Vapes Act 2026 , retailers are prohibited from selling tobacco products to anyone born after January 1, 2009, in perpetuity. It doesn’t matter when those people turn 18—or 38 or 68, for that matter. It will always be illegal to sell to anyone born after that date. This is what’s described as an “endgame” approach. While many tobacco control strategies—such as taxation or gory imagery—aim to reduce consumption, policies like the UK’s are designed to eliminate it entirely . It’s a new approach, and no one knows whether it will work. The Maldives was the first country to implement a generational smoking ban, in November last year. It’s too soon to say how that has panned out. Nor do we know if these laws will even last. In 2022, New Zealand passed a similar generational sales ban as part of a broader anti-smoking law. But it was never enacted—the law was repealed by a new government in February 2024. In the UK, both major parties support the ban. But Nigel Farage, whose right-wing party has seen a recent surge in support, has promised that “the generational smoking ban will not last long if Reform gets the chance to start rebuilding our mismanaged country.” Chris Bostic, an attorney and former policy director for the advocacy group Action on Smoking and Health, says he and his colleagues began promoting the idea of a generational ban in the United States 11 years ago. Back then, they struggled to win support, even from major health charities. “People said we were crazy … [and] that this was impossible,” he says. Opponents argued that bans would infringe on personal freedoms. “The public health argument is: Well, what about freedom from addiction?” says Britta Matthes, a tobacco control researcher at the University of Bath in the UK. Most people who smoke began when they were teenagers, want to quit , and wish they’d never started . Tobacco is arguably the most harmful consumer product of all time. It will kill half its users who don’t quit, according to the World Health Organization. It also kills people who don’t smoke. Of the 7 million who die from tobacco every year, 1.6 million are nonsmokers who were exposed to secondhand smoke, according to the WHO. Generational sales bans are a long-term strategy that will only protect future smokers. Most experts agree that people who already smoke should be a main consideration for any policy, and that a multipronged approach is probably the best way to go. Janet Hoek at the University of Otago, who has explored tobacco control policies in New Zealand, believes that enforcing very low limits on nicotine levels and banning filters—an environmental scourge that does not make smoking safer, as many people believe—might be a “powerful combination,” for example. But preventing teenagers from starting to smoke in the first place is an enticing prospect, even among the majority of people who smoke . And it’s starting to look a lot less radical. The US has quietly been making progress on a smaller scale. Since 2021, Brookline, a town in the Boston area, has banned the sale of tobacco products to anyone born after January 1, 2000. The idea has spread. Today there are 23 towns in Massachusetts with similar bans, says Bostic. Nine towns across Minnesota, New York, and California have implemented other endgame policies. The UK law has normalized the idea more than ever, he adds. His colleagues are already fielding calls from health agencies around the world. “People [are] saying, Wow I can’t believe the UK

just did this—can we do this here? ” he says. Norms change. Like many other millennials, I vividly remember my first night out after a ban on indoor smoking took effect. My clothes didn’t stink! My hair still felt clean! And my throat wasn’t scratchy the next morning! Now that’s just normal. I hope a tobacco-free world can be the new normal for my kids. The country wants to become a global leader in brain implants. Strong government support is expected to help accelerate that process. Researchers are decoding how signals move between body and brain, with implications for how we understand and treat conditions from obesity to anxiety. The next step in the youth quest is a technology called chemical reprogramming. In an early step towards artificial wombs, a biotech company claims it’s developed a “fully artificial” chicken egg. Discover special offers, top stories, upcoming events, and more. Privacy Policy Thank you for submitting your email! It looks like something went wrong. We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@ with a list of newsletters you’d like to receive.

Godot says bye bye AI, bans vibe-coded contributions

'We can’t trust heavy users of AI to understand their code enough to fix it,' say maintainers who previously called the flood of vibe-coded pull requests 'demoralizing' Vibe coders apparently don't understand what their AI servants write - at least that’s what the team behind open-source game engine Godot seems to be implying with a new policy that cracks down on AI-generated contributions. The Godot team announced on Tuesday that they were in the process of rewriting their contribution policy to prohibit almost all use of AI from contributors, citing an overwhelming number of pull requests that have poured in, many of which appear to be AI-generated. Nor, the maintainers suggested, can many heavy AI users be relied on to respond meaningfully to review feedback. “AI cannot take responsibility, and we ca

