AI News

I'm going to try to explain this crazy viral story, because I'm not sure most people actually understand it: Claude seems to have an inner monologue. It thinks stuff without telling us, and that may change how we see AI. Here's what's happening: Here's what Anthropic researchers have learned... DUDE - NOBODY BUILT IT. WUT? First, there's a small internal workspace inside Claude where it holds and moves concepts around without ever writing them down. They call it J-space. Nobody designed it. It emerged on its own during training. This is where it gets confusing. I had to read so many articles to really understand this, and here's how I'd describe it: WHAT WE THOUGHT WAS HAPPENING BEFORE We thought an LLM just predicted the next word, over and over. Ask it the capital of France and it says "Paris" because "Paris" is the word most likely to come next. Not because it *knows* anything about France. We also thought that when a model wrote out its reasoning, those words were just more predicted text. They looked like thinking. They weren't actually driving the answer. So the picture was: math underneath, convincing words on top, nothing in between. WHAT'S ACTUALLY HAPPENING There's something in between. Claude holds real ideas in its head while it works - ideas you never see. Researchers found those. Then proved they matter by reaching in and changing one mid-thought. The answer changed with it. So there's a step between the question and the answer. It's not just saying words that sound like thinking - it's more than that. And that hidden step is what actually decides what Claude says. THE NEUROSCIENTISTS NOTICED J-space looks a lot like global workspace theory - the leading model for how the brain broadcasts conscious thought. Deliberate reasoning routes through it, routine stuff bypasses it entirely. Anthropic makes no claims about consciousness. But Stanislas Dehaene and Lionel Naccache, who built that theory, called this a landmark finding. MY TAKE We keep saying that Claude and LLMs in general behave like humans because they kind of learn and act and 'think' like humans. And we always get pushback, but this isn't just an academic conversation. But if you want to master AI, or drive that across your company, you have to understand that implementing AI is much closer to merging with a new company - a very human problem - than it is like a software implementation. I would argue it has almost nothing in common with the latter. Which means we have to treat upskilling completely differently - because nobody upskills to deal with new colleagues, they think about how they act, every day, and how to get the best out of these new relationships. That's AI, friends. It's weird. | 60 comments on LinkedIn

Today's AI Mindset Newsletter! This is the speech I would give as a CEO to my people about AI. The first thing we do in our senior leadership workshops is messaging. Get that wrong, everything else implodes. Here's some thoughts: For the full message, and to get this stuff in your inbox, subscribe to our newsletter! Many say it's the best thing they've ever read in the history of reading anything ever. (Find it at our AIM website.) Okay, here's the TL;DR... Nobody has ever won an argument by saying "get over it." Yet that's basically the CEO message on AI: you may not love it, but this is the future, so use it. THE FEARS ARE RATIONAL Your people are not children refusing to use the new CRM. They're good at their jobs. They have muscle memory. They spent twenty years building skills that AI now threatens, along with their ability to feed their family and their kid's job prospects. Nobody here is being irrational - they're paying attention. THE MANDATE IS EMPTY ANYWAY Every tech mandate that ever worked had an old thing to kill. Salesforce killed the spreadsheet, Slack killed the fifty-person email chain, and nobody needed convincing because the alternative got taken away. AI has nothing to kill because there's no legacy system for thinking bigger. Which means your people choose, every single day, whether to open the thing. It's voluntary whether the CEO admits it or not. THE SPEECH So I wrote the speech I'd give if I ran a large company. It starts with "yeah, this is scary" and it ends with why hallucinations are your people's job security. The whole thing hinges on one move: hand people something instead of demanding something. AI training is a gift from the company to the employee, not the other way around. You stop asking for productivity and you get it anyway. WHY IT WORKS Leading with vulnerability is the most underrated unlock in AI adoption (preach, Brené Brown!). When a CEO admits they were intimidated for a year, everyone in the room is suddenly allowed to be behind - which means everyone in the room is allowed to start. AI adoption is an employee engagement strategy, not a technology rollout. It comes down to whether your people believe you're on their side. | 14 comments on LinkedIn

