Sometimes I would wonder if Glen Hansard really existed…. Seriously, he was such an angelic presence… not otherworldly…in fact, very present this presence… A smiling rascal if you needed him to be… this most musical and mischievous archangel of Ballymun… no airs, plenty of graces… Of course, Glen was the shining star of the Christmas Eve busk… sometimes the only one reminding the rest of us that Christmas began with a refugee born in a manger, which is hardly a step from living on the street. To be utterly yourself by being more interested in others – a clue for any of us trying out different personas. He really was who you thought he was. He could never walk by a person living rough without checking they were OK… and more than that, he worked very hard so that less people had to live on the streets in the first place. When the crowds got bigger and for safety’s sake Glen had to agree to move the busk to a stage kindly provided outside The Gaiety Theatre, he would say ‘we’ll give it our best but it’s not a busk if we’re not on the street!’ …because that’s who he was – a performer at eye level in any situation. The stage was a separation he wouldn’t recognize. Of all the grand and gigantic venues he and The Frames played, you sensed the street was his most favoured place to perform. And street people, his favourite audience. Voice of the streets. A choir of angels in one man. The rest of us blessed to stand there beside our very own and very earthed Angel Gabriel… All of us welcome in his choir, whatever shape or form. ….and I’m telling myself this morning that if he never existed, then he can never cease to exist. For me, he will always be everywhere I see a coin spinning into an open guitar case. May you rest in the peace you gave to so many. Bono
Does the rapid pace of technology’s advancement ever make you feel like a monkey trying to operate a machine? As it turns out, monkeys can relate. Wild monkeys in Costa Rica, including one named Papi, are learning to use an AI-powered device that dispenses bananas when they tap a touchscreen. It took some practice: Papi repeatedly slapped the machine before figuring out to touch the screen. The goal of the device from CapuchinAI is to study monkeys’ cognitive abilities in the wild. But we’re hoping Papi gets enough practice to start writing AI prompts soon.
“Say ahh.” Nobody likes it, but your mouth may be one of the most revealing windows into how well — and how long — you live, Axios’ Natalie Daher writes.
The big picture: Scientists are finding that gum disease may do far more than threaten your teeth, The Wall Street Journal reports (gift link).
Zoom in: Bacteria from diseased gums and the inflammation they trigger may be linked to heart disease, diabetes, cognitive decline and other conditions that shape how well we age.
Stunning stat: Nearly half of all adults 30 and older have periodontitis, a serious form of gum disease that can cause bone loss around the teeth, according to the CDC.
That share rises to nearly 60% for those 65 or older, just as the risk of other chronic conditions increases.
Between the lines: Better coordination between dentists and doctors could help identify health risks earlier and improve patient care.
But first, the health system needs to overcome the traditional divide between medicine and dentistry through more record-sharing and cross-referrals.
“We envision a future where a heart doctor asks patients if they are seeing their dentist regularly, and whether they are aware their periodontal disease could mean they have a much worse heart situation,” Wenyuan Shi, head of the American Dental Association Forsyth Institute, tells the WSJ.
Friction point: Many older Americans lack dental coverage, and Medicare doesn’t generally cover routine dental care.
What to watch: Researchers and startups are developing new ways to detect, prevent and treat gum disease.
That includes therapies that target inflammation, AI tools for reading dental X-rays, saliva tests, and sensors that monitor changes in the mouth and even connect to wearables.
A recent Journal of Periodontologyreport predicted those technologies could power better detection and treatment.
The bottom line: Brushing and flossing aren’t just saving your teeth. They’re supporting your whole body.
According to a recent New York Times analysis, “the total value of the U.S. stock market has more than doubled over the past decade to over $75 trillion, roughly two and a half times the annual output of the entire U.S. economy, itself a record ratio.” Much of that growth is driven by AI, or, more accurately, bets on future profits from companies developing and integrating AI—about half of the rise in the S&P 500 index this year was driven by AI-related stocks, the Times reported.
