Fed Forced to Confront Case for Bigger and Faster Rate Rises

Federal Reserve officials spent October talking markets down from bets on a quick string of rate increases. Inflation data haven’t cooperated.

Core consumer prices, which exclude food and energy, rose at a 3.7% annualized rate over the three months through October, a broad acceleration from the 1.6% rate for the three months through July, according to a Labor Department report released Tuesday.

The figures left little doubt that the Fed will raise rates at its Dec. 8-9 meeting. The bigger questions are how quickly the central bank should move and whether it is still making the modest adjustment of two or three increases that many officials have envisioned or is instead at the start of a longer campaign to slow the economy and bring down inflation.

On Tuesday, two officials who supported the decision to hold rates steady last month suggested the Fed might now need to pick up its pace. Fed governor Christopher Waller, who spoke in London hours before the inflation data were released, said the Fed should consider a half-point increase next month if core prices rose around 0.3% in October from the previous month, which they did. And after the report, Dallas Fed President Lorie Logan hinted that a faster pace of hikes might be warranted given the current inflationary pressures hitting the economy. (…)

The strength in core prices last month showed up in categories that might be expected to reflect the pass-through of higher costs from tariffs, energy or shipping—and in categories less exposed to those pressures, such as housing, recreation and other non-transportation services, said Tiffany Wilding, an economist at Pimco.

Prices for retail goods also rose despite early-October discounting from Amazon’s Prime Day, which usually holds those figures down, she said. Surging artificial intelligence investment, meanwhile, has pushed up costs for electronic components. Prices of televisions and smartphones rose briskly last month. (…)

Officials last year were willing to look past one-off price hikes from tariffs, and in the spring, they did so for energy. But a steady run of new increases has led more officials to worry about an economy running hot enough that businesses can keep raising prices. (…)

Also in the WSJ:

Analysts described the October inflation report as concerning because price increases were broad based, ranging from a 0.4% month-over-month increase in airfares to a 0.7% increase in auto insurance. It came after data also showed larger-than-expected increases in September, creating a frustrating sense that inflation pressures aren’t going away.

“The details of today’s data are interesting largely because they are not interesting,” Eric Winograd, an economist at AllianceBernstein, wrote in a note to clients. “There was nothing obviously fluky about the data—just a generalized sense that inflation is not moving lower.” (…)

The latest inflation data provided strong evidence that investors will need to wrestle with competing threats simultaneously. Wall Street has grown increasingly wary that demand hiccups or a public backlash against data centers might trigger a slowdown in the historic boom in AI infrastructure investment. At the same time, that boom is helping to keep inflation elevated in the here and now—raising the risk that the Fed will raise rates not just once more in December but at least a few more times after that. (…)

A big contributor to inflation right now is the rise in oil prices spurred by the U.S.-Iran conflict. But those prices have already come down since mid-October and could fall further if the two sides can reach a peace deal, Arone said.

The reality is that there won’t be a “data center slowdown” until late 2028, if any, since approved projects will go ahead and that time to market is paramount.

The October CPI was bad, confirming that August and September were “real”.

CPI-Services have been rising at a steady 4.2% annualized rate for 3 months, with wage inflation (black above) picking up after being quiet between March and July. Even Kevin Warsh’s preferred measure has spiked up; trimmed-mean CPI was up 4.5% a.r. in October after +3.5% in September and +2.7% in August.

It might take more than falling oil prices, like, maybe, more productivity from that Trump-made declining labor force. Some law of economics are pretty stubborn.

• Fed Likely To Hike By 50bps In December (Ed Yardeni)

(…) Echoing the CPI report’s fire alarm, October’s NFIB survey showed that the net share of small business owners planning to raise selling prices jumped to 37%, the highest since July 2022, while 24% planned to raise worker compensation, the highest since December 2025. (…)

The FOMC focuses on PCE inflation which will be out at the end of the month. Goldman Sachs, most surprised by October’s core CPI coming in at +0.30% vs its forecast of +0.16%, acknowledges that

the upside surprise was driven by firmer-than-expected readings in a broad set of categories. Based on details in the CPI report, we estimate that the core PCE price index rose 0.35% in October (vs. our expectation of 0.23% before the CPI release), corresponding to a year-over-year rate of +3.30%.

That would come after +0.32% in September for a 2-m growth rate of 4.1% a.r., double the target that Warsh has committed to achieve.