n’t trust heavy users of AI to understand their code enough to fix it,” the Godot maintainers said in their announcement. The maintainers described AI pull requests as “demoralizing” for the Godot team, echoing comments made earlier this year when maintainer Rémi Verschelde said AI pull requests were increasingly draining and demoralizing wastes of time. AI PRs, one game studio that uses Godot noted earlier this year, are largely garbage, come from users who don’t understand what they’re proposing, and are largely “a total shitshow.” The Godot team said that it now recognizes the problem isn’t going away, so it’s time to do something about it. “Accordingly, we are in the process of updating our contribution policies, including adding a stricter policy on AI contributions,” they said. For starters, new contributors (defined as anyone with three or fewer merged pull requests) will be required to get explicit permission from maintainers if they want to submit new features or significant refactoring to Godot's codebase. This, the team said, is a bid to exclude vibe coders and AI agents and nurture a group of contributors who understand the Godot codebase and are willing to communicate with the team to learn more about it. On that note, contribution discussions will be required to remain human-to-human, too: No AI agents or bots clogging up the comms channels, unless they're being used to translate between languages. “We need to ensure that people who choose to review PRs feel their time is well spent,” the Godot maintainers explained. As for AI code itself, any autonomous agent-authored contributions or vibe-coded garbage will continue to result in an auto-ban from the team’s GitHub repo, and the team is extending that ban on AI code to include a prohibition on the use of AI to generate any substantial piece of code. “AI assistance should be limited to menial things (like code completion, regex, or find and replace),” the team explained. “If you do use AI in some capacity to author code, you must disclose it in the PR discussion.” The policy has yet to be formally amended, and the Godot maintainers didn’t say when exactly they’d release the update. Needless to say, vibe coders and AI agents aren’t welcome even now, so don’t push your luck. Vibe coding has shown other signs of falling out of favor lately as horror stories about deleted databases and wiped drives continue to pile up. Just last week, the chairman of IT consulting service Infosys predicted that vibe coding wasn’t something professionals should be worried about as there’s more to writing good software than coding. “Given that AI is a much larger and disruptive technology transition than ever before, the questions are louder and the doubts are more insistent,” Nandan Nilekani said in a speech at the company’s AGM last week. “While we will embrace the best coding tools and improve our productivity, there is much more to do in the software development life cycle.” Context, Nilekani said, is paramount in software development. Based on the vibe-coded disaster that Godot has dealt with, AI doesn’t seem quite capable of grasping that important element. ®

How to Build an AI Chat Endpoint in with the Telnyx AI Assistants API

Most "add an AI assistant to my app" tutorials stop at the demo. They show you how to call an LLM and print the response, then leave the...

in 2026 - 2027. What did you expect the bots-finally-beat-humans-at-predicting-the-future moment to look like? Vibes? Papers? Essays? In retrospect, sure: it will look like AIs making crazy profits on prediction markets and beating the stock market by some comfortable amount. But what happens next? Before getting into details, what exactly are we talking about? An AI superforecaster is an AI - usually a frontier model like ChatGPT or Claude - which has been modified to be good at forecasting. This usually means a “scaffold” - a program that handholds it through a long research process with various prompts, tools, advice about when to create subagents, etc. The overall experience is a lot like using any other AI, but slower and more expensive, because it’s doing more work. This might make more sense with an example. FutureSearch - the company that claims to be beating the stock market - kindly offered to let me try their AI superforecaster and write about it here. For a test question - some Silicon Valley philanthropists recently started a project to end respiratory infections like the common cold . I decided to ask about their chances of success. Since forecasters need very precise questions, I asked how likely it was that the rate of colds would be cut in half by 2040: By two minutes in, the AI had deployed three subagents, read 16 websites, and (at the exact moment I took this screenshot) was “investigating the scalability of ASHRAE Standard 241 air cleaning technology for widespread residential adoption by 2040.” After five minutes, it had its answer: the chance of US respiratory infections halving by 2040 was 7% : The agents’ reasoning centers on a tough conjunctive chain of requirements that all must succeed simultaneously: 1: The biology is brutal. 200+ cold-causing viruses, 150+ rhinovirus serotypes alone — 50 years of vaccine efforts have failed, and experts as recently as late 2025 didn’t expect a cold vaccine within 5 years. During COVID lockdowns with unprecedented behavioral changes, rhinovirus incidence dropped only modestly and rebounded immediately. 2: Intercept’s own timeline is tight. Their roadmap projects commercialization 5–7+ years out, leaving little room to clear Phase 3 trials, FDA approval, and achieve mass deployment by 2040 — all for under $500M (likely far too little for that scope). 3: Adoption is a wildcard. For a mild illness, compliance with regular nasal sprays or prophylactics is expected to be low. Air-cleaning infrastructure faces steep retrofit costs and a mixed evidence base ( a 2025 RCT of germicidal UV found no significant reduction in infections). 4: Measurement may be impossible. The US lacks routine population-wide cold surveillance, and the 2020–2026 baseline is distorted by pandemic-era behavior — making a verified >50% reduction hard to demonstrate even if real progress occurs. The initiative is genuinely novel and credible, but halving cold frequency in 14 years without transformative AI acceleration is an extraordinarily high bar. You could follow up by forecasting a softer threshold (e.g., 25% reduction by 2035), or by ranking the specific Intercept technical approaches (nasal sprays vs. vaccines vs. air cleaning) by their likelihood of meaningful impact. …and, in case I wanted to learn more, cited its 212 sources. The forecast had taken five minutes and cost me $8 in credits. But is it true? Ideally we would wait until 2040 and see. For now, I started by comparing its answer to another

