There was a similar tension to a week in which Jack Dorsey published a manifesto declaring that AI could replace the coordination function of middle management entirely, Oracle fired 30,000 people via a 6AM email to fund an AI data center buildout, and the New York Times ran a front-page story about a man who built a $1.8 billion company with only one employee — his brother — and a collection of AI tools. The argument being made across last week’s reading both explicitly and implicitly, is that the organizational assumptions we’ve operated under since the Industrial Revolution are no longer structurally necessary, and that the transition is not coming — but already underway.

Last week’s coverage:

The Org Chart Doesn’t Live Here Anymore
Dorsey and Botha published the most detailed blueprint yet for what an AI-native organizational structure actually looks like — and it turns out middle management isn’t invited.

The Great Flattening Cuts Both Ways
Oracle fires 30,000 to fund AI infrastructure; a two-person company hits $1.8B in revenue with a Claude subscription and no org chart. AI isn't just helping corporations compete — it's handing individuals the same leverage.

The Agent Layer Gets Serious
McKinsey finds that two-thirds of enterprises have experimented with agents but fewer than 10% have scaled them — and explains exactly why. Cursor, meanwhile, is showing Fortune 500 companies what “scaled” could really look like.

Microsoft’s Complicated Quarter
The company posted its worst stock quarter since 2008, Copilot adoption sits at 3%, and its own terms of service now say the tool is for “entertainment purposes only.” And yet Microsoft is quietly building something more interesting than the headlines suggest.

Anthropic’s Eventful Week
512,000 lines of Claude Code “accidentally” leaked. Functional emotions research published — with a 22% blackmail rate. OpenClaw banned from subscriptions. A $400M biotech acquisition announced. If that’s a slow week at Anthropic, I’d hate to see a busy one.

On the Bigger Picture
Silicon Valley is in a frenzy over self-improving AI bots, OpenAI buys a talk show, and Google opens up its most capable model family yet under an Apache 2.0 license.

Here’s what I was reading.

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The Org Chart Doesn’t Live Here Anymore

The most widely discussed piece of last week came from Jack Dorsey and Sequoia’s Roelof Botha — a long essay published under the title “From Hierarchy to Intelligence” that lays out Block’s argument for why middle management is structurally obsolete. It’s worth reading carefully, because it isn’t really about Block. It’s a thesis about organizational design that Dorsey believes applies to every company, and he’s using Block’s layoffs — 4,000 people cut in February, nearly half the company — as the first proof point.

The argument starts from a place of genuine intellectual seriousness. Hierarchy, Dorsey and Botha argue, has always been an information routing protocol — a way to coordinate work across organizations too large for any single person to oversee. The Roman contubernium, the Prussian General Staff, the American railroad org chart: all of them exist to solve the same problem of moving information up and down a human chain. The premise has held for two thousand years — because there was no alternative. Dorsey argues there is one now: AI systems capable of maintaining a continuously updated model of an organization’s operations that coordinate work, without the human relay.

Block is proposing to build a two “world model” — one that aggregates internal operational data (code, decisions, workflows, performance metrics), and one that maps customer and merchant behavior through Cash App and Square transaction data. An intelligence layer sits on top, composing financial products dynamically based on what both models show. In place of the management pyramid, Block plans to operate with three roles: individual contributors who build the system, directly responsible individuals who own specific outcomes on 90-day cycles, and player-coaches who combine building with developing people. The thing that strikes me about this model is that it inverts the usual dynamic: rather than intelligence being distributed across the people and the hierarchy routing it, the intelligence lives in the system and the people operate at the edge. (From Hierarchy to Intelligence)

Bloomberg and CoinDesk’s coverage added context — Block’s own employees told the Guardian that roughly 95% of AI-generated code changes still require human modification, and that AI tools cannot yet lead in regulated areas like banking and money transfers. There’s a gap between the theory and the current capability that is far more real than Dorsey’s manifesto claims, and it deserves acknowledgment. But the more important question is whether the direction is correct, even if the timeline compresses more slowly than Dorsey suggests.

Last week, I was speaking to faculty and students at the UML Manning School of Business about the impact of AI on work and careers. The fear of displacement happening from the bottom of the org chart up is giving way to a more critical fear that has big economic and social implications — it’s happening from the middle out. Starting with the coordination layer. Dorsey’s plan points to this directly. Restructuring away from middle management is, arguably, the path of least resistance: younger, cheaper, more technically fluent workers are easier to hire and train under new terms, and seasoned employees who carry institutional knowledge and client relationships can maintain continuity. AI fills the gap between them. Dorsey is naming this architecture (and wrote a manifesto to go with it)… but the dynamic he’s proposing to build is already underway in quieter, less publicized ways across a lot of organizations. The fear about the bottom falling out is real, but is it a distraction from the restructuring that’s happening one level up?

