimism about the year ahead was matched by a jump in hiring. Employment rose in January at the fastest rate for two-and-a-half years, up for a second successive month after four months of job shedding. The improvement was led by a surge in service sector hiring, where jobs were added at the sharpest rate for 30 months, though manufacturing payroll growth also edged up to a six-month high. The latter remained modest, however, reflecting ongoing cost concerns at producers amid low sales. Firms more broadly also continued to report ongoing issues with poor staff availability.

Inflationary pressures meanwhile intensified in January. Both input costs and average selling prices rose at the fastest rates for four months, the rate of inflation of the latter now having increased for two successive months.

Factory input prices rose at the steepest rate since last August, generally linked to supplier-driven raw material price increases. Growth of service providers’ costs also revived after having cooled to a ten-month low in December, rising at the fastest rate for three months amid increased staff costs and rising material prices.

Higher costs were passed on to customers, with average prices charged for services rising at the fastest rate since last September. An even larger rise was reported for goods, the rate of inflation of which hit a ten-month high.

The S&P Global Flash US Manufacturing PMI rose from 49.4 in December to 50.1 in January, signaling a marginal improvement in business conditions within the goods-producing sector and a contrast to the deteriorations seen over the prior sixth months.

Factory production rose marginally, increasing for the first time in six months, with new orders also returning to modest growth after six months of decline. Employment increased for a third successive month, the rate of job creation the highest since July.

Suppliers’ delivery times meanwhile lengthened for a fourth straight month, adding support to the PMI (longer lead-times often indicate busier supply chains), albeit slightly less so than in December. However, inventories fell at the steepest rate for 17 months, acting as a drag on the PMI, though this in part reflected higher than anticipated use of inputs in production rather than cost-focused destocking.

Commenting on the flash PMI data, Chris Williamson, Chief Business Economist at S&P Global Market Intelligence said:

“US businesses are starting 2025 in an upbeat mood on hopes that the new administration will help drive stronger economic growth. Rising optimism is most notable in the manufacturing sector, where expectations of growth over the coming year have surged higher as factories await support from the new policies of the Trump administration, though service providers are also entering 2025 in good spirits.

“Although output growth slowed slightly in January, sustained confidence suggests that this slowdown might be short-lived. Especially encouraging is the upturn in hiring that has been fueled by the improved business outlook, with jobs being created at a rate not seen for two-and-a-half years.

“However, rising price pressures are a concern, with companies reporting supplier-driven price hikes as well as wage growth amid poor staff availability. Higher input cost and selling price inflation was broad-based across goods and services and, if sustained, could add to worries that a combination of robust economic growth, a strong job market, and higher inflation could encourage a more hawkish policy approach from the Fed.”

“Adverse weather” slowed booming services demand but service companies hired at “at the sharpest rate for 30 months” amid “robust new orders”. Meanwhile, manufacturers reported the “first, albeit very modest, rise in new orders for seven months” and payroll growth edging up to a six-month high.

Good news for the Fed on the labor market.

But the bad news is “intensified inflationary pressures” as prices charged for services rose “at the fastest rate since last September” and “an even larger rise was reported for goods, the rate of inflation of which hit a ten-month high.”

Not a hint of productivity effect there…

Recent Fed regional surveys were not as upbeat:

• The January NY Fed surveys of manufacturing and non-manufacturing activity remained fairly subdued on all fronts.
• The January Philly Fed manufacturing survey was very strong on all aspects while its non-manufacturing was soft.
• Kansas City manufacturing was rather soft.
• The December Richmond Fed manufacturing survey was also soft but services were upbeat though tempered.
• The December Dallas Fed factory indices were also generally neutral while service sector activity was good but not inflationary.

Much has been said and written about the rise in U.S. productivity (unseen in most other economies) but, in reality, the sharp slowdown in employment costs in the last 2 years has only brought the ECI back to its 2012-2019 trend as Ed Yardeni illustrates:

The inflation genie is not completely back into the bottle, is it?

The next JOLTS report will likely decline back to trend but Indeed job postings have perked up recently (through Jan. 17).

New job postings might also have changed trend lately:

The U.S. consumer is in great shape and feels giddy:

Very giddy:

January U.S. Light-Vehicle Sales Continue Q4-2024’s Growth

While demand in the middle of the month was negatively impacted by extreme weather conditions across most of the country, with a week remaining in January there is upside to the outlook. On the flipside, there could be pause among some consumers, as they wait to see how the apparent revamping of federal policies and institutions by the new administration plays out. Regardless, sales are tracking to their fourth straight increase in January.

Almost back to pre-pandemic levels:

Imagine if vehicle production recovers.

Existing-Home Sales Ascended 2.2% in December

(…) Total existing-home sales – completed transactions that include single-family homes, townhomes, condominiums and co-ops – elevated 2.2% from November to a seasonally adjusted annual rate of 4.24 million in December. Year-over-year, sales swelled 9.3% (up from 3.88 million in December 2023).

This was the third consecutive year-over-year increase after declining YoY every month for over 3 years. Total housing inventory registered at the end of December was 1.15 million units, down 13.5% from November but up 16.2% from one year ago (990,000).

Imagine if the housing market recovers.

• Slowly getting used to the old normal 6-7% mortgages.

The average 30-year fixed mortgage rate has been above 6% since September 2022 and above 7% on and off since October 2022. The daily measure by Mortgage News Daily is today at 7.11%. Freddie Mac’s weekly measure, released yesterday, of the average 30-year fixed mortgage rate was 6.96%.

The real estate industry has now given up waiting for mortgage rates to plunge to wherever and is encouraging sellers and buyers to get used to “a new normal of mortgage rates between 6% and 7%,” as the NAR had put it, which are the old normal rates that prevailed before the money-printing era started in 2009..

