Experts unpack when benzodiazepines help or harm, tapering myths, older-adult risks, and why Alzheimer links may be overstated.
BRAIN TRUST: CONVERSATIONS IN PSYCHOPHARMACOLOGY Series Editor Joseph F. Goldberg, MD
Joseph F. Goldberg, MD, in this installment of “Brain Trust: Conversations in Psychopharmacology,” sits down with Carl Salzman, MD, to discuss the appropriate clinical use, misuse, and safety of benzodiazepines, including special considerations for older adults and their debated association with Alzheimer disease.
Benzodiazepines have been used since the 1960s, Salzman reviewed, and largely replaced barbiturates and meprobamate-type drugs, which carried greater toxicity.Sixty years of research established that, when appropriately prescribed, benzodiazepines were safe and effective.1 Salzman highlighted survey data which indicated roughly 10% of people had taken a benzodiazepine at least once in the past 12 months, with about 5% to 6% using them regularly on prescription; unprescribed use—often for occasional sleep or travel anxiety—was common even among physicians, with no clear evidence it led to addiction.2 Regular use over a period of weeks produced physiologic dependence, but withdrawal symptoms at therapeutic doses were typically mild and resolved with a gradual taper over 1 to 2 weeks. Salzman emphasized that benzodiazepines, at anxiolytic doses, did not show dose escalation or tolerance over time.
Comparing long-term options for chronic anxiety disorders, Salzman noted, “We know that both serotonin antidepressants, the SSRIs, can be very useful for treating long-term chronic anxiety disorders, and anxiety disorders tend to be long-term and chronic,” though sexual side effects could limit acceptability. Short half-life agents such as lorazepam were preferred for acute use to avoid next-day sedation, while longer half-life agents suited chronic, once-daily dosing.
On the Beers criteria’s caution against benzodiazepine use after age 65, Salzman favored lower doses over discontinuation, noting, “Benzodiazepines, long half-life and short half-life, may increase the risk of falls. If you look at the risk of falls with antidepressants or antipsychotic drugs at therapeutic doses, the risk is greater than with benzodiazepines.” He also addressed the misconception that benzodiazepines cause Alzheimer disease, attributing early associational data to protopathic bias (anxious, early-dementia patients being more likely to receive a prescription) rather than a causal effect.3 In a controlled nursing home study Salzman conducted, discontinuing benzodiazepines improved recent recall on formal testing, but most residents still preferred remaining on the medication for its anxiolytic effect.
Dr Goldbergis a clinical professor of psychiatry at The Icahn School of Medicine at Mount Sinai in New York, NY and the immediate-past president of the American Society of Clinical Psychopharmacology.
Dr Salzman is a professor of psychiatry at Harvard Medical School.
Nvidia has been arguably the No. 1 profiteer of the AI boom, selling the picks and the shovels of the trade. But now it wants Wall Street to figure out how to keep paying for them.
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On Monday, Nvidia announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to create financing platforms intended to mobilize more than $500 billion for AI infrastructure. The money will largely come from “third-party investors,” allowing Nvidia customers to finance chips and data centers while keeping Nvidia’s own risk limited and off the balance sheet.
Details of the arrangements, like the extent of each deal, are still unknown. But analysts have been watching for a deal like this—that treats AI compute into an infrastructure asset, like a toll road or power plant—that produces cash flows and therefore can support debt. As of now, many have feared the chips instead look like a rapidly depreciating, and thus depleting, pile of graphics processors that will need more and more capital to finance.
“We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure,” Nvidia CEO Jensen Huang wrote Tuesday. In Huang’s formulation, the premise is simple: “In AI, compute is revenue.”
Underneath that transformation is a second one: who is actually paying for the AI boom.
A year ago, most of Big Tech could claim it was financing AI from its enormous cash flows, accrued from decades of executing software-level thin margins and massive profits. But now debt is taking over. Goldman Sachs estimates AI-related financing now accounts for nearly one-quarter of all gross U.S. investment-grade issuance, while AI investment itself is approaching $600 billion this year.
