Democracy Now: “Empire of AI” Karen Hao on How AI Is Threatening Democracy.

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Yuval Noah Harari: Power of algorithms

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Robert A. Pape on X: Most are missing what really happened today. Iran EXPANDED its control of Hormuz ….

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Truth Matters: Fareed Zakaria One week visit to China and what he has to say about China

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Axios: 1 page to end war

 1 page to end the war
 
Photo illustration of President Trump over the flag of Iran with soldier's from Iran in the foreground
Photo illustration: Sarah Grillo/Axios. Photos: Getty Images
 
Stocks futures surged and oil prices fell in response to this exclusive reporting by Axios Middle East expert Barak Ravid:

The White House believes it’s close to an agreement with Iran on a one-page memorandum of understanding to end the war and set a framework for more detailed nuclear negotiations, according to two U.S. officials and two other sources briefed on the issue.

The U.S. expects Iranian responses on several key points in the next 48 hours. Nothing has been agreed yet, but the sources said this was the closest the parties had been to an agreement since the war began.

Among other provisions, the deal would have Iran commit to a moratorium on nuclear enrichment, the U.S. lift sanctions and release billions in frozen Iranian funds, and both sides end restrictions on transit through the Strait of Hormuz.

Many of the terms would be contingent on a final agreement, leaving open the possibility of renewed war or an extended limbo in which the hot war has stopped but nothing is truly resolved. 

Reality check: The White House believes the Iranian leadership is divided, and it may be hard to forge a consensus across factions. Some U.S. officials remain skeptical that even an initial deal will be reached.

But the two U.S. officials said President Trump’s decision to back off his newly announced operation in the Strait of Hormuz and avoid a collapse of the fragile ceasefire was based on progress in the talks. 

Behind the scenes: The one-page, 14-point memorandum of understanding is being negotiated between Trump’s envoys Steve Witkoff and Jared Kushner and several Iranian officials, both directly and through mediators.More details.
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Fortune: Economists have found an answer to slowing cognitive decline: avoid retiring early, study finds

EconomyRetirement

Economists have found an answer to slowing cognitive decline: avoid retiring early, study finds

Sasha Rogelberg

By 

Sasha Rogelberg

Reporter

May 5, 2026, 12:09 PM ET

A man shaves wood pieces from a block.

A team of economists found loss of employment can lead to greater cognitive decline.Ulrich Baumgarten—Getty Images

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While economists sound alarms about Gen Z unemployment, new research points to a quieter crisis: Gen X workers retiring years before 65—and paying a steep cognitive price for it.

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About 35% of workers who have been unemployed for more than 24 weeks are over the age of 55, according to an April 2025 analysis. Over the last 35 years, the retirement age for men in particular has gotten younger, with about half of retirees saying they made the choice to stop working.

For these workers, the financial risks are ample: Few retirees have a pension outside of Social Security, and with Social Security’s average benefit at about $18,000 per year, many will take the benefit before its peak, receiving far less money retiring at 62 than at 70.

But new research suggests it’s not just money early retirees need to worry about, it’s also their health. A working paper published by the National Bureau of Economic Research found that among Americans ages 51 to 75, leaving employment led to cognitive decline, while consistent employment caused greater sustained cognition.

Though research has previously shown a correlation between early retirement and cognitive decline, University of California at Irvine economists sought to prove a causational relationship between the two. The researchers used data from 40,000 participants from the University of Michigan’s Health and Retirement Study (HRS), a longitudinal study that measures, among other variables, cognitive ability over time. They overlaid that data with County Business Patterns data generated by the U.S. Census to look at changes in cognitions following large labor demand shocks, finding “substantial declines” in cognitive scores following periods of meaningful negative employment shifts.

The results of the study are clear to David Neumark, a UC Irvine professor of economics and study coauthor: There’s an urgent reason to keep Gen X in the workforce.

“This would be yet another reason to say, ‘We should really think about the potential consequences of a really large-scale decline in employment,’” Neumark told Fortune. “That’s probably the group for whom this might be more serious.”