Given the stakes, Harvard economists Karen Dynan, PhD ’92, Douglas Elmendorf, PhD ’89, and Louise Sheiner, PhD ’93, recently sought to outline some possible scenarios for AI’s impact on the US economy, who the “winners” and “losers” would be, and, finally, how governments could mitigate negative effects through fiscal policy. Their analysis is distilled in “How Might Fiscal Policy Respond to the Rise of Artificial Intelligence?” a new working paper for the National Bureau of Economic Research. Dynan, professor of the practice in Harvard’s Department of Economics and at the Harvard Kennedy School, recently sat down with Harvard Griffin GSAS Communications to discuss the paper, its recommendations, and her hopes for the work.
I’d love it if we could begin with the four scenarios for AI that you outline in the paper. What happens to economic growth, inequality, and jobs in each?
The first thing I should emphasize is that these aren’t predictions. Nobody knows what kind of economic outcomes are going to result from the rise of AI. What we’re doing in this paper is looking at four scenarios that are among the most often discussed ways in which things could play out.
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Professor Karen Dynan, PhD ’92, served as assistant secretary for economic policy and chief economist at the US Department of the Treasury from 2014 to 2017.
The first scenario is the more optimistic, which is that AI raises productivity growth, which raises income growth, and that the income gains are broadly shared. In this case, the gains are distributed in proportion to where people started.
The second scenario assumes that the gains aren’t equally distributed—in particular, that the gains go to workers at the top of the wage distribution. So, you have faster economic growth from the increased productivity, but you also have a rise in income inequality.
The third scenario adds labor market disruption to rising income inequality. AI will cause some workers to lose their jobs, but it’s going to create new opportunities. Altogether, it’s going to create more churn in the labor market. This scenario assumes that it’s going to take some time for workers who’ve been displaced to find new jobs. So, you get some rise in the unemployment rate, and you get some workers dropping out of the labor force because they’re more discouraged as it takes longer to find a job.
The fourth scenario is the most disruptive.We have even higher productivity growth, but at the same time, we assume more displacement of workers, and in particular, we assume more permanent job loss. So, there’s a higher unemployment rate and more people dropping out of the labor force. The additional income in this scenario all goes to the owners of capital— the shareholders and other investors who own productive assets—not to workers.
The last two scenarios in particular remind me of the research your colleagues David Autor, PhD ’99, and Gordon Hansen did on the “China Shock.” Economists predicted there would be GDP growth, job growth, and new opportunities, and that the job losses relative to the size of the economy would be relatively small and manageable. What Autor and Hanson found was that those losses were deeply concentrated and had powerful effects on certain communities. Translated to our electoral system, the shock had ripple effects that people couldn’t really understand or predict.
The research around the China Shock and its effects on workers who lost jobs is important. The AI shock is unlikely to look exactly the same, as the China Shock resulted in highly localized economic devastation because it hit particular manufacturing communities very hard. Manufacturing is very geographically concentrated in our country.
AI is going to be a little bit different. It’s more likely to affect people spread across industries, occupations, and regions.Today it might be call-center workers and software coders, but tomorrow it could be administrative workers and lawyers. We may see substantial job loss, but it’s likely to be more diffuse geographically, even if it is larger on a national basis.
That said, the broader lesson from the China shock literature—and this is a theme that runs throughout our paper—is that job loss can be tremendously costly. In particular, we know that workers who lose their jobs because demand for their skills falls can face persistent earnings losses. The literature has documented effects on people’s health, the pain they are feeling, and family structure, as well as social and political dysfunction in the places where they live. We talk a lot about that in our paper because it is something that you want to consider in the policy response.
AI is going to be a little bit different [than the China shock]. It’s more likely to affect people spread across industries, occupations, and regions. Today it might be call center workers and software coders, but tomorrow it could be administrative workers and lawyers.
