James' World 2

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Nobody likes showing up to work with a hangover. But these days, you don’t need a long night of drinking to feel the effects.

Instead, you might be suffering from a meeting hangover—the lingering exhaustion, disengagement, and productivity drain that follow an unproductive meeting.

Studies show that 28% of workplace meetings leave employees feeling drained, with more than 90% of workers experiencing meeting hangovers at least occasionally. Nearly half (47%) report feeling less engaged with their work afterward, while more than half say these hangovers disrupt their workflow and productivity. 

Meetings are a double-edged sword. Despite their pitfalls, they remain the most common form of workplace communication. In fact, research suggests face-to-face meetings are more effective for idea generation and task absorption than video calls. In other words, meetings aren’t going anywhere.

But leaders can take charge—ensuring meetings are productive, efficient, and, most importantly, not hangover-inducing. Here are the strategies I use as CEO of Jotform.

Set a concise agenda

If you’ve ever walked into a grocery store for a few essentials and walked out with a cart full of snacks, you understand the power of having a clear list. The same principle applies to meetings.

At Jotform, meeting agendas are indispensable. We also believe in minimizing meetings. By preparing an agenda, you can determine if a meeting is really necessary.

If an asynchronous method—like an email, Slack message, or shared document—can achieve the same outcome faster, we opt for that instead. But when a real-time discussion is necessary, such as brainstorming solutions to an ongoing issue, a meeting is the right call.

An agenda also ensures that only the necessary people are in the room. If someone isn’t essential to the conversation, they can contribute asynchronously—perhaps by answering follow-up questions afterward.

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The US National Institutes of Health (NIH) has terminated nearly 800 research projects at a breakneck pace, wiping out significant chunks of funding to entire scientific fields, finds a Nature analysis of the unprecedented cuts.

The administration of US President Donald Trump began purging NIH-funded studies on topics that it deems problematic less than 50 days ago, continuously expanding its list to include research on topics ranging from COVID-19 to misinformation. Hundreds of the 30,000-plus scientists funded by the NIH yearly have been forced to halt their work after receiving notices that their research “no longer effectuates agency priorities”, and some have had to fire personnel or even shut down their laboratories.

To understand the extent and breadth of these actions, which have so far clawed back more than US$2.3 billion allocated to US researchers, Nature tapped into a scientist-led effort to track these cuts (see ‘How Nature analysed NIH’s grant terminations’ in supplemental info). Our analysis reveals the project topics, NIH institutes and US states affected the most.

The cancellations of projects, despite scientists scoring them highly during review, “tears the long-standing fabric of the government’s contract to pursue medical research that seeks to better the healthspan and lifespan for all Americans”, says Francis Collins, a geneticist who led the NIH, based in Bethesda, Maryland, for 12 years under 3 US presidents, including Trump.

The NIH and its parent organization, the US Department of Health and Human Services (HHS) based in Washington DC, did not respond to Nature’s queries about the terminations or scientists’ concerns about them.

Grant assessment

The NIH is by far the world’s largest public funder of biomedical research, with an annual budget of US$47 billion, paying for more than 60,000 grants. This size means that the agency’s funding is irreplaceable for science, says Shirley Tilghman, a molecular biologist and former president of Princeton University in New Jersey.

Nature’s analysis shows that, looking at just the projects terminated so far, 17% are related to COVID-19, and 29% to HIV/AIDS (see ‘Terminated grant tally’) — although this represents less than 4% of all the grants awarded to each of those topics that the agency funded in 2024. One reason for the focus of these cuts is that the Trump administration has said that the COVID-19 pandemic is over, and people in the United States have moved on from it. Another potential reason is that HIV/AIDS disproportionately affects sexual and gender minorities (LGBT+); Trump signed an executive order on his first day in office on 20 January, directing the US government to stop acknowledging the fact that a person’s gender can differ from their sex at birth.

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Artwork of K2-18b, a faraway world that may be home to life, Cambridge University

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This anxiety-induced thought played like a broken record in my head as my first leadership training event approached in my new role as a training director. Talking in front of 40 leaders, most of whom were older than my ripe old age of 30 at the time, felt like the perfect opportunity for them to see right through my lack of expertise and expose me as the fraud I was.

Years ago, while working at a regional bank, I was promoted from trainer to leading a training team in another department. My prior roles as a sales trainer and human resources consultant allowed me to build a company-wide reputation as an expert on complicated sales processes and navigating employee benefits and hiring processes.

This new role pushed me out of the payment division into the retail branch banking side. In other words, it was way out of my comfort zone. And at this first training event, I could no longer rely on my expertise to help me feel safe, trusted, or relevant. 

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What happens when a spacecraft leaves the solar system? The Voyager probes have finally crossed into interstellar space, sending back unbelievable data about what lies beyond our planetary neighborhood. From detecting mysterious plasma waves to finding unexpected solar winds, their findings have changed everything we thought we knew.

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A deep field image captured by the James Webb Space Telescope is of galaxy cluster SMACS 0723. Credit: NASA, ESA, CSA, and STScI | mash mix: Space.com Music: Tranquil Dawn by Amber Glow/courtesy of Epidemic Sound

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Imagine calling the Social Security Administration and asking, “Where is my April payment?” only to have a chatbot respond, “Canceling all future payments.” Your check has just fallen victim to “hallucination,” a phenomenon in which an automatic speech recognition system outputs text that bears little or no relation to the input.