• Trump’s Plan to Import Russian Diesel Stalls on the High Seas Tankers initially bound for U.S. change course after opposition to deal with Putin makes buyers nervous

President Trump’s efforts to supply the U.S. with Russian diesel have yet to result in any new imports into the country, as several fuel tankers bound for America shifted course following difficulties finding buyers willing to accept the controversial cargoes. (…)

Critics, including senior Democrats and Republicans in Congress, said that Trump’s carve-out of diesel from U.S. sanctions on Russia was illegal. Another concern was that accepting the diesel could cause companies to run afoul of international sanctions: fuel imports from Russia are still banned by the U.K., European Union and Switzerland. (…)

U.S. oil companies aren’t short of diesel: domestic refiners already make more than enough diesel to sate the country’s consumption and export more than 1.5 million barrels a day to overseas buyers. Despite this, the U.S. average for a gallon of diesel has climbed to $6.16 as of Tuesday, as global supplies of the fuel have tightened following the Middle East conflict. It is up from $3.75 before the war. (…)

Under its deal with Trump, Russia planned to restore its global diesel exports this year to less than one-fifth of what it was shipping before Ukraine’s drone-strike campaign, based on figures released by the president.

Those volumes represented a drop in the bucket compared with the global supply shortfall, analysts said.

“Combined diesel exports from the Mideast and Russia are still very low, with little movement there,” said Martijn Rats, chief commodities strategist at Morgan Stanley.

Why not a presidential decree capping US diesel prices… After all, these oil majors are making a windfall from the war against Iran.

• Masayoshi Son’s Huge Bet on Electrical Turbines Backfires

(…) Like an overeager miner who tries to corner the market for picks and shovels in a gold rush, Son’s SoftBank Group ordered so many turbines that even after fully supplying the world’s largest gas power plant, it was left with reservations for far more than it could possibly use.

The global tech investor switched course on Tuesday, reporting a $3.7 billion hit to its earnings due largely to canceled reservations on power generation equipment. (…)

Seeing that the rapid growth in data centers would require a historic surge of electricity production, between late 2025 and early 2026, SoftBank began placing reservations for the massive electricity generating machines, largely from GE Vernova, according to people familiar with the orders.

Turbines—the engines of gas plants that convert natural gas to electricity—require orders years in advance. (…)

The orders involved a substantial, nonrefundable reservation fee. Turbine manufacturers typically charge tens of millions of dollars per gigawatt for a reservation. (…)

The reservations strengthened SoftBank’s hand when it engaged in negotiations with the Trump administration to build a massive gas-fired power plant in Ohio tied to the larger Japan-U.S. trade agreement.

The final deal, announced in March, calls for the Japanese government to fund a $33 billion, 9.2-gigawatt gas plant on a former uranium enrichment site in southern Ohio. Construction is scheduled to start early next year, and the plant is to be owned by a joint venture of the U.S. and Japan.

SoftBank was named builder and operator of the plant, and it plans to use the electricity for a neighboring 8-gigawatt data center, which has been leased to OpenAI in what would be the world’s largest data center.

But even after supplying the plant and other smaller plants SoftBank plans, the company still had ordered too many turbines.

Its initial plan had been to use the turbines for future gas-powered plants, but in recent months, as additional payments for the turbine orders loomed, SoftBank made a change. The company opted to cancel its excess reservations, people familiar with the turbine deals said.

It also unwound contracts for some turbines that were further along in the order process, one of the people said.

A spokesperson for GE Vernova, the turbine supplier, said there was “no negative or positive impact” to the company from SoftBank’s moves. (…)

Some relief for real data center promoters.

• CoreWeave Meets Wall Street Expectations, Shares Rise

The so-called neocloud company—which buys advanced artificial-intelligence chips known as graphics processing units from Nvidia, installs them in data centers and leases them to companies such as Microsoft and OpenAI—reported revenue of $3.7 billion for the quarter that ended in September. And it said its backlog of business hit $133 billion. Analysts polled by S&P Global had expected quarterly revenue of $3.6 billion and a backlog of $131 billion. (…)

CoreWeave’s chief executive, Michael Intrator, sought to tamp down concerns about the company’s financing costs.

“Our growth is supported by multiple sources of funding, including financing backed by customer contracts, operating cash flow, and approximately $9 billion in customer prepayments and deposits expected in the next three months,” he said. “That financial flexibility positions us to keep delivering the infrastructure and software customers need to build, run, and scale AI.”