Technological advances usually hit low-skill workers. What’s different with AI is its ability to do work that has traditionally been thought of as high-education and high-skill. Is there any historical equivalent of that kind of impact with a new technology?
Not that I know of. A question that has been of tremendous interest to researchers is whether this will play out the same as the last big wave of technology, which benefited workers higher in the distribution but hurt those lower in the distribution. There’s been speculation that this time will be different. The literature is still very much in flux, but it suggests there could be a lot of heterogeneity—workers all over the income distribution and in many types of occupations will be affected. It’s hard to predict, which is precisely why we are looking at scenarios rather than just a single set of outcomes that reflects our view of the most likely thing to happen.
One real surprise for me reading your paper was the thought that all four scenarios you present would actually reduce the US national debt anywhere from 39 to 49 percent of GDP over 30 years. You say that not all of that will come from higher tax receipts due to economic growth. If not, what else is going on? This seems like terrific news, particularly at a time when the deficit and national debt are rapidly expanding. So, what’s the catch?
One important thing to recognize up front is that the numbers we calculated are based on the assumption that there is no change in fiscal policy in response to the rise of AI. Under current law, the faster economic growth associated with AI improves the budget situation. Debt remains high in all our scenarios, but it just doesn’t grow as much as currently projected by the Congressional Budget Office (CBO). Some of the improvement is because income is higher, meaning the government collects more taxes.
The CBO currently projects debt to rise to about 175 percent of GDP over the next three decades. Our estimates reflect how much lower that trajectory would be. In all our scenarios, debt remains above its current level of roughly 100 percent of GDP, but it rises by less than projected—ending up at roughly 135 percent in our first scenario and about 125 percent in the other three scenarios.
Another really important part of the story is where the improvement comes from. The higher tax revenues I mentioned earlier contribute a bit, but the bigger story is that, if fiscal policy is unchanged, many categories of government spending don’t automatically keep pace with a faster-growing economy. For example, Social Security benefits for people who are retired now are indexed to inflation, but GDP growth reflects productivity growth as well as inflation. So, the benefits don’t rise with the broader pace of the economy.
One catch is that history suggests policymakers don’t always allow those automatic changes to play out. Over time, they have often adjusted taxes and some categories of spending to keep them more closely aligned with the size of the economy.
People’s well-being depends not only on income but also on having a sense of purpose. If that remains true, you want to think about transition support, such as modernizing unemployment insurance, building better wage insurance programs, or improving worker training.
For the scenarios that have negative impacts—ranging from increased inequality to major disruption of the labor market—what can government do through fiscal policy to mitigate that impact?
We offer a menu of items rather than a particular package. Even in the optimistic scenario where the rising tide lifts all boats, we may decide we don’t want the government to shrink as a share of the economy—we might decide that our richer society deserves better roads or expanded access to education. If inequality grows, we will need to discuss whether we want a more progressive system of taxation.
Labor market displacement is another big area. Work is not just a way of bringing in income; it seems to have independent value to people. People’s well-being depends not only on income but also on having a sense of purpose. If that remains true, you want to think about transition support, such as modernizing unemployment insurance, building better wage insurance programs, or improving worker training. Finally, if income goes mostly to the owners of capital, the government could look at ways to own a piece of these companies—either directly or by setting up individual investment accounts.
Finally, if you were the chair of the White House Council of Economic Advisors, what would you urge leaders in Washington to do right now in anticipation of AI’s impact, and what is actually feasible in our political environment?
We want them to think about what types of policy would provide insurance against the more disruptive outcomes AI could bring. One example is strengthening and refining programs already in place to help workers transition to new jobs, such as modernizing the unemployment system and putting in place training programs backed by evidence. If bad outcomes occur, these programs will be there and can expand. Similarly, we could lay the groundwork for broader public ownership of capital that benefits from AI, so the public could share more directly in the gains if that became desirable.
As for the politics, preparing for uncertainty doesn’t require agreement about exactly what AI will do or how big a government you want. People with very different views can still agree that it’s prudent to improve our ability to help workers adapt if disruption occurs. I think that’s a much easier conversation than trying to tailor a set of policies based on predictions about what exactly is going to happen.