An extremely useful research paper investigates the relationship between human and agentic organization structures, and the optimal organizational forms. It is substantially framed around an idea very aligned with my current thinking (will share in a bit), that the core issue is "contextual transaction cost". What are the gaps and costs in transferring work between agents, human or AI? Canhui LIU ran 8,000 identical knowledge-work tasks through seven organizational forms, spanning human-imitation, hybrid, and agent-native. Agent-native forms were 395% more efficient than the human-imitation family. However notably single-expert (so zero transaction costs) did very well. Best by far: agentic meta-organization, using an orchestrator to switch between organizational forms depending on the task. Dead last (surprise, surprise): committee. The short version: there are reasons why human organizational forms work for humans, given attention, collaborative transaction costs, and relationship value. However these to not map into agentic structures. The BIG question: what are the optimal Humans + AI organizational forms. I'm doing a lot of work on this and will be sharing more in the coming weeks and months.

Very interesting data from Anthropic on global use of AI, showing dramatic differences in usage (#1 Australia) and augmentation vs automation use (#1 Norway), as well as other instructive insights. A few of the standout findings from the data that point to GenAI adoption around the world: 🌍 Global usage is spreading unevenly across countries. The top 20 countries account for 48% of all per-capita usage, up from 45% in the previous report, so the leaders are pulling further ahead. Inside the US, usage across states is evening out, but between countries the divide is slowly widening. 🏙️ Small, technologically advanced countries punch far above their weight. The US, India, Brazil, Japan and Korea lead in absolute usage. But once you adjust for working-age population, the ranking flips to Israel, Singapore and Australia. On that per-capita measure Australia now sits first of 121 countries, using Claude at 6.4x its predicted rate. Capability density beats raw scale. 🤝 Higher-income countries collaborate rather than automate. Wealthier, higher-usage countries delegate less independent decision-making to Claude, using it as an assistant with humans in the loop. Just over half of Claude.ai conversations are now augmentation rather than automation, and that collaborative tilt is strongest at the top of the income range. 🧩 Lower-adoption countries often show more complex use. Where adoption is thin, the early users are the technical elite, so their usage looks sophisticated relative to richer markets. More casual, everyday use only emerges once AI diffuses to the wider population. 📚 Purpose of use shifts along the income curve. Higher-GDP countries lean toward work and personal tasks. Lower-GDP countries lean toward education and coursework, where students are a larger share of early users. 💰 Income alone does not explain the middle. For the 37 middle-income countries where most of the world's population lives, income is a weak predictor of relative usage. Education, connectivity, language access, regulation and local competition matter more than GDP.

„Mit KI gemacht!" ist zum Vorwurf geworden – als wäre KI-Nutzung eine Frage von Ganz-oder-gar-nicht und das Resultat immer schlecht.

Does AI have values? If you believe The Economist, the answer is “yes… AI models are Godless hippies”. As more people turn to AI to help them plan, communicate and make decisions about their work and personal lives, what values these models reflect and express are incredibly important. Values will influence what advice AI models give, what news they deem worth sharing, what facts they choose to surface and how they write content. For the most part, it seems, the values these models reflect is based on the language data used to train them. For example, when The Economist asked several of the most popular AI models to complete a World Values Survey questionnaire (in English), their values aligned almost exclusively with English-speaking and wealthy countries (see graphic). Elsewhere, researchers have found that changing the language used to ask a question changes the way models respond - because it changes what training data is used to inform the response. At Common Cause Australia we spend a lot of time talking about the importance of values in messaging and other forms of community engagement because values strongly influence our attitudes and behaviours. As the use of AI explodes across all areas of life, we need to start asking some serious questions about what values AI models are reflecting and expressing as they influence our daily lives. You can read the full Economist article here: https://lnkd.in/gVGbEM6K

KI muss Probleme lösen und einen Mehrwert haben, technisch umsetzbar sein und auch einen Bedarf treffen.

Using data from more than 72,000 Word users, researchers compared sustained Copilot adopters with similar coworkers who adopted later, and found the pace of work in Word changed markedly after adoption. The study introduces a new way to measure AI impact as products and people evolve together.

