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Ubisoft Co-Founder Claude Guillemot Dies In Plane Crash
Ubisoft Co-Founder Claude Guillemot Dies In Plane Crash
Ubisoft's co-founder Claude Guillemot dies in plane crash
Claude Guillemot, who co-founded French video-game publisher Ubisoft with his brothers in 1986, has died, the company said on Saturday.
Assassin’s Creed co-creator killed in plane crash in France
Claude Guillemot, a founder of Assassin's Creed maker Ubisoft, has died in a plane crash in western France.
Founder of Assassin's Creed maker Ubisoft killed in a plane crash in western France
Claude Guillemot, a founder of Assassin's Creed maker Ubisoft, has died in a plane crash in western France
Ubisoft Co-Founder Claude Guillemot Dies in Air Crash at Age 69
Claude Guillemot, who co-founded French video-game publisher Ubisoft Entertainment SA with his brothers in 1986, has died, according to the company. He was 69.
Ubisoft Co-Founder Dies in Tragic Plane Crash
69-year-old Ubisoft co-founder Claude Guillemot died in a passenger plane crash on June 19.
Forget prompts: 'Loop engineering' is all the rage now
Claude Code creator Boris Cherny says he doesn't "write the prompt anymore." Here's how loop engineering is changing coding.
AI optimizer beats Claude Code, Codex by 2.5x
Arbor separates strategy from execution using isolated git worktrees, so engineering teams can finally trace which optimization actually moved the needle.
Anthropic Is Bringing Together AI Design and Coding in Claude
New updates mean you should be able to go back and forth between coding and designing without interruptions.
Anthropic ships major Claude Design overhaul with design system imports, code round-trips, and a fix for its token-burning problem
Anthropic has overhauled Claude Design with brand-compliance controls, Claude Code integration, lower token usage and new enterprise app exports, positioning the AI tool as a serious platform for design-to-code workflows.
Siri AI in iOS 27: Features, Requirements, and How It Works
The biggest change in iOS 27 is Apple's total overhaul of Siri, with Siri turning into Siri AI. Apple delivered on the features originally promised at WWDC 2024 and added new capabilities that turn Siri into a viable competitor to chatbots like Claude and ChatGPT. Siri AI is available in the iOS 27 beta, but everyone will get access this fall. Below, we walk through everything new you can expect from Siri.
4 Lines You Should Include in Your Claude Skill
Without these, Claude will be confidently wrong.
Why Apple built a third-party AI system for Siri and then refused to show it at WWDC
Apple's iOS 27 beta contains an Extensions framework for Claude, Gemini, and ChatGPT inside Siri, but the feature was absent from the WWDC 2026 keynote.
An AI startup founder is trying to vibe code his own "Grand Theft Auto" game
Can Claude deliver a viable GTA ripoff before the most hyped game of all time releases this November?
This Guy Is Trying to Vibe Code GTA 6 Before Its Official Release
An AI startup founder is using Claude to make a GTA ripoff.
US Government Suspends Access To Claude Fable 5 And Mythos 5 (What It Means For Your Business)
The US government just forced Anthropic to suspend Claude Fable 5 and Mythos 5 overnight. Here is what every business owner building on AI needs to do.
Anthropic Blindsides Its Business Partners
Weeks before Anthropic in April revealed Claude Design, an AI tool for creating designs and software application prototypes, it asked firms including Figma and Canva to be “partners” of the launch announcement showcasing the tool’s capabilities. These two design firms, longtime Anthropic ...
Chatbots Keep Telling Stories About Lighthouse Keeper 'Elias Thorne'. We Might Know Why
LLMs including ChatGPT, Gemini and Claude are obsessed with telling stories about lighthouse keepers and clockmakers, and one character named 'Elias Thorne' has made his way from chatbots to Amazon books. Researchers are trying to discover why.
Microsoft AI head calls out Anthropic for acting like Claude is conscious
“This is exactly what we don’t want from AIs.”
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
Code Puppy: Walmart's secret weapon against AI lock-in
Walmart's viral Code Puppy AI tool helps avoid vendor lock-in, cut costs, and reduce dependence on Claude Code and Codex.
iOS 27: What We Know About the New Siri App
iOS 27, iPadOS 27, and macOS 27 will include a standalone Siri app for the first time, providing a dedicated space for interfacing with Siri. Siri Chatbot Apple needs a Siri app because Siri is turning into a chatbot. Siri will work like ChatGPT or Claude, able to pull information from the web to provide answers to questions. Siri will be integrated into iOS, iPadOS, and macOS at the system level, and can draw on device information.