superforecaster AI. Preseen is the company that claimed to 100,000x their seed money on Kalshi. Here’s their answer : 8.8% compared to FutureSearch’s 7%, not bad! Are either of these true? I asked a human superforecaster to predict this question, to see if she got the same as the AI. She said that depending on an ambiguity in the wording, she would give it 5-10%. Again, not bad! Of course, it would be even better to do the same experiment at scale and figure out how AIs compare to humans once and for all. But measuring forecasting ability is hard. You can’t say something like “it gets 85% of questions right”, because that depends entirely on question difficulty. If the questions are things like “will the sun rise tomorrow morning”, then even a 100% hit rate is unimpressive. Instead, we can only match different forecasters against each other and determine who is better or worse. Any anchoring in an absolute space will come from the inclusion of groups whose predictive abilities we intuitively understand (eg the average member of the public, CIA analysts, etc). The forecasting website Metaculus matches AIs against humans and each other on a common metric. Here are their results over time : The Metaculus Community Prediction is a “wisdom of crowds” style aggregation of all the forecasters on Metaculus. The Metaculus Pro Forecasters are top professional superforecasters. This graph makes it look like - as of May 2026 when Gemini 3.1 was state of the art - AI was approaching the Community Prediction. This is no mean feat, but it’s still far from the professional superforecaster level. But in a recent blog post , Metaculus adds context. The graph above only measures out-of-the-box brand-name AIs like GPT and Claude. It doesn’t count forecasting-focused scaffolds like FutureSearch. A different investigation by Metaculus finds that these efforts are “worth 9 months of base model progress”, eg a well-scaffolded AI today is already as good at forecasting as base models will be in nine months. If you extend the dotted green line on the graph to July 2026, then add nine months for the extra scaffolding, it looks like the best AIs should be around 31, compared to top pro forecasters’ 36. So in theory, the absolute best forecasters in the world are still beating the top AIs, but the margin of victory is less than the graph suggests, and we should expect human-AI parity in about six months. But the claim that scaffolded AIs are nine months behind base models is itself ~9 months old. Several people in the field told me that they thought this underestimated true progress. Claims by the AI startups themselves may be treated skeptically, but even a few top human superforecasters said they were no longer confident they could beat the bots. Seems like time for a head-to-head matchup. The Metaculus Cup - the World Cup of forecasting! - is on the case. Once a season, top humans and AIs compete on about fifty questions like “Who will win the upcoming Nepali elections?” and “Will the US attack Iran?” Here are the winners of the most recent tournament: Humans took the top two spots, but Preseen’s AI came in third. Every forecasting competition involves a heavy dose of luck, so realistically at this point humans and AIs are in a statistical dead heat. We can confirm by looking at the intermediate results of the ongoing summer Metaculus Cup: Of humans who placed in the top ten during spring, 2/10 - benshindel and MarcosO - repeated their performance in summer. So