At the same time, the company said it launched a new program to sell stock to raise money. That will dilute existing shareholders and suggests the company can’t raise debt financing at attractive rates. (…)

CFO Nitin Agrawal said that CoreWeave is charging between $20 million and $25 million per megawatt for its longer-dated contracts, and up to $40 million per megawatt for short-term commitments. Shorter-duration contracts can pay back the initial investment in anywhere from six to 18 months.

• TSMC’s Revenue Rises 60% in a Sign of Resilient AI Chip Demand

The go-to chipmaker for Nvidia Corp. and Apple Inc. reported October revenue of NT$586.9 billion ($17.9 billion). That pace was up from around 51% for the September quarter. Analysts on average are projecting 63% growth for the year’s final three months. (…)

TSMC — a key barometer for the industry because it makes chips for a broad swathe of tech players — is betting on a fundamental shift. It’s trying to build and furnish about 20 factories at home and overseas, an unprecedented pace for a company that typically works on four or five new buildings simultaneously. (…)

The company has so far managed to accelerate sales growth and ramped up capacity expansion. The company reiterated its capital expenditures will reach as much as $64 billion, a record, this year. (…)

Even with its $250 billion build-out, leading-edge capacity stays effectively sold out through 2028, supporting price increases.

OpenAI Continues to Suffer Heavy Losses as Revenue Rises to $10 Billion The company improved its adjusted operating margin but remains far off from profitability

OpenAI grew revenue by roughly 50% to $10 billion from the second to the third quarter, but continued to report heavy operating losses, showing mixed results in its effort to catch up with industry leader Anthropic.

(…) The maker of Claude recently disclosed that its revenue rose to $17 billion in the third quarter while reporting a smaller operating loss of $567 million during that period.

The numbers show that OpenAI is accelerating revenue growth but still has a long way to go to match its rival’s sales and operating efficiency. The company grew revenue by only 18% in the second quarter compared with the first, The Wall Street Journal previously reported.

OpenAI told investors Tuesday that its adjusted operating margin improved from minus 150% to minus 109% from the second to the third quarter, according to documents viewed by The Wall Street Journal. Even so, the company’s adjusted operating losses ticked up slightly from $10.1 billion to $10.9 billion, based on those figures.

The calculations for the losses exclude stock-based compensation, which ticked up to $2.6 billion in the third quarter, according to a person familiar with the matter. Anthropic’s operating loss includes stock-based compensation. (…)

OpenAI also told investors that the number of paying users on ChatGPT grew to roughly 100 million, or 8.5% of total weekly active users. That percentage is slightly higher than what OpenAI reported for the previous quarter, suggesting progress in its effort to increase paying subscribers for the world’s most popular consumer chatbot. (…)

OpenAI has a large consumer business to support and maintains an array of research programs in areas from robotics to image generation. Anthropic has taken a more focused approach on building for enterprises, though its revenue is more concentrated on those customers as a result. (…)

US Loses Grip on $121 Billion Mobile Gaming Sector to China American studios are losing ground to Chinese rivals that can make games quicker, cheaper and more profitably.

(…) “It’s extraordinarily hard for startups like ours – despite having an incredible amount of capital – to find individuals to compete on the global stage, which gaming has become in the past 10 years; and one in which the US is no longer the dominant force,” Liu said.

China’s advance has been rapid. As recently as early 2024, the US and China were virtually neck-and-neck in terms of revenue share from the world’s top 100 mobile games, according to data from Sensor Tower. But by mid-2026, China held 40% of the global market, compared with just 16.5% for the US, as the Asian nation dominated the charts with hits like Honor of Kings and Whiteout Survival.

In a cruel twist, China’s biggest overseas market last year was the US itself. Chinese titles account for over 18% of revenue from the top-100 games there, up from 8% at the start of the decade. At the same time, the share held by US publishers has shrunk from 40% to less than 30%. (…)

Mobile games’ ubiquity — more than 3 billion people will play them this year — means they’re an understated but potent channel of soft power. The highly stylized fantasy and anime characters that dominate top Chinese titles now anchor the daily digital feeds of Gen Z and Gen Alpha, underscoring the broader departure from American cultural hegemony. (…)

In a nod to the structural shift at play, some top American developers like Microsoft Corp.’s Activision Blizzard and Take-Two Interactive Software Inc.’s Zynga are using Chinese studios to make their games.

It’s not just China taking share from the US. Studios in Singapore have produced Garena Free Fire. Turkish startup Dream Games has the popular Royal Match puzzle game and a swell of M&A activity has underpinned rapid growth in recent years. In the second quarter, publishers from those two countries combined accounted for 18% of revenue from the top 100 games in the US, up from 1.2% in the first quarter of 2020, according to Sensor Tower.