🎙️ "If you want people to feel like they will lose their job to AI, or can be easily replaced by AI, then put it on the org chart." This really resonates... A study by Boston Consulting Group (BCG) challenges a worrying trend gaining traction in many organisations: treating AI agents like employees. The research, by Matthew Kropp, Julie Bedard, Emma Wiles, Megan and Lisa Krayer, PhD, finds that anthropomorphising AI may make systems feel more familiar, but it also creates unintended consequences. When AI is framed as a teammate rather than a tool, accountability becomes blurred, review quality declines, escalation increases, and trust and professional identity suffer. Managers exposed to “AI employee” framing caught 18% fewer errors and were significantly more likely to defer decisions upwards. The authors argue that the real opportunity lies not in putting agents on the org chart, but in redesigning work for responsible human-AI collaboration. That means redefining workflows and human roles, making accountability explicit, building managerial capability to oversee human-agent systems, resisting one-to-one “digital employee” thinking, and deliberately redesigning work around judgment, creativity, and ownership. The organisations that create value will be those that redesign operating models and workforce capabilities together. 🔗 The article is featured in the May edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/enxVdRH9 🔗 | 13 comments on LinkedIn

Diese Studie zeigt präzise, wie nicht-resilienter Umgang mit KI aussieht…

🤖 AI is ready but most workforces aren’t.

AI adoption is slow because leaders can't mandate it in the same way they mandate Excel for their accountants. Because AI doesn't replace a tool - it replaces a way of thinking. Which means adoption is much closer to merging with a new company than rolling out a new software...which is why enterprise wide adoption has to live under Talent/HR. Consider what you do when you roll out a new CRM system. You train everyone up, you'll have your complainers that prefer the old system, then you finally sunset the former system and bam, everyone is now using the new CRM. That's mandating a tool. If you want to work at this company, you have to use this tool. That's also why we refer to getting good with technology as getting "proficient." But notice that Anthropic and others have stopped using the word proficient. They now use the word "behavior." All this is to say: If you're a leader trying to drive transformation, you can't treat it like something you can just teach with use cases, features, prompting. It won't work - we've all seen that. We have to get people thinking differently with it, working with it like the new set of teammates that have just joined from that digital alien company. That's why HR and Talent have to lead the way (outside product development). They understand culture, learning, and change. | 55 comments on LinkedIn

"𝘞𝘦'𝘳𝘦 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘈𝘐." Every deck says it. Every all-hands repeats it. Then the same people get promoted for the same things they were promoted for ten years ago. This is what I call 𝗩𝗮𝗹𝘂𝗲–𝗥𝗲𝘄𝗮𝗿𝗱 𝗗𝗶𝘀𝘀𝗼𝗻𝗮𝗻𝗰𝗲. The behaviours that create value have changed. The behaviours that get rewarded have not. AI is changing where value is created. As execution becomes easier, value shifts towards judgment, creativity, integration, orchestration and decision-making. Yet many organisations still reward:   • Volume over value   • Answers over questions   • Presence over impact   • Speed over judgment   • Activity over outcomes Then we wonder why behaviour does not change. Here is the part most transformations miss. Using AI is not the same as creating value with AI. The value comes from people who challenge outputs, connect expertise, integrate perspectives, orchestrate work and make the call. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗿𝘂𝗻𝘀 𝗱𝗲𝗲𝗽𝗲𝗿 𝘁𝗵𝗮𝗻 𝗳𝗼𝗿𝗺𝗮𝗹 𝗶𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲𝘀. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗽𝗮𝘆 𝗳𝗮𝗿 𝗹𝗲𝘀𝘀 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝘁𝗼 𝘄𝗵𝗮𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗮𝘆 𝘁𝗵𝗮𝗻 𝘁𝗼 𝘄𝗵𝗼 𝗴𝗲𝘁𝘀 𝗮𝗵𝗲𝗮𝗱. If an organisation claims to value curiosity, collaboration, integration and human–AI orchestration, then the people being promoted need to embody those behaviours. 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗯𝘆 𝘃𝗮𝗹𝘂𝗲𝘀 𝗼𝗻 𝗮 𝘀𝗹𝗶𝗱𝗲. 𝗜𝘁 𝗶𝘀 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗯𝘆 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝘀𝘂𝗰𝗰𝗲𝗲𝗱. If the people getting ahead are still being rewarded for certainty, visibility and proximity to power, everyone gets the message. No matter what the strategy says. Organisations get the behaviours they reward. Cultures become what they celebrate. #humanai #workdesign #aivaluecreation