USC countdown to kickoff: Recalling Trojans DL Claude Pelon
Today, we look at a defensive lineman who was not necessarily a household name, but made two huge plays during his two years with the Trojans
Cancel your Claude sub and get lifetime access to ChatGPT, Gemini, Claude, and more for $60
No monthly fees, and you can even send one prompt to multiple models
The $900 Billion Giant: How Anthropic Got So Big, So Fast
The artificial intelligence giant was just valued at $900 billion, surpassing OpenAI. Here are the numbers behind its rise — and headwinds it faces.
Police responded to suicide attempt at Claude Lemieux's family furniture store
Lemieux, a four-time Stanley Cup champion, died Thursday around 3 a.m. ET in Lake Park. Multiple outlets have reported that he died by suicide.
Claude Lemieux, 4-time Stanley Cup champion, dies at 60
Lemieux won titles with the Canadiens, Devils and Avalanche during his 21-season NHL career.
AI Advice From Anthropic’s Head Of U.S. Small Business
Anthropic’s Lina Ochman explains how small businesses can use Claude, AI workflows, data connectors and employee-like prompting to save time.
Analysis Of Anthropic Claude System-Prompt Instruction That Shapes The Handling Of AI Mental Health Chats
Anthropic Claude provides open access to their system-wide prompt. I analyze the portions dealing with AI mental health guidance. An AI Insider analysis and scoop.
I'm a college senior who built vintage marketplace with Claude
Hana Elster vibe-coded a business during her last college term, and she hopes it will be a successful side hustle as she moves into a corporate role.
Top ethical hacker Chompie warns AI tools could put her out of business
Chompie, one of the world's tops ethical hackers, says AI like Claude Mythos will make it harder for people like her to compete.
Hyperreal Politics: When the Simulation Becomes Too Absurd to Satirize
My dialogue with Claude continues as we reflect on civilizational decline, democratic erosion, and the capture of shared
AI Agents Plunged the Tech World Into Chaos. Here’s Exactly How That Happened
The definitive story of how Claude Code and OpenClaw kicked off computing’s biggest transformation possibly ever.
AI is making the workplace more productive
Workers are increasingly turning to ChatGPT and Claude instead of their colleagues. It's making the workplace more productive — and more isolating.
I'm a product manager who used Claude to build a postcard business
Priscilla Tina, a product manager in San Francisco, used Claude to create a postcard app. She said it's made her a fun bit of cash on the side.
AI is getting worse as Google and Anthropic nerf AI models and limit usage
Evidence is mounting that the subsidization of AI is ending in real time. Will businesses still shell out for it?
Run Your Whole Business From One Tab With Claude's New Update
Claude's new update runs payroll, month-end and campaigns inside QuickBooks, PayPal and HubSpot. Is Anthropic going after Zapier? Here's a look.
Small Business Technology News From Epicor, Google, Microsoft, Boston Dynamics And Claude
Small Business Tech News This Week: Epicor, Google, Microsoft, Boston Dynamics, Claude
Inside startups, Claude has already won the AI coding wars.
In a survey of more than two dozen startup founders and VCs, we found a growing consensus that Claude Code has become the dominant AI coding tool.
Introducing Shortcuts Playground: Create Apple Shortcuts with Claude Code or Codex
Today, I’m pleased to introduce something I’ve been working on for the past six months: Shortcuts Playground, a plugin for Claude Code and Codex that can create any shortcut for Apple’s Shortcuts app using natural language. With Shortcuts Playground, you can simply prompt Claude Code or Codex with a sentence requesting a shortcut of any
Google Antigravity 2.0 goes after Claude Code and OpenAI Codex with a full agent-first rebuild
Google has taken the IDE off Antigravity. What is left, announced at I/O 2026, is a standalone desktop app built around agents rather than a code editor with AI bolted on.
Google updates its Gemini app to take on ChatGPT and Claude
The updates signal Google’s push to turn its Gemini Gemini app into an all-purpose AI hub rather than a standalone chatbot.
KPMG integrates Claude across its core business and workforce of more than 276,000 in strategic alliance
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
This Week In Main Street Technology: Meta’s New AI Glasses, Google Introduces The Googlebook And Claude Has A Small Business Offering
Meta Ray-Bans, Google's Googlebook, Claude For Small Business and other news impacting your small business this week.
A new tool just combined ChatGPT, Claude, Gemini, and more — lifetime access is on sale for $70
A lifetime subscription with access to 20+ AI models usually costs $619
Gemini's Spark agent has leaked, and it looks like it's gunning for Claude Cowork's throne
It could clean up your messy Gmail inbox or make changes to your Google Docs.