Meanwhile, Finland is a global gaming hub that’s produced Angry Birds, as well as some of the world’s current top titles like Supercell’s Clash of Clans and Clash Royale. Still, there’s no escaping China’s reach; Supercell has been majority owned by Tencent Holdings Ltd. since 2016. (…)

American firms looking at overseas markets face hurdles in China. A tricky regulatory environment effectively boxes them out of the market that’s expected to generate $41 billion by 2030, according to Niko Partners. In the first half of the year, the National Press and Publication Administration approved 1,110 domestic video games for Chinese consumers and just 37 from international game developers.

That lopsided access has been a longtime bugbear of Western countries, which after years of relative openness are tightening access to Chinese technology. The US has investigated Tencent over its stakes in Riot Games and Epic Games and the company has been designated as a Chinese military company — a classification it denies.

But talent is where China really has the edge.

Salaries are the biggest expense for game companies and game makers. They pay, on average, $142,000 for every developer in the US, according to a survey by the Game Developers Conference. In Shanghai, where salaries tend to be higher, the average game developer earns about $50,000 a year, according to data from Glassdoor. (…)

And now, AI is increasingly contributing.

China blocks SoftBank’s $5.4bn takeover of ABB’s robotics division

China has told SoftBank it will not allow the $5.4bn acquisition of ABB’s robotics division to go ahead in a shock move that threatens to throw Masayoshi Son’s ambitions in physical AI into disarray. (…)

China is the biggest market for ABB’s robotics unit, with more than 90 per cent of its robots for Chinese customers manufactured locally. The Swiss-Swedish company employs about 12,700 people in China. (…)

The ABB division’s operations in China were a significant attraction for the deal, say people close to the Japanese group.

The NDRC review had included detailed scrutiny of ABB’s Chinese supply chains and a demand to retain some of the robotics unit’s manufacturing capabilities in China, according to people familiar with the matter.

The deal cleared China’s competition review in February. It then became subject to a foreign investment security review that highlighted Beijing’s concern over the transfer of assets developed in China to a Japanese company at a time of heightened tensions with Tokyo and strategic competition with Washington.

The NDRC had been concerned about robotics data being transferred overseas, where it could be used to train AI models, including those developed by OpenAI, in which SoftBank is a major investor, said people familiar with the matter.

ABB set up a joint venture with a Chinese partner in 1992 and its China operations, particularly its robotics business, have long benefited from government support. Shanghai’s government also helped ABB build its $150mn robotics mega-factory in Pudong, which began operating in 2022.

According to people briefed on their thinking, regulators had also examined keeping intellectual property relating to some of ABB’s robotics business in China, potentially requiring parts of the business to be spun off to Chinese owners for the deal to proceed.

Very significant, well beyond the impact on Softbank.

Physical AI is where AI interacts with the real world through robots, autonomous machines, vehicles and industrial equipment. Digital AI creates and processes intelligence; physical AI uses that intelligence to perform work in the real world.

Physical AI could become a market worth hundreds of $billions annually. McKinsey estimates that general-purpose robotics alone could generate a $370bn market by 2040 in its base scenario.

With ABB, SoftBank was attempting to position itself for the transition from AI that thinks to AI that acts. That transition could be one of the defining investment themes of the next 10–20 years.

That gets right into (against) China’s strategy.

China’s rejection is effectively asserting control over industrial/robotics AI data.

Industrial robots generate valuable information about:

• How objects are manipulated.

• How machines move and interact with their surroundings.

• How manufacturing processes operate.

• How robots respond to unexpected situations.

• How physical tasks can be performed more efficiently.

The data generated by millions of industrial operations could become a strategic AI resource.

China is applying a national-security bend to that data, rather than treating it simply as the commercial property of a multinational company.

This has potentially profound implications for international AI development.

It suggests that access to industrial data could become as strategically sensitive as access to advanced semiconductors.

China understands the implications of a Japanese company, already a major investor in American OpenAI, acquiring a European company with substantial advanced robotics operations/knowledge/data in China.

A clear no-no for Beijing.

The wider message:

Multinational companies are discovering that owning a business globally does not mean they can freely transfer control of all its assets, intellectual property and data across national borders.

This could make future cross-border acquisitions in robotics, AI, semiconductors, autonomous vehicles and advanced manufacturing considerably more complicated.