If you want to understand why AI hasn't completely transformed your organization's outputs overnight, you need to understand the "weak link" problem. Professor Chad Jones recently gave a phenomenal talk at Stanford titled "A.I. and Our Economic Future," where he highlighted how explosive economic growth is currently bottlenecked by the tasks that still require human intervention. (Based upon risk, many tasks should remain human.) A business workflow is like a chain: If you make 17 of 20 links incredibly strong with AI agents and automation, the chain's overall strength is still capped by the three weakest links. Examples of weak links include various forms of human work (review), real-world machine completion, the supply chain, and even AI overstepping its capabilities. When AI successfully automates a large portion of a role (workflows), the remaining human tasks become the scarce, highly valued weak links. Which is why I believe salaries for humans running policy on automations will rise significantly. Now, if you break just one small link (task failure), it can seriously limit or destroy an entire system. It could be catastrophic. This reality is exactly what Conrad Gottfredson and I address in our Matrix framework. The Gottfredson-Cavalier Matrix evaluates tasks across two dimensions that perfectly mirror these weak link economics: - Level of Difficulty: The vertical axis captures the upside bottleneck. The hardest tasks are the weak links that cap a role's productivity gains until they, too, can be reliably automated by a human-running policy. - Critical Impact of Failure: The horizontal axis measures the downside risk. The severity of a broken link does not scale with its difficulty; a cheap, routine task can be the exact one whose failure causes a catastrophic event. We then determine the amount of work a human and an AI contribute to task completion. This is the Human-AI Task Scale (Values 1-7). The task is now positioned on the Matrix. The future of work is not about eliminating roles, but reshaping them around human discernment and high-stakes tasks. The organizations that win the next decade will be the ones that can see the human-machine relationship clearly, measure it precisely, and govern it deliberately. Which is what we are enabling at Talent Rewire. The video link is in the comments, and the Matrix link is there as well, where you can explore the details.

"Ich brauche keine KI Architektur - xyzGPT reicht mir." Das war eine Wette darauf, dass die Preise bei Anthropic und OpenAI niedrig und politische Machtspielchen ausbleiben.

It's true: using AI can erode our cognition. But only if we let it. We can choose how we interact with AI so that it enhances our cognition, potentially to the point that we think better even after the AI is taken away. I've redone my one-page 8 levels framework as a series of compact slides for each level, as this itself helps cognition and how we absorb information. As I continually emphasize in my keynotes, executive sessions, and leadership programs, the master capability is metacognition. The necessary first step is to recognize at which level we are working. That is the unlock to change how we are using the tools so we move up the spectrum to more enabling forms of interaction. There are an array of valuable skills, techniques, and approaches that assist us at each of the higher levels. But simply beginning to be aware of how your thinking is impacted by your AI use allows you to change and improve. | 20 comments on LinkedIn

Siara KI von eEight: implizites Wissen sichern, Performance Support aufbauen, Dashboards fürs Management. Wissen rein, Performance raus.In diesem Video zeige...

Eine Autorengruppe verschiedener Hochschulen (Australien, Neuseeland, USA, China) legt ein Rahmenmodell für «AI Learning Companions» vor: KI-Begleiter, die nicht auf schnelle Aufgabenerledigung, sondern auf nachhaltiges Lernen hin entwickelt werden. Der Pre-Print-Beitrag begründet, warum LLMs in ihr

Explore how HR leaders navigate their dual mandate by guiding enterprise AI transformation while simultaneously reimagining the HR function itself.

DIE WISSENSGESELLSCHAFT AUTOMATISIERT GERADE IHRE EIGENEN LEHRJAHRE.