Chartbook 449: Voldemort on Threadneedle street. The Bank of England and the "haunted house" of English politics.
Imagine the scene: In some Harry Potter version of 21st-century London, a group of deathless UK Treasury officials gathered from across the last century huddle over warm pints and glasses of Chardonnay. Around the table there are officials from the 1920s, the 1930s, the 1970s, 2022 and today. Inevitably, in May 2026, the subject of conversation is the disintegration of the current Labour government. Despite its huge majority in parliament, it has been reduced to tatters by catastrophic local election result. These exposed the fact that rather than having a government with a clear popular majority, the UK is like most other modern societies divided into 5 segments each of which commands between 26 and 16 percent of the vote. Perhaps unsurprisingly, the leadership at Nos 10 and 11 Downing Street - where the Prime Minister and the Chancellor (Treasury Secretary) work - seem paralyzed. They are caught between mounting discontent over unfairness and the cost of living, and tough fiscal strictures that push them into making unpopular spending decisions. In the London pub, the Treasury officials from across the ages, can barely hide their satisfaction at the latest display of democratic politics caught in the vice-grip of financial logic. “Remember the mur d’argent and the banker’s ramp?” “Just like yesterday!” There are smirks around the table. Then the representative of 2026 pipes up: “One thing you have to say about the current lot (referring to the latest hapless Labour government), you don’t have to teach them about the bond market!” There is a murmur of ascent from the Treasury Mandarins. The trauma of the fiscal crisis of September-October 2022 sits deep. Back then, in the final stages of Tory decay, a ‘mini budget’ that challenged fiscal orthodoxy led to a brief but fierce panic in one of the oldest government bond markets in the world (aka gilt market). Now all you have to do is to mention the “bond market” to hush debate. [ Paul Mason@paulmasonnews In the name of public service, and as ex-economics editor of BBC Newsnight, I offer to do a zoom call, tonight, with any Labour MP who wants to understand why bond markets do not "fall into line" with governments. 1/ 3:57 PM · May 12, 2026 · 466K Views 224 Replies · 674 Reposts · 3.93K Likes ](https://x.com/paulmasonnews/status/2054229532404519082?s=20) “And the funny thing is”, the young man in the huddle continues, “they don’t even want to talk about … ”. There is a sudden hush. Significant looks are exchanged around the table. About? One of the senior figures puts down his glass with a decisive “clink”. He-Who-Must-Not-Be-Named has entered their thoughts. Meanwhile, a few miles away in a grubby pub in Bloomsbury, two conspirators from the camp of Critical Macrofinance huddle over cans of low-alcohol IPA. They look at each other and blurt out loud: “The Bank!” “Everyone in the bloody Labour party is talking about the bond market. But why aren’t they even mentioning … the Bank of England!?” “Why is no one talking about Voldemort?” In Paul Mason’s blogpost, after much chest-thumping about basic fiscal logic, the basic point about the Bank of England is buried in the fine print at #3 in his list of suggestions as to what a radical Labour government might actually be able to do! [ Conflict & Democracy Down with the bond market! Down with gravity! It may seem unfair to focus on Labour MP Paula Barker for her off-the-cuff assertion that the bond market would have to “fall in line” with Andy Burnham, should he replace Keir Starmer as prime minister. But Burnham himself told the New Statesman that Labour had to “get beyond this thing of being in hock to the bond markets… Read more 2 days ago · 100 likes · 3 comments · Paul Mason ](https://htsf.substack.com/p/down-with-the-bond-market-down-with?utm_source=substack&utm_campaign=post_embed&utm_medium=web) Back in Bloomsbury they shake their heads and ask: “Didn’t we learn anything from the Eurozone crisis?” Meanwhile, as if summoned to the conversation by some higher force, the “pink pages” announce: “The days of ultra-cheap government borrowing have come to an end” and Martin “Whizzard” Wolf publishes the graph of the season Look at the UK in 2026, perilously positioned on the upper edge of the pack. No wonder no one in London needs any lessons about the “bond market”. But, look again. Imagine how they feel in Rome. What is London’s nightmare in 2026? The nightmare is that you end up like Italy in the early 2010s during the Eurozone crisis. Once comfortably situated in the middle of the pack, watching with horror as your ten-year yields explode to 7 percent and “spreads” become a national nightmare. One of the largest economies in Europe, a founding member of the euro, stigmatized. But the message of the Italian experience is ambiguous. After all, the stigma didn’t last. In 2026, Italy borrows at rates lower not only than the UK but the US. The US, which, as everyone knows, enjoys the “exorbitant privilege” of being the issuer of reserve, safe asets. What kind of magic is this? How did Italy, in the space of fifteen years, go from being good, to bad and back again? Which brings us back to our dissident friends in their Bloombury boozer and their memories of the Eurozone crisis. Italy never was Britain - with its ongoing fiscal deficits. Without interest payments, on primary balance, from 2000 to 2020 Italy ran a tight fiscal