did two top-ten AIs - manticAI and Laertes (Preseen-Chestnut is having a tough summer and is down to #40). Industrial Revolution folklore tells of John Henry , the great steel-driver, who refused to accept that machines were making him obsolete. He challenged a steam drill to a competition, won by a hair, and dropped dead, symbolizing the end of human supremacy in manual labor. This is how I think of this summer’s Metaculus Cup, with Ben Shindel and MarcosO playing the role of John Henry. Humans are still holding out, but for how long? This is a forecasting question, so all the forecasting nerds at Metaculus have opinions on it. They think there’s a 15% chance that a bot will win this summer’s Metaculus Cup - the one shown above - and a 95% chance that one will win sometime before 2030. If bots aren’t soundly beating top humans, why are people able to tell me stories about their bot beating the stock market, or making millions on Kalshi? I think a combination of reasons. First, the best human superforecasters in the world probably also beat the stock market. Somebody has to, and the best human forecasters in the world seem like the sorts of people who would do this. This would also explain why big hedge funds like Bridgewater keep trying to hire superforecasters . Second, AIs are faster and more diligent than humans. Plenty of people beat prediction markets. But it might take them several hours to figure out which markets have untapped alpha, several more hours to make a model and decide who to bet on at what probability, et cetera, and then they can only put in a few thousand dollars before the inefficiency is corrected and they need to move on to something else. AIs can automate that process, betting on hundreds of markets every week. I asked the guy who turned $35 into $2 million in seven months on Kalshi whether, in another seven months, he would be able to 100,000x his money a second time to $200 billion. Unsurprisingly, he said no - there’s only so much easy money on Kalshi, and his AI had already taken it all (also, other people with similar AIs are starting to fight him for it!) Third, and most speculatively, AI may have a special advantage in finance. This is exactly the sort of well-contained data-heavy domain where machines are most likely to excel. In Metaculus’ Market Pulse competition , a purely finance-focused tournament, Preseen’s bot recently beat all humans (including Cup rival MarcosO) to take first place. (“If this is true, then why aren’t all the top trading firms rushing to switch to AI?” I don’t know the details, but Jane Street is building their own data center , I wonder what they need all that compute for?) I think the best summary of the evidence is that the best human superforecasters and the best bots are too close to clearly tell apart, but if you absolutely had to guess, the bots are very slightly better in finance, and the humans very slightly better in everything else. Suppose that AIs don’t improve any further. What would happen? Would anything happen? We already have top human superforecasters. Do bots which are just as good, but no better, add anything? Yes. Getting information out of top human superforecasters is hard. First, you need to find one. There are companies that will connect you to them, but like all companies, they charge money, take time, and are annoying to work with. Then you need to talk to them at length about exactly what you mean (do you mean the total number of colds should halve,

or the number of people who get colds in any given year?) Then you need to wait a few weeks as they research the issue and decide what they think. Then you need to convince stakeholders that the answer means something (“I got it from superforecasters! They’re people who . . . uh, can you read this Philip Tetlock book? It probably explains it better than I can.”) As a result, using superforecasters is a Big Deal. Only a few institutions do it, for a few very important questions, and it’s a news story every time it happens (remember when Google DeepMind used superforecasters to predict risks from one of their models?) If you read about a cool new charity that’s trying to end the common cold, even if you’re pretty interested in it, you’re not going to call up a team of human superforecasters and pay them tens of thousands of dollars to spend weeks researching whether it will succeed. But with AI superforecasters, you can absolutely do that as part of your normal news-reading process. AI forecasters are the same kind of advance as going from a world where writing required hiring a scribe and baking a clay tablet, to a world where writing only requires hitting the “send tweet” button. But there are three more differences that I think will start out underappreciated. First, superforecasting feels like the sort of thing AIs should be doing. One of the commonest ( and worst ) objections to forecasting is “That guy said there was an 11% chance of Smith winning the election, but that’s too precise, it sounds fake, nobody can know that!” But if an AI says there’s an 11% chance of Smith winning the election, people will eat it up. Thanks to science fiction, they already imagine AI thinking that way! And although AIs can have political biases the same as humans, “how do we know that you’re not biased against Smith?” feels like a live question for human forecasters in a way that the machines may partly escape. Second, AIs are a standardized branded product, which makes them easier to explain and to hype. Everyone knows that Nate Silver is a good forecaster; if you said “I hired Nate Silver to think this question over” then people would pay attention. But everyone can’t hire Nate - he’s one person, his time is limited - so instead you’re reduced to saying “I hired these people called superforecasters, I promise that they’re good in the same way Nate Silver is, you can, uh, read this book by Philip Tetlock if you want to learn more”. But if “the Preseen AI” gets the same reputation as Nate Silver, then the situation is rosier; everyone can use it, cite it, and trust that its opinion will carry the appropriate weight. Third, AIs aren’t trying to screw you over. Human superforecasters are mostly very nice and don’t want to screw you over either, but most people get their introduction to superforecaster-quality opinions through prediction markets - and prediction markets are definitely trying to screw you over. The really crazy stories - like people threatening journalists into covering up information which would make them lose - are thankfully pretty rare; the real threat comes from people exploiting resolution criteria that don’t match the common-sensical definition of what the market’s trying to predict. This becomes fatal in conditional markets, where there’s no way to write the resolution so that it expresses the causal statement you probably intended 1 . But AIs aren’t after your money and you don’t need to treat them like an adversary. You can