𝗞𝗜-𝗪𝗲𝗻𝗱𝗲 𝗮𝗺 𝗝𝗼𝗯𝗺𝗮𝗿𝗸𝘁: 𝗔𝗿𝗯𝗲𝗶𝘁 𝘄𝗮𝗻𝗱𝗲𝗿𝘁 𝘇𝘂 𝗞𝗜-𝗳𝗲𝗿𝗻𝗲𝗻 𝗧𝗮̈𝘁𝗶𝗴𝗸𝗲𝗶𝘁𝗲𝗻

🎙️"The real question isn’t whether people have the right skills. It’s whether the organization is built to unlock them" Microsoft’s 2026 Work Trend Index argues that the next competitive advantage will belong to what it calls Frontier Firms. These are organisations that move beyond AI adoption to AI absorption, redesigning work around people, agents and continuous learning. The report is structured around three powerful ideas. 1️⃣ AI lifts the ceiling on individual potential. Analysis of more than 100,000 Copilot interactions found that almost half of AI use supports higher-order cognitive work such as analysing information, solving problems and making decisions. Two-thirds of users say AI enables them to spend more time on higher-value work, while 58% report producing work they could not have done a year ago. The most advanced users - 'Frontier Professionals' - are not simply working faster. They are redesigning workflows, orchestrating agents and applying stronger judgment, critical thinking and quality control. 2️⃣ The job of every leader is to rearchitect work. This is arguably the report’s most important insight: the Transformation Paradox (see page 12). Only 19% of workers sit in the Frontier zone where individual AI capability and organisational readiness reinforce each other. Half remain in an emergent middle ground, while others are either stalled, blocked by organisational barriers, or working in companies that are ready but whose employees have yet to catch up. The challenge is not technology. It is systems design. Employees are often ready to reinvent work, but incentives, metrics and management practices continue to reward the old way of working. 3️⃣ Every firm is a Learning System. Microsoft finds that organisational factors such as culture, manager support and talent practices account for roughly twice the impact of individual effort alone. The organisations pulling ahead are those that systematically capture what employees learn, codify successful human-agent workflows and scale those practices across the enterprise. 🎬 The key takeaway Frontier Firms redesign roles and workflows, align leadership and incentives, build manager capability, define effective human-agent handoffs and establish governance that keeps human judgment at the centre. In an era where intelligence is increasingly available on demand, the organisations that learn fastest, not merely deploy fastest, will be best positioned to lead. 🔗 The study is featured in the May edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/enxVdRH9 🔗 | 16 comments on LinkedIn

Learning must be at the heart of all work. A re-analysis of human-AI collaboration research shows that if you actively enable human learning in the collaboration process, you achieve considerably better outcomes. I am continually frustrated by the research on human-AI collaboration, because much of it focuses on narrow reductionist cases that are massively over-generalized. This is exemplified by a recent meta-analysis that purported to show that human-AI synergy - as in outperforming either alone - was highly limited. However reviewing the underlying research to uncover which of the studies incorporated a learning loop so the human could learn and improve in the process (relatively few) showed that this is a key driver of synergy. I have long said there are two ways for humans and AI to work together: ➡️ ones where the task is bounded and the human is unchanged, which essentially means their capabilities erode over time ➡️ ones where the human learns and improves Every human-AI interaction must enable human learning. Building this into research - and of course all real-world work - will support superior Humans + AI value creation. Thank you for this excellent research and insight Julian Berger Jason Burton Thomas Kosch Ralf Kurvers Christopher Lazik Linda Onnasch Tobias Rieger Anna Thoma Dirk Wulff Stefan Herzog et al