ship. Even in 2008 it barely went into deficit and when it was being put on the rack in the early 20210s its primary surplus matched that of austere Germany. So, what happened? How did it end up stigmatized? Of course Italian politics didn’t help. Berlusconi was at the helm and he had legal issues. But amongst friends in the Bloomsbury pub, Berlusconi is a sideshow, the name we whisper to each other is “Trichet”. From 2004 to 2011 the ECB was headed by Jean-Claude Trichet, ex of the French Treasury, more German than the Germans. After 2009 as the news of the Greek fiscal disaster broke, the European bond markets wento into panic mode and Trichet began a brutal game of cat and mouse. Between 2009 and 2011, Trichet intervened in the bond market to help, only when he thought he had compliance with austerity. When he thought they were uncooperative, he withdrew. In the summer of 2011, he even made direct suggestions to his Italian counterparts in Rome, as to how Italy should be governed. It was this cat and mouse game that drove the bond markets crazy and exposed Italy to a violent squeeze. When Mario Draghi took over the ECB at the end of 2011 and declared “whatever it takes”, the pressure on Italy, gradually eased. It is that starring role that has made Draghi into the towering figure in European politics that he remains today. So now you see why voices in London hush when it comes to the Bank of England. “The bond market” isn’t an irresistible objective force like the weather or an avalanche. It can appear like that. But that depends on the way it is being handled, or not handled by the central bank. Which is why the silence about the Bank of England in London right now is so significant. If you think this all sounds a bit fishy, coming from a left-liberal Keynesian like myself, well, let me introduce you to Robin Brooks, who, to put it mildly, plays on a different quidditch team to me. Here, from the position of a fiscal hawk is Robin’s celebratory account of the Bank of England’s role since 2022; In October 2022, at the height of the bond market blow-up in the UK, something incredible happened. Governor Andrew Bailey told British pension funds the Bank of England would end its support program for the country’s fragile gilt market, drawing a line under how much help the central bank was willing to give to exposed pension funds and - implicitly - the government. By the end of that week, Kwasi Kwarteng - the finance minister - had resigned. Prime Minister Liz Truss would soon follow. This episode is so incredible because it contrasts with what happened in the Euro zone earlier that year. Bond markets in Spain and Italy were under pressure amid high inflation and rising interest rates. In the middle of this, Mario Draghi - Italy’s Prime Minister - resigned, compounding pressure on bond markets of high-debt countries on the Euro periphery. Unlike the Bank of England, the ECB caved. It bought Italian and Spanish government bonds in an effort to cap yields and introduced a new tool that signaled to markets it might do such caps more frequently going forward. Faced with pressure on bond markets, the ECB capitulated to political pressure from high-debt countries like Italy and Spain. The Bank of England stood its ground and toppled the Truss government. This kind of thing has long-lasting consequences. It means markets price a higher probability of yield caps in the Euro zone, which keeps Italian and Spanish yields artificially low, while markets feel free to push up UK yields without fear of central bank intervention. As I flagged in yesterday’s post, this means you really can’t compare UK yields to those of other high-debt countries. The UK looks worse, but only because its central bank isn’t interfering with markets. There’s more to this than just that 2022 episode. The Bank of England has also been much more aggressive in its quantitative tightening (QT) than other central banks. If one motivation for quantitative easing (QE) is to lower bond yields - especially longer-dated ones - then it stands to reason QT pushes yields up. The left chart above shows a four-quarter moving average of UK government debt issuance (black line) in percent of GDP and which sectors absorb this issuance, including the Bank of England (blue bars). The right chart above compares the Bank of England’s accumulation or run-off of government bonds (blue line) with that of other central banks, where this blue line is the same as the blue bars in the left chart. The Bank of England was quicker and more aggressive than other central banks in downsizing its bond holdings, so UK yields will have risen more than elsewhere also for this reason, not just because of Governor Andrew Bailey’s remarks in October 2022. Back in October 2025, after a conversation in New York with friends in British politics, my sense of dysphoria was such that I raised the issue of the Bank of England openly in the FT Since 2008, the UK has slid into a deep economic and political predicament. Growth has stagnated to a degree far worse even than in the 1970s. The Tory leadership’s wager on the Brexit referendum spectacularly backfired, leaving the UK isolated from Europe and at the mercy of its special relationship with the US, many of whose political class regard the UK as a basket case. After years of austerity, the Labour party lurched to the left and then back to the centre. The Tory party has been eclipsed by the populist nationalism of Reform. Any British government should be worrying first