The blocking of Meta’s acquisition of AI company Manus earlier in 2026 seemed relatively minor but it was the first indication that China is serious about AI leadership and corporate data.

It thus looks like Physical AI will also be split into 2 ecosystems.

Physical AI requires integration of several things:

• Advanced AI models.

• Computing power.

• Sensors and machine vision.

• Robotics hardware.

• Industrial manufacturing expertise.

• Real-world operational data.

Control over any one of these can create strategic advantages. China considers that the entire combination as strategically important.

The risk is that companies will find it increasingly difficult to develop globally integrated AI and robotics businesses.

The AI industry is moving from a competition over who builds the most capable models to a much broader competition over who controls the computing, data, machines and industrial ecosystems that make those models useful.

Our research trip to China last December included robotics.

From Bots On The Ground:

(…) On the battlefield for technological supremacy, robotics is now on the front line and China’s efforts are centered in Shanghai.

So far, robotics was essentially supplying fixed industrial robots. China already totally dominates this field.

The battle is now getting more serious: this is the “bots on the ground” era.

The Shanghai Humanoid Robot Kylin Training Ground, covering approximately 5,000 square meters (54,000 sq ft) in the Zhangjiang area of Shanghai, is China’s first national-level, public facility designed to mass-train humanoid robots from different companies in a shared environment. (…)

China clearly displayed its robotics objectives by inserting the word “kylin” above. “Kylin” is a standard brand name for homegrown Chinese “core technologies” intended to replace foreign dependencies.

The facility’s core purpose is to accelerate the entire Chinese robotics industry by centralizing data collection and standardizing how robots learn complex tasks.

It is a “heterogeneous” facility, meaning it trains robots from competing manufacturers (like AgiBot, Fourier, and Unitree) side-by-side rather than serving just one company.​

It aims to collect 10 million high-quality physical data entries (robot movements, interactions) to build a shared “brain” for the industry.​ (…)

The facility also acts as a standard-setter. Just as cars need to pass crash tests, robots here must pass proficiency exams in these standardized environments to prove they are ready for factory work. (…)

The “US model” relies on private enterprise, venture capital, and a university system that fosters fundamental research and innovation.

The “China Model” offers several strategic advantages over the decentralized US approach.

Its primary superiority lies in treating AI training data as a public utility rather than a private asset, which theoretically allows the entire industry to advance faster than any single company could on its own.

In the US, valuable training data is trapped in “walled gardens.” Tesla does not share its Optimus data with Boston Dynamics, and Figure AI does not share with Agility Robotics. This means every US company must solve the same basic problems (e.g., how to open a door, how to walk on gravel) from scratch.

The Kylin center aggregates data from 100+ (1000+ in 2027) different robots into a single, massive dataset targeting 10 million entries.​ This creates a shared “industry brain.”

A startup’s robot can theoretically learn from the mistakes made by a competitor’s robot yesterday, accelerating the baseline intelligence for everyone. (…)

The US market typically waits for a clear winner to emerge before setting standards (e.g., waiting for Tesla’s NACS charging port to become the standard). This can lead to years of fragmentation where parts and software aren’t compatible.

The Kylin center acts as a central certification authority from day one. By forcing all robots to pass the same “exams” in the same physical facility, it enforces early standardization.​ This ensures that supply chains (batteries, joints, sensors) can be standardized faster, lowering component costs for the whole industry.

The Kylin center is “heterogeneous,” meaning it trains robots with different body shapes and mechanical structures side-by-side.​ This diversity is rare. Most US labs only contain one type of robot. Training AI on diverse hardware makes the algorithms more robust.

The China model (centralized infrastructure) and the US model (market competition) generate completely different economic benefits. While the US model maximizes corporate value and radical innovation, the Chinese model is designed to maximize manufacturing scale and supply chain dominance. (…)

US labs have text data; Chinese labs have physical data. This will create a new “data wall” that the US cannot easily climb because it lacks the manufacturing environments to deploy these robots at scale. (…)

Per the International Federation of Robotics:

China totally dominates the Industrial Robots field …

… and has boosted the share of its domestic suppliers from 32% to 55%, creating a huge domestic, efficient ecosystem.

As of September 2026, IFR has identified 174 humanoid robot manufacturers globally. More than 50% are in China, a proportion rising rapidly as a wave of new company creation coincides with breakthroughs in Gen AI.

But all is not lost for the US. Watch this Apple video: Perceptive Humanoid Motion from Simulation.

Here’s what the authors wrote (BTW, 5 Chinese and 3 Europeans):

Humanoid robots have the potential to automate a wide range of physical tasks.