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“1000% Going to Go Bankrupt.” Elon Musk warns the U.S. will likely fail as a county. During an appearance on the Dwarkesh Podcast, tech billionaire Elon Musk issued a stark warning regarding the financial trajectory of the United States.
Musk stated that the nation is “1000% going to go bankrupt as a country and fail as a country.” He argued that this will be the future of the country, unless advanced technologies, specifically artificial intelligence (AI) and robotics, are aggressively adopted.
Highlighting that federal interest payments on the national debt have reached approximately $1 trillion annually—surpassing the defense budget—Musk argued that conventional fiscal policies are insufficient. He noted that his involvement with the Department of Government Efficiency (DOGE) was intended to temporarily curb federal spending to allow sufficient time for AI and robotic automation to scale economic productivity and resolve the debt crisis.
Musk further projected that the mass adoption of AI and automation will dramatically expand the supply of goods and services, ultimately driving prices down and inducing structural deflation rather than inflation. However, this technological push presents broader monetary implications.
Analysts note that massive government spending required to fund the global AI race could lead to fiat currency debasement through increased money printing. In response to concerns over fiat devaluation, Musk has repeatedly advocated for fixed-supply digital assets like Bitcoin (BTC), arguing that unlike fiat money, cryptocurrency supply is constrained by energy computational costs and cannot be artificially inflated.
References Patel, D. (Host). (2026, February 5). Elon Musk — “In 36 months, the cheapest place to put AI will be space” [Audio podcast episode]. In Dwarkesh Podcast. TheStreet Roundtable. (2026, February 8). Elon Musk says that without AI and robotics technology, the US is 1000% certain to go bankrupt. Bitget News / TheStreet.
For chronically online youth, AI has evolved beyond a tool for homework and creative exploration. Increasingly, young people are turning to chatbots for emotional support.
A new report from Hopelab and the Center for Digital Thriving found that teenagers are often turning to chatbots to discuss emotional topics when they feel unable or unwilling to rely on the people around them for companionship.
The researchers interviewed 30 teenagers and young adults ages 14 to 22, including LGBTQ+ folks and people of color, about their experiences using generative AI tools like ChatGPT, Claude, and Gemini. Participants included both frequent AI users and young people opposed to using chatbots for emotional support.
Researchers found that those who did turn to AI for support feel more understood, validated, and taken seriously by AI than the people around them. Six central themes emerged from the research:
They don’t want to burden friends and family with their struggles.Chatbots won’t judge them or use their secrets against them. AI responds as though it understands experiences others may not relate to. AI is available whenever they need it. Some see chatbots as neutral third parties, even while recognizing they can pander to users. AI offers concrete, step-by-step guidance for navigating difficult emotions.
The findings add to a growing body of research showing how young people are navigating their social and emotional lives with AI. A Pew Research Center survey found that 12% of US teenagers have used generative AI for emotional support or advice, while research from Surgo Health and The Jed Foundation found that about one in eight young people who reported mental health struggles had discussed those concerns with chatbots.
The report raises broader questions about how AI could shape young people’s relationships and emotional development. It’s still unclear what, exactly, the risks may be. But rather than focusing solely on AI’s potential risks, the report’s authors argue that it’s crucial to understand what unmet needs are driving young people toward chatbots. AI has the potential to complement, rather than compete with, human relationships, the researchers claim.
“As we navigate the rapidly changing landscape of AI and set norms for safer use, young people’s experiences and motivations must be central,” the researchers wrote. “If we focus only on limiting risks without understanding why AI chatbots sometimes feel safer, kinder, or more competent than the humans in their lives, we risk pushing young people away from AI without offering better human alternatives.”