So Nvidia’s getting ahead of the whole debacle to find the next pool of money. The chain is straightforward. An independent financing vehicle can raise money to buy Nvidia GPUs and data-center infrastructure. An AI company then leases that compute or commits to using it, creating a stream of payments against which the vehicle can borrow. Apollo, KKR, and their peers can structure or manage that debt and place it with the enormous pools of institutional money—mostly insurance and retirement capital—that they oversee.
Bloomberg columnist Matt Levine distilled the long-term vision into three steps: Put more private investments into ordinary people’s retirement accounts, raise “a gazillion dollars” of private-credit and infrastructure funds, and use that money to build the data centers that AI will rent.
There is a reason those pools of money are attractive. Data centers are expensive, long-lived, and long-standing projects that require financing over many years. Insurers and pension funds, conveniently, have long-dated obligations—annuities that may pay for decades, or retirement benefits owed decades into the future—and therefore look for long-duration assets whose cash flows can be matched against those liabilities. Private-credit and infrastructure managers act as the middlemen, turning projects like data centers into debt those institutions can hold.
Nvidia, however, has said it’s putting something of its own behind the bet: Huang said the company may provide residual-value support of up to 25% for some projects—effectively promising some protection against the possibility that the chips backing a financing are worth much less in the future than lenders expected.
Ben Thompson, who writes the technology strategy publication Stratechery, calls that “in a certain sense, a price cut”: Nvidia is using its own profits to reduce customers’ cost of capital and make Nvidia-based data centers easier to finance.
And that is where the deal gets more interesting. The AI boom started with some of the richest corporations in history spending their own cash. Then came bonds. Now Nvidia is helping Wall Street turn compute itself into an investable asset capable of drawing on insurance floats, pension funds, and other long-duration savings.
Wall Street sees it as a positive sign. Morgan Stanley’s Joseph Moore said the arrangement alleviates concerns about circular financing because third-party investors would provide most of the capital, while Nvidia would participate only to a limited extent. Bank of America’s Vivek Arya mostly agreed and added Nvidia’s chips are unusually financeable because GPUs can be moved among operators and CUDA software can extend their own useful lives.
But Thompson’s concern is those pools of cash institutions have are fundamentally different from venture capital or tech stocks: They are designed, at least in part, to seek safety.
“It’s one thing to spend all of your free cash flow; it’s another thing to tap the debt markets,” he writes. “And, beyond that, it’s a completely new nerve-racking thing to bring safety-seeking assets to bear.”
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Brain-Computer Interfaces are rapidly advancing from science fiction to practical medical tools that enable thought-controlled devices, restore communication and mobility for people with paralysis, and improve treatment of neurological conditions, with the global market expected to reach approximately US$6.2 billion by 2030.
India has focused primarily on affordable, non-invasive wearable BCIs (such as hybrid EEG-fNIRS systems developed at IIT Delhi) for assistive communication, neurorehabilitation and cognitive enhancement, aligning research with domestic healthcare needs amid a sharply rising national burden of neurological disorders.
Translating laboratory advances into clinical products remains limited by funding, clinical-trial infrastructure and industry collaboration, while specialised governance is needed for neural privacy, data ownership and safety; a National Neurotechnology Task Force is proposed to coordinate standards, trials, regulation and ethical oversight.
The idea of controlling a computer, wheelchair, or robotic limb using only human thought has rapidly moved from the realm of science fiction to real-world medical innovation. Brain-Computer Interfaces (BCIs) are emerging as one of the most transformative technologies of the twenty-first century. These systems establish a direct communication pathway between the human brain and external devices by translating neural activity into digital commands. Their applications are already transforming healthcare by enabling individuals with paralysis to communicate, restoring mobility through robotic prosthetics, and improving treatment options for neurological conditions such as Parkinson’s disease, epilepsy, and age-related vision impairment.
The significance of BCIs continues to grow as neurological disorders become one of the world’s largest public health challenges. Today, these disorders affect more than 3.4 billion people globally, making them the leading cause of disability and poor health. At the same time, advances in artificial intelligence have dramatically improved scientists’ ability to interpret complex brain signals, accelerating the development of increasingly sophisticated neural interface technologies. Together, neuroscience and AI are driving a technological revolution that promises to redefine healthcare, rehabilitation, and even human-machine interaction.