Cognitive decline as a wake-up call for what’s keeping Gen X out of the workforce

The negative economic consequences of early retirement may begin with how an aging population could weigh on social benefits.

“Cognitive decline is really expensive,” Neumark said.

Alzheimer’s disease and other dementias, which often begin with cognitive decline, cost the U.S. economy an estimated $781 billion in 2025, according to an analysis from the University of Southern California. In addition to care costs, that sum includes money from lost earnings from patients and caretakers unable to work. Even the narrower projection from the Alzheimer’s Association sees direct health and long-term-care costs alone hitting $384 billion to $409 billion in 2025–2026, with an additional $413.5 billion in unpaid caregiving paid out in 2025 on top of that.

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Treatment is often prolonged, Neumark explained, because cognitive decline is a condition individuals may live with for years, and not one they die from. 

Those costs may be on top of an aging population no longer working. A 2016 study predicted annual GDP growth would slow by 1.2% between 2016 and 2026 as a result of a graying population. However, though about one-third of this slowdown was a result of fewer older people in the workforce creating less output, most of the impact of an aging population on GDP was a result of older workers being less productive.

Still, Neumark said, there’s more that can be done to make sure older workers stay in the workforce longer. For example, about 28% of Social Security Disability Insurance (SSDI) recipients attempt to return to work within 10 years of receiving benefits. While some can’t return to work because of permanent disability, others could make more working than on SSDI, Neumark argued. Beyond trade adjustment assistance and retraining efforts already in place, he called for proactive employment policies, such as accommodating flexible hours or phased retirement programs.

Neumark said greater awareness of the downsides of staying out of a job—increased likelihood of cognitive decline and its related diseases—could be a motivating factor for individuals weighing early retirement.

“We have some influence on the margins about both people losing jobs and things we might do to help them find reemployment if they did,” he concluded.

The Fortune 500 Innovation Forum will convene Fortune 500 executives, U.S. policy officials, top founders, and thought leaders to help define what’s next for the American economy, Nov. 16-17 in Detroit. Apply here.

About the Author

By Sasha RogelbergReporter

Sasha Rogelberg is a reporter and former editorial fellow on the news desk at Fortune, covering retail and the intersection of business and popular culture.

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The Harvard Gazette: Should you ask ChatGPT for medical advise?

Health

Should you ask ChatGPT for medical advice? 

Physician and AI researcher Adam Rodman says AI can be helpful but has some tips on how, when to use it safely

Sy Boles

Harvard Staff Writer

May 5, 2026 6 min read

Adam Rodman standing in hospital corridor.
Adam Rodman.Niles Singer/Harvard Staff Photographer

Physicians noticed something unusual in the late 2000s: Patients were coming to appointments armed with sometimes-dubious medical information they had gleaned online from “Dr. Google,” according to Adam Rodman, an internist and AI researcher.

Today, about 68 percent of adults have turned to a search engine for medical advice in the past. But Dr. Google has a competitor. About 32 percent of adults, approximately half of those who sought advice online, turned to AI chatbots for help. 

Rodman thinks such resources, used appropriately, are an overall net good. In op-eds and online coursesRodman, a Harvard Medical School assistant professor of medicine at Beth Israel Deaconess Medical Center, has shared advice for how to best employ Dr. Chat. 

In this interview, edited for length and clarity, Rodman offers a stoplight system to figure out when it’s safe to ask a chatbot, and when you should really just ask your doctor.


How were doctors thinking about online medical information before the age of AI? 

The early literature refers to this as the internet-informed patient. In the early 2000s, doctors noticed people would come into their appointments with articles they found online, but it was still only among really tech-savvy people. It certainly wasn’t a normal interaction. 

Then in the late 2000s, search engines started to take advantage of neural network technology, and they were able to serve up more relevant health information. They figure out what you’re going to want to read next, and they give it to you.

That’s when we first got the phrase “Dr. Google,” often used as a pejorative, from doctors who saw patients coming in with a level of confidence that may or may not have been earned.