The line between human and machine interactions is starting to blur as AI increasingly creeps into users’ personal lives. Some are using chatbots to text, flirt, and navigate difficult conversations. Others are developing friendships and romantic relationships with AI companions. Young people who grew up with unfettered access to the internet are susceptible to engaging with AI with this level of depth. Researchers are still trying to understand what that shift means. One study found that young people who report loneliness and difficulty making friends are more likely to use AI for social and emotional support. As emotionally responsive AI becomes more common, researchers are increasingly asking how those interactions could shape expectations around friendship and intimacy. Those questions will only become more urgent as AI grows increasingly humanlike. OpenAI, for example, recently updated its Advanced Voice Mode to stutter, pause, and listen more closely, making conversations feel more natural. As AI are trained to be more emotionally attuned, understanding how those interactions shape human relationships may become just as important as understanding the technology itself.
Elon Musk may be the most powerful man in the world. He’s worth almost $1trn, runs Tesla and SpaceX, boasts an enormous social-media following and, to top it all off, he reckons he’s pretty good at predicting the future, too. In his first extended interview since SpaceX’s blockbuster IPO, Elon Musk sat down with Zanny Minton Beddoes, The Economist’s editor-in-chief, at his Texas Gigafactory to talk about what he thinks this future holds. They discuss the timeline for AI overtaking human intelligence, the rapid progress of China, the reasons for his interest in Europe and why he thinks he “got a little too involved” in politics. #ai#elonmusk#spacex#tesla
00:00 AI will be smarter than humans in five years 12:10 How AI companies could regulate themselves 15:20 Will China be the leader in AI? 25:43 Elon Musk on Sam Altman and the AI bosses 31:20 “Work is going to be optional” 41:07 Should one man have so much power? 49:44 Starlink and the war in Ukraine 55:18 “I got too involved in politics” 57:14 Musk: zero people died because of USAID cuts 01:01:15 Europe, the far right and civil war in Britain 01:08:21 Musk: I’m not a racist 01:20:27 What do people get most wrong about Musk?
Summary: Researchers have developed a energy-efficient artificial intelligence model inspired by hippocampal brain mechanisms. The system mimics how the human brain constructs cognitive maps, performs stochastic computations, and utilizes compositional coding to solve complex planning tasks without exhaustive calculations.
Published as a proof-of-concept for alternative AI architectures, the model flexible reacts to novel environments without requiring retraining, consuming a fraction of the power required by conventional multi-layer neural networks or large language models. The technology offers a viable framework for local deployment in autonomous robotics, edge computing devices, and vehicles operating under strict energy constraints.
Key Facts
Biological Energy Efficiency: The human brain operates complex planning and cognitive tasks on approximately 20 watts of power, contrasting sharply with the megawatt-scale power demands of modern large language models and deep neural networks.
Hippocampal Architectural Principles:The model translates three specific neural mechanisms into algorithmic code: geometric cognitive maps, stochastic scenario generation, and compositional reusability of action components.
Non-Exhaustive Search Strategy: Guided by cognitive maps, the algorithm evaluates randomly generated intermediate steps toward a goal rather than computing full solution paths, significantly reducing computational overhead.
Zero-Shot Adaptability: The system adjusts to structural environmental changes or novel tasks dynamically without requiring retraining or parameter optimization.
Validation Tasks: The brain-inspired architecture was successfully validated across three experimental benchmarks: 2D spatial navigation, abstract multidimensional orientation, and building-block silhouette assembly and disassembly.
Source: Graz University of Technology
The capabilities of large AI systems are constantly improving, but they consume a great deal of energy during training and operation. The human brain, by contrast, is extremely energy-efficient: it requires only around 20 watts.
Researchers at Graz University of Technology, in collaboration with international partners, have developed a novel, brain-inspired AI model that can plan flexibly and solve complex problems. In doing so, it consumes significantly less energy than multi-layer neural networks or large language models.