Recognising this immense potential, governments and private companies worldwide are investing heavily in neurotechnology. China has already implanted its Beinao No. 1 brain-computer interface in patients, marking a significant milestone in clinical neurotechnology. Similarly, companies such as Synchron have demonstrated the practical capabilities of BCIs through their COMMAND trial, where participants successfully controlled Apple’s Vision Pro headset using only their brain signals. These technological breakthroughs have contributed to rapid market expansion, with the global BCI industry expected to reach approximately US$6.2 billion by 2030. India, however, has followed a different developmental path.
Rather than concentrating primarily on expensive implantable BCIs, Indian researchers have largely focused on affordable, non-invasive technologies designed to address domestic healthcare needs. Most research has centred on assistive communication systems, neurorehabilitation, and cognitive enhancement using wearable devices. This approach reflects India’s emphasis on accessible healthcare solutions while simultaneously establishing a solid academic foundation in neurotechnology research.
The growing importance of neurotechnology is particularly relevant for India because neurological disorders are becoming an increasingly significant healthcare burden. Between 1990 and 2019, neurological diseases more than doubled their contribution to India’s overall disease burden. This increase has been driven largely by an ageing population, longer life expectancy, and the rising prevalence of non-communicable diseases. Stroke continues to remain the largest neurological health challenge, while dementia cases are increasing steadily. With projections indicating that more than 230 million Indians will be over the age of 60 by 2036, the demand for rehabilitation services, assistive technologies, and long-term neurological care is expected to grow substantially. Recognising these emerging healthcare needs, India has gradually built significant expertise in neurotechnology research. Several premier institutions have made notable contributions to the field. Researchers at the Indian Institute of Technology (IIT) Delhi are developing advanced hybrid wearable BCIs that combine electroencephalography (EEG) with functional near-infrared spectroscopy (fNIRS), thereby improving the accuracy of brain signal interpretation.
Another emerging dimension of implantable BCIs is their contribution to artificial intelligence. High-quality neural recordings are becoming increasingly valuable as training data for AI systems. For example, Precision Neuroscience’s Layer 7 cortical interface is capable of collecting billions of neural data points from a single patient every minute. Such massive datasets allow researchers to develop advanced neural foundation models that continuously improve AI’s ability to interpret human intentions. These models could eventually support personalised neurotechnologies while also accelerating the development of safer, less invasive brain-computer interfaces in the future.
Although India’s research capabilities continue to expand, one of its greatest challenges lies in translating laboratory discoveries into technologies that benefit patients. Many promising research projects fail to progress beyond experimental stages due to limited funding, insufficient clinical trial infrastructure, and weak collaboration between researchers, hospitals, and industry.International collaborators also remain cautious about conducting early-stage clinical trials in India, reducing opportunities for technological advancement. Consequently, only a small proportion of academic innovations successfully evolve into commercially approved medical devices or successful biotechnology companies.
Equally important is the establishment of effective governance mechanisms. Since implantable BCIs are medical devices that directly interact with the human brain, patient safety must remain the highest priority.India already possesses strong regulatory institutions, including the Central Drugs Standard Control Organisation (CDSCO), the Indian Council of Medical Research (ICMR), the Department of Health Research (DHR), and the Bureau of Indian Standards (BIS). These organisations provide an important regulatory foundation for medical technologies. However, brain-computer interfaces raise several unique ethical and technical concerns that extend beyond conventional medical regulation. Questions relating to neural privacy, cybersecurity, informed consent, ownership of neural data, AI integration, and long-term patient monitoring require specialised expertise. Addressing these issues will demand greater coordination among regulators, neuroscientists, AI specialists, cybersecurity experts, clinicians, and ethicists to develop comprehensive governance frameworks that keep pace with technological progress.
Beyond regulation, India must adopt a broader vision for frontier innovation. Building a globally competitive neurotechnology ecosystem requires more than scientific excellence alone. It also demands institutions that encourage interdisciplinary collaboration, facilitate responsible experimentation, and provide innovators with clear pathways for product development, clinical validation, regulatory approval, and commercialisation.