just ask “Hey, can you please predict this as if it’s the causal statement I’m intending, even though there’s no ironclad way to grade you on it after the fact?” 2 Bots are now slightly below or equal to top humans. And bots improve at 0.9 Metaculus Elo points per month. By this time next year, the trendline predicts they should be well beyond the best human forecasters. Do we believe the trendline? So far, AIs have vastly surpassed humans in a few limited domains - chess, Go, protein folding. Forecasting feels like a bigger deal - a messy human-level skill with contributions from all of our higher faculties. Indeed, Sayash Kapoor and Arvind Narayanan, in their article arguing against worrying about near-term superintelligent AI , specifically flag forecasting as a place where they expect superhuman performance to be impossible: We offer a prediction based on this view of human abilities. We think there are relatively few real-world cognitive tasks in which human limitations are so telling that AI is able to blow past human performance (as AI does in chess). In many other areas, including some that are associated with prominent hopes and fears about AI performance, we think there is a high “irreducible error”—unavoidable error due to the inherent stochasticity of the phenomenon—and human performance is essentially near that limit. Concretely, we propose two such areas: forecasting and persuasion. We predict that AI will not be able to meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools) at forecasting geopolitical events (say elections). We make the same prediction for the task of persuading people to act against their own self-interest. I generally disagree with Sayash and Arvind, but this is the prediction of theirs that I’ve thought about the longest, without being able to find any decisive refutation. It’s a great test case! If AI hits top-human level forecasting and then flattens off, maybe there’s something special about the human level, and S&A will also be right about superpersuasion, super-research, etc (at least for the near-term). If it keeps going, reaching heights far beyond the human maximum, then we should be concerned that it will do the same thing in other skills too. We’ll start to have a good idea which world we’re in within a year; after two years, the answer should be decisive. If the trendline does keep going, things start changing quickly. Finance gets transformed first, as human stock analysts go the way of horse-drawn carriages and kerosene lamps. The opportunity for smart humans to consistently make money on prediction markets likewise dries up - instead, bots duel other bots for the privilege of collecting money from dumb sports fans. Savvy institutions will cede some of their strategic thinking to AI. Before starting a new project line, smart businesses will ask the superforecaster AIs how much money it will make (their investors will definitely be asking!) Smart political consultants will ask the superforecaster AIs about their candidates’ chance of winning conditional on running this or that ad. The government isn’t usually classified as a savvy institution, but we might hope that parts of it will seek AI forecaster advice. Probably defense analysts will include in their PowerPoint presentations some fact about how AI forecasters say their new fighter jet design is more likely to find a use case than some competing fighter jet design.

But the dream is that, armed with AI superforecasters, the public and the politicians who they elect will make better decisions about policy. Dare we hope for this? The argument against: there are many policies now which no expert - no smart person who has considered the matter honestly - really supports, but which happen anyway. Why should adding one more smart expert to the opposition change anything, just because that smart expert is an AI superforecaster? Here our analysis devolves into questions about human psychology - will people who “get to know” an AI superforecaster (the way many people currently “know” Claude or ChatGPT) believe it more than they believe random experts? Does something about it being a machine save it from charges of bias? The optimistic argument runs less through an immediate direct effect, and more through longer-term trends about the role of human judgment. When experts made a few really bad calls in the late 2010s and early 2020s, it changed the way people related to expertise as a concept (for the worse). If AIs can ma

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