Ai f

NVIDIA CEO Huang hat die ehrlichste KI-Rede aller Zeiten gehalten & die Tokenisierung der Welt beschrieben: „The more you buy, the more you make!" - Teleshopping im Jahr 2026! 👇 Dass ich jemals einen Rant ueber eine NVIDIA Keynote veroeffentlichen werde, das haette ich auch nicht gedacht, aber in meiner aktuellen Podcastausgabe 👉 https://t.ly/NVIDIA war es dann so weit! Huang hat die Wertschoepfungskette der KI-Industrie auf eine einzige Metrik reduziert: Compute erzeugt Token, Token sind Umsatz, fertig. Was mit den Token passiert, ob jemand damit etwas Sinnvolles baut oder nur teuren Leerlauf produziert, faellt komplett raus. KI-Gigafabs produzieren Token wie eine Druckerei Papier, und am anderen Ende sitzt der Endkunde, der diese Token verbrennt, um Agents laufen zu lassen – und der „Erfolg" misst sich nicht am Ergebnis, sondern am Verbrauch. Das ist keine Technologiegeschichte mehr, das ist ein Geschaeftsmodell, das sich selbst als Naturgesetz verkauft. Compute ist Umsatz, Compute ist Profit, jeder Token ist Profit. Das Perfide daran ist die Verkuerzung – der Wert entsteht angeblich beim Rechnen selbst, nicht bei dem, was dabei rauskommt. Fuer Nvidia und die Hyperscaler stimmt das sogar, die verdienen an jedem erzeugten Token. Fuer euch, mich und alle anderen Endkunden ist das schlicht eine Rechnung, die nie aufgeht. Huang findet es „zutiefst alarmierend", wenn ein gut bezahlter Entwickler nicht mindestens eine Viertelmillion Dollar an Token-Kosten verursacht. Das ist keine Produktivitaetslogik, das ist Verbrauchszwang als Management-Doktrin. Bei einigen Firmen sehen wir die Folgen schon: Mitarbeiter feuern sinnlose, teure Prompts ab, nur um im Token-Ranking nicht abzustuetzen. Wenn der Verbrauch zur Kennzahl wird, optimiert man auf Verschwendung – und genau das ist gewollt! NVIDIAs Behauptung, KI verdreifache die Produktivitaet, das ist methodisch einfach laecherlich. Es betrachtet ausschliesslich Softwareentwickler, also genau die Gruppe mit dem groessten KI-Nutzen, und blendet die unzaehligen Berufe aus, in denen KI praktisch nichts bringt. Daraus dann abzuleiten, es wuerden mehr statt weniger Jobs entstehen, ist Marketing, keine Analyse. Die eigentliche Pointe: Der KI-Wahn in den Rechenzentren saugt den Speicher ab, treibt die Preise hoch und ruiniert gerade den klassischen PC-Markt – mitverursacht von NVIDIA. Und dann kommt ausgerechnet NVIDIA und erklaert den alten PC fuer tot, um euch mit RTX Spark einen ARM-Notebook-Chip zu verkaufen, auf dem ihr „eure eigenen Token" generieren duerft. Man zerschlaegt erst den Markt und verkauft euch dann die Loesung fuer das selbst geschaffene Problem. Ich habe langsam aber sicher das Gefuehl, dass ich in eine Parallel-Dimension gebeamt wurde, denn all dies findet tatsaechlich gerade statt. Maschinen produzieren fuer Maschinen und wer mehr Maschinen kauft, der kann fuer noch mehr Maschinen produzieren. Das ist kein Kartenhaus, das ist kein Ponzi-Scheme... das ist einfach nur noch Wahnsinn!

Two very different AI strategies are playing out right now. Oracle. Meta. Dozens of others. Cutting tens of thousands of people to fund AI investments. They are betting that the technology replaces the humans. Then there's IKEA. They deployed an AI chatbot that now handles 47% of all customer conversations. And instead of using that as a reason to cut staff, they looked at the 53% the bot couldn't handle. Those conversations weren't transactional. Customers wanted design advice. Help with their homes. Guidance on spaces. So IKEA reskilled their customer service staff into interior design consultants. Same people. Just a slightly different job description. That decision generated €1.3 billion in new revenue in the first year. One group of companies asked: how do we cut costs with AI? IKEA asked: what can our people do now that AI handles the routine stuff? Same turbulent technology market. Completely different outcomes. AI doesn't decide what happens to your people. You do. | 300 comments on LinkedIn

siehe eEight.io für technische Umsetzung.

This chapter analyzes the economic footprint  of AI across the private sector and its implications for labor markets, productivity, and the future of work.

Earlier this year, we published The Great Flattening. Our argument was straightforward: AI is making every company sound the same.