Hallucinations are one of the many issues that plague so-called generative artificial intelligence systems like OpenAI’s ChatGPT, xAI’s Grok, Anthropic’s Claude or Meta’s Llama. These are design flaws, problems in the architecture of these systems, that make them problematic. Yet these are the same types of generative AI tools that the DOGE and the Trump administration want to use to replace, in one official’s words, “the human workforce with machines.”

This is terrifying. There is no “one weird trick” that removes experts and creates miracle machines that can do everything that humans can do, but better. The prospect of replacing federal workers who handle critical tasks, ones that could result in life-and-death scenarios for hundreds of millions of people, with automated systems that can’t even perform basic speech-to-text transcription without making up large swaths of text, is catastrophic. If these automated systems can’t even reliably parrot back the exact information that is given to them, then their outputs will be riddled with errors, leading to inappropriate and even dangerous actions. Automated systems cannot be trusted to make decisions the way that federal workers, actual people, can.

Historically, “hallucination” hasn’t been a major issue in speech recognition. That is, although earlier systems could take specific phrases and respond with transcription errors in specific phrases or misspell words, they didn’t produce large chunks of fluent and grammatically correct texts that weren’t uttered in the corresponding audio inputs. But researchers have shown that recent speech recognition systems like OpenAI’s Whisper can produce entirely fabricated transcriptions. Whisper is a model that has been integrated into some versions of ChatGPT, OpenAI’s famous chatbot.

For example, researchers from four universities analyzed short snippets of audio transcribed by Whisper, and found completely fabricated sentences, with some transcripts inventing the races of the people being spoken about, and others even attributing murder to them. In one case a recording that said, “He, the boy, was going to, I’m not sure exactly, take the umbrella” was transcribed with additions including: “He took a big piece of a cross, a teeny, small piece…. I’m sure he didn’t have a terror knife, so he killed a number of people.” In another example, “two other girls and one lady” was transcribed as “two other girls and one lady, um, which were Black.”

In the age of unbridled AI hype, with the likes of Elon Musk claiming to build a “maximally truth-seeking AI,” how did we come to have less reliable speech recognition systems than we did before? The answer is that while researchers working to improve speech recognition systems used their contextual knowledge to create models uniquely appropriate for performing that specific task, companies like OpenAI and xAI are claiming that they are building something akin to “one model for everything” that can perform many tasks, including, according to OpenAI, “tackling complex problems in science, coding, math, and similar fields.” To do this, these companies use model architectures that they believe can be used for many different tasks and train these models on vast amounts of noisy, uncurated data, instead of using system architectures and training and evaluation datasets that best fit a specific task at hand. A tool that supposedly does everything won’t be able to do it well.

The current dominant method of building tools like ChatGPT or Grok, which are advertised along the lines of “one model for everything,” uses some variation of large language models (LLMs), which are trained to predict the most likely sequences of words. Whisper simultaneously maps the input speech to text and predicts what immediately comes next, a “token” as output. A token is a basic unit of text, such as a word, number, punctuation mark, or word segment, used to analyze textual data. So, giving the system two disparate jobs to do, speech transcription and next-token prediction, in conjunction with the large, messy datasets used to train it, makes it more likely that hallucinations will happen.

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The Social Security Trust Fund could be insolvent by 2035

Social Security has been running deficits since 2021, and the losses are projected to persist indefinitely unless lawmakers intervene. The Congressional Budget Office (CBO) estimates the Social Security Trust Funds will be depleted before 2035, at which point the remaining revenue from taxes would cover just 77% of scheduled benefits.

Importantly, that does not mean Social Security is going bankrupt or that benefits will stop. Instead, it means Social Security will lose one of its three funding sources. The trust funds earn interest because assets are invested in Treasury bonds, but the interest income will stop when the trust funds are depleted. That will leave Social Security with two funding sources: (1) taxes collected on payroll and (2) taxes collected on benefits.

One the trust funds are insolvent, the CBO estimates tax revenue will cover only 77% of scheduled payments in 2035. That means Social Security benefits could automatically be cut 23% within a decade unless lawmakers find a fix for the deficit problem. But the timing and severity of the problem would change if President Trump’s tax proposals became law.

President Trump’s proposed tax cuts would further reduce Social Security revenue

As mentioned, Social Security has three funding sources: interest earned on trust fund assets (5%), taxes on benefits (4%), and taxes on payroll (91%). Trust fund insolvency would eliminate 5% of program revenue, which is about $70 billion in 2025. But changes to tax law, such as those Trump has proposed, would reduce other revenue sources.

A budget model from Ivy League business school Penn Wharton suggests ending taxes on benefits would reduce revenue by $1.5 trillion in the next decade, thereby accelerating the time to trust fund depletion by two years. Ending taxes on overtime and tips would further reduce revenue and hasten trust fund depletion by another year, according to the Committee for a Responsible Federal Budget (CRFB).

In total, President Trump’s proposal to end taxes on benefits, overtime, and tips could pull benefit cuts forward to 2032. Additionally, the CRFB estimates those tax law changes would reduce benefits by 33% by 2035, which is 10 percentage points higher than the anticipated reduction under current law.

Importantly, Congress has avoided trust fund insolvency in the past, and there’s no reason to expect a different outcome this time. That means automatic benefit cuts are unlikely. But ending taxes on benefits, overtime, and tips would still reduce Social Security revenue, which makes it more likely that the fix Congress eventually puts in place will involve benefit cuts. That could be bad news for retired workers.

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