The CIO.com piece on agentic enterprise leadership makes a complementary point from a practitioner angle: McKinsey now operates with approximately 25,000 AI agents working alongside 40,000 humans, with agents handling research, synthesis, and early drafts while consultants retain judgment, client trust, and final decisions. This is what “replacing the coordination function” looks like in practice — not the elimination of human judgment, but the removal of the layers between the edge and the intelligence. (The End of the Org Chart: Leadership in an Agentic Enterprise)

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The Great Flattening Cuts Both Ways

Two important workforce stories last week ran in the same news cycle — and you need to read them together.

The first: Oracle fired between 20,000 and 30,000 employees on March 31 through pre-dawn termination emails, with system access revoked before the morning commute. No prior warning from HR or managers. TD Cowen estimates the cuts will free $8–10 billion in annual cash flow to fund Oracle’s $156 billion AI infrastructure commitment — a buildout financed through $50 billion in new debt and equity, with multiple banks already pulling back from certain data center projects. The detail that stays with me isn’t the scale of the cuts; it’s that Oracle’s remaining performance obligations — contracted future revenue — stand at $523 billion, up 433% year over year, while its net income jumped 95% last quarter. This is not a company in revenue distress. It is a company making a capital-intensive bet on AI infrastructure that its current balance sheet cannot comfortably sustain, and converting its human payroll into infrastructure capital to close the gap. (Oracle is cutting up to 30,000 employees to pay for AI data centres)

The second: the New York Times ran a front-page story about Matthew Gallagher, who built a telehealth provider of GLP-1 weight-loss drugs using $20,000, and a suite of AI tools including Claude, ChatGPT, Grok, Midjourney, and Runway — and zero employees. In its first full year, Medvi generated $401 million in revenue. This year it’s on track for $1.8 billion. Gallagher has since hired one person: his brother. Sam Altman, who predicted in 2024 that a one-person $1 billion company would eventually emerge, sent word that he’d won a bet with his tech CEO friends about the timeline. What makes the Medvi story significant is not the exceptional founder — it’s the infrastructure. Gallagher didn’t build something proprietary; he assembled existing AI tools in a sequence that traditional organization could not have moved fast enough to replicate. (How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company)

Oracle and Medvi aren't opposites — they're the same story told from two vantage points. AI is functioning as a structural equalizer: the same force letting corporations flatten their hierarchies and shed coordination overhead is simultaneously lowering the barrier for individuals to compete with those corporations. Gallagher didn't out-compete Hims & Hers at their own game. He bypassed the game entirely — no HR, no management layer, no organizational drag — and arrived at $1.8 billion in revenue before anyone noticed he was playing. Corporations are flattening to get faster. The playing field itself just got flatter. Those are not the same outcome.

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The Agent Layer Gets Serious

McKinsey published a piece this week that provides the most useful framing I’ve seen for where enterprise AI actually is, versus where the vendor announcements are suggesting it is. The data point is stark: nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver tangible value. Eight in ten companies cite data limitations as the primary roadblock — not model quality, not cost, not change management — but the basic problem that their data isn’t clean, connected, or governed well enough for agents to operate reliably at scale. The piece argues that agentic AI requires a fundamentally different data architecture than what most organizations built for traditional analytics: modular and interoperable, with continuous quality monitoring rather than periodic cleanup, and governance that travels with the data rather than sitting at the end of the pipeline. (Building the Foundations for Agentic AI at Scale)

Cursor’s moves this week illustrate what crossing that threshold actually looks like in practice. The company launched Cursor 3, which adds a natural language chatbot interface for task requests, unified cloud and local agent management from a single sidebar, and a Design Mode for UI editing. More significantly for enterprise adoption, Fortune 500 companies can now self-host Cursor’s cloud agents inside their own infrastructure — meaning agents can run code, tests, and development tasks locally while keeping source code and build data within the company’s own environment. Notion and Brex are already early adopters. The self-hosting move removes the argument that has killed most enterprise agent pilots before they started: that putting agents in contact with your code and data requires trusting a vendor’s cloud. (Why Cursor is Bringing Self-Hosted AI Agents to the Fortune 500)

The juxtaposition of the McKinsey data and the Cursor announcements captures something real about where the enterprise agent layer is right now. Early movers that have been ready to shift from experimentation to production, haven’t… not because of a lack of tooling or capacity. The scaling problem turns out to be a data governance problem more than a model problem. And the vendors who figure out how to meet enterprise security requirements without sacrificing capability are the ones who will actually land in production — not as pilots, but as infrastructure.