Progress toward generalist humanoid autonomy, however, remains constrained by a data bottleneck: mapping pixels and free-form language to low-level joint commands requires paired supervision, and no corpus of such data exists at sufficiently large scale.

Some approaches rely on human teleoperation and wearable capture devices, which require at least an hour of human time to produce an hour of data.

We present HALO, a humanoid autonomy system powered by a pipeline that synthesizes vision-language-motion data across a thousand procedurally generated homes. The corpus scales with compute rather than human labor.

HALO acquires a repertoire of physical skills, including sitting on chairs, navigating to named objects, stepping sideways through half-open doors, crouching and crawling beneath tables, and pushing boxes. It orchestrates these skills to solve composite, long-horizon tasks.

Without ever seeing a real home during training, HALO transfers zero-shot to the physical world, achieving a 91% success rate across 360 trials in six unmapped real-world environments.

US software teams (including Apple with HALO) often must run their models on Chinese platforms like the Unitree Robotics G1 because equivalent domestic hardware is either scarce or cost-prohibitive.

The US seeks to overcome the data collection bottleneck using systems like Apple’s HALO, Nvidia Cosmos, and Google DeepMind Genie to generate hundreds of thousands of synthetic hours of photorealistic motion and perception across randomized 3D environments.

While simulation excels at visual navigation and high-level trajectory planning, physics engines struggle to accurately model complex micro-frictions, soft-body deformations, and tactile grasping. Analyses from robotics practitioners at Field Motion AI emphasize that contact-rich manipulation (such as tool use or precise mechanical assembly) still heavily relies on real-world demonstration data

Analysts from the Carnegie Endowment for International Peace note that Chinese state initiatives and factory deployments generate massive feedback loops. Deploying thousands of robots directly onto manufacturing floors yields corner-case physical failure data that synthetic models cannot anticipate.

And the US must convert whatever algorithmic lead it may get into commercial dominance, requiring overcoming reliance on foreign hardware manufacturing.

Trump: New Iran attacks “quite unlikely”

President Trump said Monday that a renewed military campaign against Iran is “quite unlikely” and “doesn’t seem to be necessary.”

Trump stopped short of saying the war was over, but his comments on Truth Social and at an impromptu press conference were the furthest he went in that direction since February.

The president in recent days has been weighing three options: launch new strikes, continue to strangle Iran’s regime economically, or cut a deal that includes significant concessions to the Iranians.

• Trump signaled on Monday that he’s going with the strangle strategy for now. That means keeping up the “status quo” — continuing the naval blockade, in addition to a new operation dubbed “Iron Gate” to cut off land routes that Iran uses to move advanced weaponry and other technology.
• Trump said the war is “effectively over” militarily. But he added that he doesn’t want to formally announce that before he makes sure U.S. allies in the region are safe.

Trump made his comments hours after meeting with Qatari Prime Minister Sheikh Mohammed bin Abdul Rahman Al Thani about the negotiations with Iran.

• “Qatar, Saudi Arabia, UAE, Bahrain, Kuwait, and others, would prefer the status quo” to resuming the war, Trump said.
• The Qatari prime minister also met separately with Vice President Vance, who’s been leading the Iran talks for the administration in recent weeks. Al Thani wrote on X that he discussed with Trump ways “to progress the U.S.-Iran talks.”
• A diplomat briefed on the details of the meeting said Trump made clear to Al Thani that he wants a deal and will give negotiations a chance. But he also noted that if Iranians don’t take negotiations seriously, he could reverse course, the diplomat said.

“Should there be any change with respect to developments in the Middle East, the United States of America is locked and loaded and ready to go. At this point, however, that seems to be quite unlikely,” Trump wrote on Truth Social.

• “We are seeing positive momentum toward an end to this Conflict, and will ensure that America’s interests are at all times protected. At the conclusion of this Military Engagement, the American People, and the World, will be far safer and more secure,” he added.

Trump said that Iran is “talking very politely” with the U.S. and noted that there is a “good chance that could get ended,” seemingly referring to the military conflict.

“Hope we are gonna be able to make a deal with them,” he added.

Iran’s Supreme National Security Council secretary, Mohsen Rezaei, wrote on X shortly after Trump made his comments that the war “has wrecked the U.S. government.”

He called Trump comments about negotiations with Iran “imaginary” and called on the U.S. “to SURRENDER” to Iran’s conditions for ending the war.

In effect, the GCCs told Trump to stop hurting them.