Of course, there are patients who know a lot about their health and are very well informed, but we also saw a lot of patients misinformed. 

That’s where we get this concept of cyberchondria. It’s related to hypochondria: this idea that search engines can drive people to more and more extreme places until you go from googling your headache to reading about glioblastoma multiforme — and research has shown that it’s a real phenomenon. 

“Both Google and AI companies are now very aware that people are using their tools for health information and are trying to build in safety mechanisms.”

We all have understandable and reasonable anxieties about our health. Seeking out information is something fundamental about humanity.

The problem is when that starts to interact with these recommendation algorithms that are optimized for engagement, and for showing you what you want to see even if it’s incorrect. 

Now let’s bring AI into the mix. Is it any different to ask a chatbot about symptoms versus googling them? 

It’s nuanced. In one sense, LLMs do exactly what Google does: They serve you up the things you unconsciously want to hear, even if those things make you anxious. 

On the other hand, unlike with a Google search, some people feel they have a relationship with an LLM. LLMs speak with extreme authority and confidence no matter what they say. It’s under-explored the extent to which that could make cyberchondria worse. 

Both Google and AI companies are now very aware that people are using their tools for health information and are trying to build in safety mechanisms. The bots will tell you to go to the emergency room or call your doctor, those sorts of things.

But at least theoretically, language models are much, much better than Google, especially the more modern reasoning models, when it comes to identifying medical conditions.

What do you mean by “theoretically”?

There was a very good paper earlier this year from a researcher named Andrew Bean that tested several LLMs and found they performed very well at identifying medical conditions alone, but did much worse in conversation with real people.

What that shows is that user interaction matters a lot. The way people interact with the model, the clarity of their questions, matters. Those psychological phenomena we talked about are present in ways that are really hard to mitigate. 

What kinds of health questions are safe to ask an LLM, and what kinds aren’t? 

I would divide it into a stoplight system. Red: never safe. Yellow: sometimes safe. Green: almost always safe. 

In the green light are general questions about health, where the quality of the information is not particularly context-dependent.

For example, “I have diabetes and my doctor has told me I need to eat a diabetic diet. Here are some things I like to eat. Can you help me build a diabetic meal plan?” Or “I’m trying to start a new exercise program, can you help?” Or “My doctor just prescribed me amlodipine. What are some common side effects?” 

In the yellow light are questions where you want to involve a doctor in the loop. For example, prepping for your visits, understanding a visit after it happens, or understanding a test result that doesn’t entirely make sense to you.

Let’s say you just left your doctor’s visit and you’re a little bit confused about what’s going on. Log in to your patient portal, copy that note, take out your identifying information, plug it into an LLM, and then have a discussion.

With these kinds of questions, you really need to make sure you’re putting in enough health context to help LLM give you a good response. So you need to have some understanding of prompt engineering to get information that’s helpful for you.

In the red light — and I should stress that this might change in the future as technology develops — are things like asking an LLM how to manage a condition, if your doctor is prescribing the right medication, or why you were prescribed drug X over drug Y. These are highly contextual questions that the models aren’t trained for. 

In short, the best way people can use it right now is not as a replacement for medical advice but as a way to help prepare or increase your understanding before or after visits. 

Are there privacy concerns when it comes to sharing health information with AI? 

It’s not inherently riskier to share data with an AI firm than with a search engine. That said, the major companies — OpenAI, Anthropic, Microsoft — are now developing health functions specifically so that people can put in their medical information directly, and that’s quite new.

Additionally, studies have shown people do share more information with an LLM than they would with a search engine. So from a technology perspective, it’s no different, but in practice it is a much bigger security concern. 

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The Deep View: Quantum, AI spur biological breakthroughs

RESEARCH

Quantum, AI spur biological breakthroughs

For years, IBM has been at the forefront of quantum computing. At this year’s Think conference, that ambition was on full display. During the opening keynote, IBM CEO and Chairman Arvind Krishna highlighted quantum’s potential with the technology having the capability to unlock new discoveries at an incredibly quick pace — including AI developments.“Quantum can help uncover what AI cannot yet compute, then AI learns from the quantum and can make faster and faster progress on algorithms and on computations to give you a state of where we are,” said Krishna. 