A brain-inspired AI model that utilizes hippocampal cognitive mapping and stochastic neural computation to solve complex tasks with low energy consumption. Credit: Neuroscience News
“The brain works in a completely different way to today’s AI systems,” says Wolfgang Maass from the Institute of Machine Learning and Neural Computation at Graz University of Technology. “We are trying to translate the way it works into algorithms and apply them to AI systems.”
Three mechanisms of the brain
Inspired by neuroscientific studies of the hippocampus, Wolfgang Maass and his colleague Yukun Yang have identified three mechanisms that the human brain employs in planning and problem-solving:
the creation of cognitive maps, i.e. the transformation of relations between abstract objects into geometric relations between neural codes in the brain, that provides a “sense of direction” like a spatial map;
stochastic neural computations, i.e. the ongoing generation of hypothetical scenarios and predictions;
and compositional coding, i.e. the breakdown of information and action plans into reusable components.
The translation of these mechanisms into algorithms enables the Graz-based AI model – much like humans or animals – to conceive of and test possible approaches to solving complex problems without having to completely calculate them all the way to a solution.
If a randomly selected intermediate step points towards the intended goal – here, the cognitive map guides the system – this path is pursued. At its new position, the system again evaluates various options for action, thereby gradually getting closer to the goal.
“With this approach, our AI model can also react flexibly to changed or new situations without needing to be retrained,” says Yukun Yang.
Assembling and disassembling a silhouette
The researchers successfully tested the capabilities of their brain-inspired AI using three tasks: navigating a two-dimensional space, orienting itself in an abstract, multi-dimensional space, and assembling and disassembling a silhouette made up of various building blocks.
The researchers emphasise that their approach is not intended to rival today’s large language models, but provides the basis for an alternative approach for certain applications. “We are still at a relatively early stage of development,” says Wolfgang Maass. “But our work shows that powerful AI does not necessarily require huge data centres and enormous amounts of energy.”
Suitable for robots and edge devices
In the long term, such brain-inspired systems could be used in robots, autonomous vehicles or other edge devices – in other words, wherever AI needs to operate locally with limited energy supply.
Wolfgang Maass works as a key researcher at the Bilateral AI Cluster of Excellence. The current study was conducted in collaboration with Tsinghua University and the National Research Council of Italy.
Key Questions Answered:
Q: How do cognitive maps help the AI model save operational energy?
A: Standard AI systems often calculate every potential action sequence to completion to find an optimal solution, which requires vast memory and processing power. Cognitive maps transform abstract relationships into geometric coordinates, allowing the model to instantly assess whether a randomly generated step points toward the goal, discarding unpromising paths before spending energy calculating them fully.
Q: What is the role of stochastic neural computation in this architecture?
A: Stochastic computation introduces controlled randomness to generate hypothetical scenarios and predictions on the fly. Rather than running deterministic, brute-force searches, the system samples probabilistic paths, mimicking how biological neurons use noise and variability to rapidly sample potential solutions.
Q: Where are these brain-inspired AI models intended to be deployed?
A: While not designed to replace large language models in centralized data centers, this light-footprint architecture is optimized for edge devices, autonomous vehicles, and mobile robotics. These real-world applications require real-time adaptation, complex path planning, and low latency under strict battery and thermal limits.
Editorial Notes:
This article was edited by a Neuroscience News editor.
Jaw-dropping analysis here from Nick Delehanty on accommodating asylum seekers, which is set to cost us €1.5bn this year. Deloitte, the auditors? €6m for the D Hotel in Drogheda for six months? Wasn’t that supposed to be returned to normal commercial use after two years in March 2026? Has it been extended? So many unlimited companies (which don’t file public financial accounts), and so many non-standard corporate structures. Where is our mainstream media on this?
Zoom in: Bacteria from diseased gums and the inflammation they trigger 
Between the lines: Better coordination between dentists and doctors could help identify health risks earlier and improve patient care.
Friction point: Many older Americans lack dental coverage, and Medicare doesn’t 
What to watch: Researchers and startups are developing new ways to detect, prevent and treat gum disease.
canisgallicus 