and foremost about how to restart growth. Instead, Keir Starmer is beset by shades of the gilt market panic triggered by the Liz Truss government in 2022. The debt level is higher than in recent years, but there is no cause for panic. UK debt is at far from critical levels and of long duration. Whether you want to “take back control” or launch progressive economic policy, a democracy dogged by fear of bond market vigilantes is unhealthy. In a country that is a monetary sovereign, this climate points to one thing: a disconnect between the government and the central bank. Since 1997, the independence of the Bank of England has become a sacred cow. But independence is what you make of it. And in the wake of the gilt market crisis of September 2022, relations between the Bank of England and the UK government need a reset. In normal times, there is no doubt that an inflation-targeting independent central bank can serve a country well. But since 2008, the UK and other advanced economies have not had the luxury of such simple problems. To their credit, central bankers around the world have not stood on orthodoxy. After 2008, the US Federal Reserve rescued the global banking system and, through quantitative easing, it stabilised the Treasury market in the face of huge deficits. In Europe, things were trickier. Both the Bank of England under Mervyn King and Jean-Claude Trichet’s European Central Bank played cat and mouse with national governments. This was a high stakes wager that between 2010 and 2012 escalated bond market tension and triggered a disastrous turn to austerity. Shaken by the impact of those shocks, their successors, Mark Carney and Mario Draghi adopted a more protective stance (assuming you were not the government of Greece). Recall that Draghi’s legendary slogan, “Whatever it takes”, was uttered to face down the City of London’s bond vigilantes. In 2015, Draghi launched QE — financial repression by another name. In 2016, Carney used the authority of the Bank of England to stabilise markets following the vote for Brexit, an exemplary demonstration of how central banks can underpin democratic choice, even when those choices are wrong headed. All these were independent central bankers steeped in the best practice of the 1990s, who recognised that historic crises demanded radical action. It was the price surge of 2021-2023 that poisoned the atmosphere. Though the shock came mainly from the supply side, central banks ended up in the dock. Nowhere more so than in the UK. The Bank of England reacted with aggressive normalisation. Faced with a cost of living crisis, the governor called for workers to accept real wage cuts. In early 2022, as gas prices were at record levels and Russia’s army was poised to invade Ukraine, the Bank adopted a stringent programme of quantitative tightening, elevating interest rates. When the Truss government got itself in trouble, the Bank took almost a week to come to the rescue. Rather than enacting a charade of normality, it is time to recognise that in 2025 the UK faces a period of historic transition. To meet this challenge, what is needed is a new concordat with the Bank. This should not ignore inflationary pressures or the long-run trajectory of debt, but should affirm the priority of reviving investment-led growth. One could follow Donald Trump in demanding interest rate cuts. The Bank could shadow the ECB in its commitment to capping Eurozone yields. But the top priority should be to lay the ghost of September 2022. The Bank’s slowing of QT last month was a concession. An immediate and complete end would send the right signal. Such a concordat will not by itself restore sanity to politics, solve the housing problem or rebuild the NHS. But it would help to lift the fear of bond market crisis, which is paralysing the effort by democratic politicians to answer the fundamental questions facing the nation. Now, that call for a new concordat between government and Bank of Englands seems more urgent than ever. The problem is that in October 2025, Starmer still had some authority. Today the government is in tatters. And when you are the “under the harrow” - to echo a phrase of Montagu Norman’s from the 1920s - it is the worst possible time to consider institutional change. To make general arguments at a time of crisis can only seem like special pleading rather than a general argument for long-term change. The same, unfortunately, is also true for the other basic institutional reform that Britain urgently needs i.e. the abolition of the out-dated first past the post electoral system and its replacement by a moderated form of proportional representation. And thirdly - whilst we are saying the quiet parts out loud - Britain needs to reopen the question of EU membership. What the local election results of 2026 show is that this remains a key issue. And what they also show is that there is a clear majority that would favor a return. Until these basic features of Britain’s political economy are addressed the country will carry on its politics as though in a haunted house, governed by governments that lack actual majorities, conjuring up false constraints whilst ignoring the real ones. This one is for DG and MI. I love writing the newsletter. If you enjoy it too and fancy buying me a coffee once a month, you know what to do. Click below! Subscribe
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‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
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