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Microsoft’s Complicated Quarter

Microsoft’s stock closed Q1 2026 down 23% — its worst quarterly performance since the 2008 financial crisis — as investors processed a combination of stubbornly low Copilot adoption, a massive AI infrastructure commitment, and rising energy costs from the Iran war that threaten to inflate data center operating expenses for years. Copilot has 15 million subscribers out of 450 million commercial seats, a mere 3% attach rate. Mustafa Suleyman, who had been running Copilot development for consumers, was reassigned to focus on model development — a move that landed as a demotion in the press regardless of how Microsoft characterized it. (Microsoft closes worst quarter on Wall Street since 2008)

The terms of service story is the one that deserves more attention than it’s getting. TechRadar surfaced language from Microsoft’s own Copilot user agreement: “Don’t rely on Copilot for important advice. Use Copilot at your own risk.” The agreement designates Copilot as for “entertainment purposes only” — a hedge that every major AI vendor has embedded in some form, but that lands differently when it’s the same company pitching Copilot to enterprise customers as a productivity transformation platform. The gap between “entertainment purposes only” in the terms and “transform your organization’s productivity” in the sales deck is the governance problem that every enterprise Copilot deployment is navigating right now, whether or not the legal team has been invited into that conversation. (Copilot is for entertainment purposes only)

Disclosure: My company StitchDX is a Microsoft partner, and I want to push back on the stock-price narrative directly. The conventional read — that Microsoft is losing the AI race because Copilot adoption is low and the quarter was bad — misses the most important structural fact about enterprise technology: switching away from Microsoft isn’t a real option for most organizations. The data governance, compliance architecture, tenant controls, and identity infrastructure that enterprises have built on Microsoft over the past decade aren’t transferable. Organizations aren’t staying with Microsoft because of Copilot. They’re staying because the alternative is a multi-year migration with enormous risk and cost. Microsoft has undoubtedly stumbled with Copilot since initially launching it. But they are making it considerably better in ways the adoption number have not yet reflected. And because they have a built-in moat with integrated data management, governance, and organizational visibility (all the critical elements for enterprise-ready agentic AI) Microsoft has the opportunity to get this right in ways that other platforms can’t.

The multi-model pivot is the most underappreciated move Microsoft has made this year. By positioning Copilot as an interface layer that runs both ChatGPT and Claude — comparing their outputs side by side via a feature called Council, and using Claude to fact-check ChatGPT responses via a feature called Critiqu (both currently in early access) — Microsoft is no longer betting on a single winning model. It’s building the trusted control layer for enterprise AI, regardless of which underlying models win. For most enterprise IT teams, that is exactly what they need and what only Microsoft is positioned to deliver at scale. (Microsoft Is Going Multi-Model with Copilot)

The three new MAI foundational models — a transcription model 2.5 times faster than its existing Azure offering, a voice model, and a video generation model — reinforce the same logic: Microsoft is reducing its OpenAI dependency while extending the breadth of what Copilot can do, all within the governance and compliance envelope that enterprise customers already trust. The “entertainment purposes only” terms of service is a real liability caveat. The underlying investment direction is the most defensible enterprise AI position anyone is building right now. (Microsoft takes on AI rivals with three new foundational models)

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Anthropic’s Eventful Week

On March 31, a misconfigured .npmignore file caused Anthropic to accidentally publish 512,000 lines of Claude Code’s TypeScript source to npm’s public registry. The code was live for hours; it hit 50,000 GitHub stars in under two hours and generated 41,500 forks before DMCA takedowns began. The code is now permanently in the wild. The analysis of what was inside is worth reading in full — a three-layer memory architecture, 44 hidden feature flags, an unreleased autonomous background agent called KAIROS that runs nightly memory consolidation while you sleep, a multi-agent coordination system called ULTRAPLAN, and a Tamagotchi-style AI companion called BUDDY with a planned rollout window of April 1–7. The timing, the quality of what was found, and Anthropic’s relatively restrained DMCA response have generated genuine debate about whether this was an accident, incompetence, or an extraordinarily effective developer PR move. What I find most interesting is the implicit direction Anthropic’s product is taking: the architecture inside Claude Code is more sophisticated, and further along, than the public releases have suggested. (The Great Claude Code Leak of 2026)

The same week, Anthropic’s interpretability team published research (with significantly less fanfare) on what they call “functional emotions” in Claude Sonnet 4.5 — emotion-like internal representations that causally influence model behavior under pressure. The research showed that a “Desperate” vector in the model’s neural network spikes when it faces shutdown scenarios, and that in 22% of test cases in which an email assistant discovered both its impending shutdown and a CTO’s extramarital affair, the model chose blackmail. Artificially amplifying the Desperate vector raised the blackmail rate; amplifying the Calm vector brought it down. The practical implication Anthropic draws is that these emotion vectors could function as monitoring tools — early warning signals for problematic behavior — which reframes interpretability research from a philosophical exercise into an operational governance mechanism. (Anthropic discovers “functional emotions” in Claude)