The CEO of Fannie Mae, the largest Government Sponsored Enterprise that buys and guarantees mortgages, also encouraged buyers, sellers, and everyone in the industry to get used to these 6% to 7% mortgage rates.

Before the money printing era, the average mortgage rates had been well above 5%. The Fed’s QE and zero-interest-rate policy, which started in 2008 and, with some interruptions, finally ended in 2022, had created an anomaly:

If housing recovers, where will the workers come from?

Data: Associated Builders and Contractors. Chart: Axios Visuals

The construction industry needs to attract 439,000 new workers this year to meet [current] demand, otherwise costs will rise — putting some projects out of reach — per projections from the Associated Builders and Contractors trade group out this morning.

Increased immigration during the Biden administration was a boon for the construction industry, which is perennially short on workers, but with the Trump administration cracking down on migration, progress could reverse.

• The issue takes on new urgency as swaths of Los Angeles need to be rebuilt in the aftermath of devastating fires.
• A coming surge in data center construction nationwide will also require resources.

Immigrants make up about 26% of the construction workforce, per census data cited by Pew Research Center last fall.

• The construction industry also employs the largest share of undocumented immigrant workers, among all other industries.
• An estimated 13% of construction workers are undocumented, per Pew. (Axios)

The total U.S. labor force grew 8.5 million since late 2020, 6 million of which were new immigrants, not only contributing to labor supply but also presumably to keep average wages lower than otherwise, not only in construction.

China’s Economy Stumbles in Sign Rebound Hinges on More Stimulus Manufacturing PMI unexpectedly fell to lowest since August

Factory activity shrank in January after three months of expansion, with the manufacturing purchasing managers’ index falling to 49.1, the lowest since August. The non-manufacturing gauge for construction and services dropped to 50.2, just above the 50-mark that separates growth and contraction. (…)

 

Both production and new orders fell to a five-month low, according to the PMI data. In a sign of weak global demand, new export orders dropped to the lowest since February.

EARNINGS WATCH

78 companies in the S&P 500 Index have reported earnings for Q4 2024. Of these companies, 78.2% reported earnings above analyst expectations and 14.1% reported earnings below analyst expectations. In a typical quarter (since 1994), 67% of companies beat estimates and 20% miss estimates. Over the past four quarters, 78% of companies beat the estimates and 17% missed estimates.

In aggregate, companies are reporting earnings that are 8.6% above estimates, which compares to a long-term (since 1994) average surprise factor of 4.2% and the average surprise factor over the prior four quarters of 6.6%.

Of these companies, 61.5% reported revenue above analyst expectations and 38.5% reported revenue below analyst expectations. In a typical quarter (since 2002), 62% of companies beat estimates and 38% miss estimates. Over the past four quarters, 62% of companies beat the estimates and 38% missed estimates.

In aggregate, companies are reporting revenues that are 1.0% above estimates, which compares to a long-term (since 2002) average surprise factor of 1.3% and the average surprise factor over the prior four quarters of 1.2%.

The estimated earnings growth rate for the S&P 500 for 24Q4 is 10.4%. If the energy sector is excluded, the growth rate improves to 13.8%.

The estimated earnings growth rate for the S&P 500 for 25Q1 is 11.5%. If the energy sector is excluded, the growth rate improves to 13.1%.

The estimated revenue growth rate for the S&P 500 for 24Q4 is 4.2%. If the energy sector is excluded, the growth rate improves to 4.9%.

The 78 companies that have reported had earnings growth of 25.8% on revenues up 4.5%. The surprise factor is +8.6%.

After Q3, the first 71 companies reporting had earnings growth of 7.8% on revenues up 4.6%. The surprise factor was +6.4%.

FYI:

While

Trump Tariffs Are a Wealth Killer Two centuries of experience proves the economic foolishness of taxing imports.

From Andy Kessler in the WSJ:

(…) In a populist bid to protect our dwindling manufacturing workforce, economically clueless Trump whisperers push tariffs. A select few workers may be helped, but most Americans will be worse off—though I doubt we’ll see riots protesting higher prices on made-in-China Gap clothes, Barbie dolls or Hush Puppies shoes. But new Trump tariffs will raise prices and restrict other countries from affording our high-margin exports—drugs, phones, planes and many software and artificial-intelligence services. That’s dumb. (…)

Notice how everything is now either a national-security concern or an emergency. Tariffs on Canadian bacon for national defense? A national emergency? (…)

Apologists try to rationalize tariffs as a negotiating tactic, violating the first rule of negotiating by even mentioning it. Tariffs are coming. Expect retaliation and inflation—precisely why tariffs don’t work. (…)

A 2021 study by Oxford Economics and the U.S.-China Business Council showed the first-term Mr. Trump’s tariffs and trade policies destroyed 245,000 jobs. The Tax Foundation estimates Trump-Biden tariffs reduced long-run gross domestic product by 0.2%—roughly $58 billion annually. On the flip side, the Peterson Institute for International Economics estimates that free trade since 1950 has cumulatively boosted the U.S. economy by $2.6 trillion, or $19,500 a household. Why go backward? Congress should reclaim its tariff power.

Instead, the backroom begging will start for tariff exemptions—machinery, certain pharmaceuticals, school pencils, cobalt for electric-car batteries, Nike Kobe 5 Protro “Year of the Mamba” sneakers—a lobbyist’s paradise. Free trade, not politicians, is best at allocating resources. Protectionism and mercantilism in the form of tariffs and subsidies, like the British Corn Laws, are inefficient, unproductive, corruption-inducing and wealth-destroying. That won’t make America great again.

FYI #2:

Source: Visual Capitalist