One promising proposal is the creation of a National Neurotechnology Task Force. Such a body could bring together experts from neuroscience, artificial intelligence, medicine, engineering, ethics, cybersecurity, and public policy under a single institutional framework. Its responsibilities could include developing indigenous technical standards, supporting clinical trials, coordinating regulatory oversight, evaluating emerging technological risks, collaborating with AI safety initiatives, and preparing ethical guidelines for future neurotechnologies. More importantly, it would strengthen India’s institutional capacity to govern rapidly evolving technologies before regulatory challenges become overwhelming.
About Dr. Sharanpreet Kaur
Dr. Sharanpreet Kaur is an Assistant Professor of International Relations at School of Social Sciences, Guru Nanak Dev University, Amritsar (Punjab) and her thrust area of research is India’s Foreign Policy with specialisation in Indo-US Nuclear and Defence Cooperation. She is the author of the book “India’s Soft Power Diplomacy: Prospects, Challenges and Way Forward”. She is also a columnist for The Daily Guardian and has written on issues related to India’s Foreign Policy. She has also been the Subject Expert for 5 Social Impact Assessment projects for Land acquisition under Punjab Government and has contributed chapters for Reports regarding the same. She has been actively involved with the Observer Research Foundation (ORF) and Institute for Defence Studies and Analysis (IDSA) and think tanks like Centre for Civil Society and Students for Liberty. Dr. Kaur’s research and writing modules include Diplomacy, India’s Foreign Policy, Politics of South Asia, Central Asia and West Asia. She has been awarded the Young Researcher Award 2023 by Institute of Scholars (InSc), an ISO certified and registered body under Ministry of MSME and Corporate Affairs. She has also been awarded for her Contribution to Education Community by Women Leaders Forum. She has also been featured among 100 Inspiring Women 2023 by Fox Story India.
Priyanka A on Reimagining India After Modi – OpEdAugust 11, 2026″On the economic front, India must restore a radical welfare state and pursue distributive economic policies to uphold the interests…
Elon Musk and Mark Zuckerberg have muscled their way back into AI’s elite ranks, closing the gap on a new generation of Silicon Valley startups, Axios’ Madison Mills and Zachary Basu write.
Why it matters: Recent gains by tech giants SpaceX and Meta — long stuck in AI’s second tier — are putting pressure on a hierarchy dominated by OpenAI and Anthropic.
Both companies released models this week with performance and pricing that would’ve been hard to imagine from either lab a year ago.
SpaceX’s Grok 4.6 scored essentially even with OpenAI’s GPT-5.6 Sol Max and just behind Anthropic’s Fable 5 Max on the closely watched Artificial Analysis Intelligence Index.
Promising “something special,”Musk said Grok 4.7 should arrive in three to four weeks and predicted it will “exceed all current models” after additional training on a massive trove of SpaceX data.
Meta is trying to squeeze the frontier from below. Its new models deliver competitive performance at dramatically lower cost — including Muse Glimmer, an open-weight system small enough to run locally on a laptop.
Zoom in: As OpenAI and Anthropic pulled ahead, Musk and Zuckerberg answered with the full force of their empires — spending billions on talent, infrastructure and acquisitions.
Zuckerberg overhauled Meta’s AI effort after the disappointing launch of Llama 4, then invested $14.3 billion in Scale AI and brought aboard its CEO,
Musk went even further, folding xAI into SpaceX and agreeing to acquire Cursor for $60 billion — bringing massive computing power, proprietary SpaceX data and a fast-growing AI coding platform under one roof.
Between the lines: The AI race is starting to reward more than raw intelligence, as price becomes a decisive advantage for many users.
Reality check: Anthropic and OpenAI still hold pole position — with even more capable models waiting in the wings, including systems deemed too sensitive for public release.Share this story.
Graduating illiterate has become the new norm in American public schools
A teacher broke down in tears describing what he’s seeing in the classroom.
His seniors, kids about to graduate, couldn’t fill in 4 simple words on a basic assignment.
The correlation between reading levels and incarceration has been documented for decades, yet the country acts surprised by where so many of them end up.
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Zoom in: As OpenAI and Anthropic pulled ahead, Musk and Zuckerberg answered with the full force of their empires — spending billions on talent, infrastructure and acquisitions.
Reality check: Anthropic and OpenAI still hold pole position — with even more capable models waiting in the wings, including systems deemed 
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