To showcase the tangible use cases of quantum computing, IBM highlighted a biological research milestone achieved with the Cleveland Clinic and Riken: using two of the IBM quantum computers and two of the world’s most powerful supercomputers, the companies were able to simulate protein complexes spanning up to 12,635 atoms. In October 2024, it was only able to simulate 10. This is important, as the molecules in your body are proteins, or the “workhorse in the cell” that allow people to exist every day, as Serpil Erzurum, EVP and chief research and academic officer at the Cleveland Clinic’s Lerner Research Institute, explained during the keynote. 

Understanding the 3D structure and motion of a protein is key in biological research, as it helps researchers understand how a drug candidate could bind to a protein and develop effective drugs. Yet, it has remained a challenge as classical computers can only approximate solutions. Erzurum emphasizes that this development is “a moment.”

“Everyone will want to see what these structures look like to understand biology, disease, what’s going wrong if it’s not working, and more importantly, what can I make to fit into the three dimensional structure, to change the structure of that protein–because that’s therapy, and that can make a difference in life,” said Erzurum. Another example Erzurum noted is using quantum computing and machine learning to dramatically speed up the identification of which treatments a harmful microbe is sensitive to, potentially saving lives given that infections remain a leading cause of death globally. 

In a separate Q&A with analysts and select press, Krishna did make it clear that in the next three years, he does not see quantum as replacing either AI or classic CPUs, but rather it will solve problems the two cannot solve, such as the modeling molecules example.While quantum computing ranks among the most cutting-edge technologies available today, so advanced that it can be difficult to fully realize, it is already demonstrating tangible results. Its relevance is also becoming increasingly difficult to overlook, particularly given its potential to address some of the most pressing challenges facing the AI industry, including the growing demand for compute power. That said, it is important to contextualize the technology’s current trajectory: Widespread mainstream adoption and full commercial deployment remain a considerable way off, largely because the hardware required to run quantum systems at scale is still enormously costly to build and maintain.

Disclosure: Sabrina Ortiz’s travel to IBM Think was paid by IBM. The Deep View’s coverage is editorially independent from the companies we cover. 
 
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Al Arabiya English: US Secretary of State Marco Rubio warned that if Iran had a nuclear weapon and closed the Strait of Hormuz, it could push gas prices to “9 dollars a gallon” with no means to respond, adding this is why Iran “cannot have a nuclear weapon”

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Axios: AI’s trillion-dollar risk

AI’s trillion-dollar risk
 
Illustration: Lindsey Bailey/Axios

Our financial system is leaning on load-bearing AI spending that may never pay off, Axios’ Madison Mills writes.

Big Tech companies are set to spend $700 billion on their AI ambitions this year, Goldman Sachs says — double last year’s bill.

That could swell to over $1 trillion next year, some estimates say. 

AI costs increased at four of the Big Tech companies that reported earnings last week.

Computing power is constrained while demand is at record highs, so AI costs keep rising. 

The big picture: Business AI spending is now contributing more to U.S. economic growth than consumer spending.

AI’s big spenders, meanwhile, make up nearly half of the stock market. 

There’s more AI spending we don’t even know about.

The biggest tech companies are forking over half a trillion additional dollars on data center leases not on their balance sheets, per Moody’s. 

At least one top CEO — JPMorgan Chase’s Jamie Dimon — says the AI buildout is worth every dollar.

Dimon, speaking at anAnthropic event this morning: “The technology is so powerful, it’s worth the trillion-dollar investment.” 

Yes, but: If AI doesn’t start showing results soon, Big Tech could slow down all that economically vital spending.

PitchBook’s Harrison Rolfes tells Axios: “The moment one of those hyperscalers doesn’t succeed … you break a link in the chain.

That could cause a market correction with “ripple effects to everyone else.”
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