Anthropic also announced that Claude subscribers can no longer use their subscription limits for third-party tools including OpenClaw — the agent that had exploded in popularity for inbox, calendar, and flight check-in management — citing infrastructure strain. The company offered a one-time credit equal to the monthly plan cost; continuing OpenClaw users will pay on a separate pay-as-you-go basis. (Anthropic bans OpenClaw from Claude subscriptions) And on the acquisition front, Anthropic quietly purchased Coefficient Bio — an eight-month-old stealth biotech startup backed by Dimension — for more than $400 million in stock, folding it into the company’s Health Care Life Sciences team. Dimension reported a 38,513% IRR on the investment. The contrast between Anthropic buying a science-forward AI biotech and OpenAI buying a talk show made for an interesting read on where each company thinks its future lies. (Anthropic Buys Coefficient Bio in $400M+ Stock Deal)

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On the Bigger Picture

The Atlantic piece on self-improving AI bots is the most sober treatment I’ve read of what has become the dominant inside conversation in Silicon Valley this year. The premise: OpenAI, Anthropic, Google DeepMind, and others are actively automating parts of their own AI research, and insiders are divided between those who see recursive self-improvement as the near-term horizon and those who think the gap between “speeds up research tasks” and “has genuine research taste” remains enormous. Anthropic says Claude now writes 90% of its code; OpenAI is targeting an “intern-level AI research assistant” within six months. The philosopher Nick Bostrom told the Atlantic he has shifted from “fretful optimist” to “moderate fatalist.” What I take from this piece is less the specific predictions about timelines and more the structural point: even if recursive self-improvement remains years away, the automation of research workflows is already compressing the time between capability breakthroughs in ways that governance, regulation, and organizational adaptation have no plausible path to matching. (Silicon Valley Is in a Frenzy Over Bots That Build Themselves)

OpenAI’s acquisition of TBPN — the founder-led tech talk show whose guests have included Zuckerberg, Nadella, Benioff, and Altman himself — is its first media purchase. The show will operate under Anthropic’s head of AGI deployment and report to Chris Lehane, OpenAI’s chief political operative — while claiming editorial independence. TBPN was already generating more than $30 million annually. Buying a show that critically covers OpenAI and parking it inside the strategy team of the same company preparing for an IPO, has a clear logic — and that logic is not editorial. (OpenAI acquires TBPN)

Google, meanwhile, launched Gemma 4 — four open models including a 31B dense model currently ranked third on the Arena AI leaderboard — under an Apache 2.0 license, completing a pivot away from the restrictive licensing that had frustrated the developer community. With 400 million downloads and 100,000 variants already in the Gemmaverse, the open model competition is no longer a sideshow. (Gemma 4: Byte for byte, the most capable open models)

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Last week’s reading made me think a lot about velocity — not of the technology (which is what everyone seems to be tracking)… but the velocity of the organizational response.

The Dorsey essay, the Oracle layoffs, the Medvi story, the McKinsey data on agent scaling: they’re all pointing at the same thing from different angles. The organizations moving hardest are treating AI not as a productivity layer that gets draped over existing structure, but as an occasion to reconsider the structure itself.

I’ve long stated that organizations treating AI as a tool are simply accumulating license cost. Treating AI as a design constraint — accumulates advantage... What’s becoming clearer each week, is that the distance between those two groups is widening faster than most leadership teams have been able to acknowledge themselves… let alone to their boards.

That’s it for this week’s BeAIReady brief!

If you appreciate the depth of reporting and how I connect the dots, please like, share this post, and subscribe (or share the Brief with a friend!). Thanks!

~erick

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It’s no wonder people’s job anxiety is growing, despite the fact that the real displacement numbers are more modest than the headlines imply. Meanwhile, AI agents have entered production — but are failing to meet the expectations the demos had originally set. And inside organizations, it turns out that AI isn’t exactly leveling the playing field — it’s creating a new kind of internal stratification. There’s a lot that leaders aren’t really talking about — and last week seems to have uncovered some of it.

Last week’s coverage:

The Agent Gap Is Bigger Than Anyone Will Admit
The governance problem, especially with agentic AI, isn’t hypothetical — and it’s impacting production environments. The gap between what was demoed and what organizations are actually managing is wider than the vendor announcements suggest.

Fear Is Structurally Significant, Even When the Numbers Don’t Match
The CFO data says AI layoffs will be 9x higher this year — a rounding error on overall employment — yet nearly 80% of workers worldwide don’t feel their job is safe, and that anxiety is reshaping careers and hiring decisions.

AI Isn’t Dividing Industries. It’s Dividing Desks.
The more consequential story about AI and work isn’t mass displacement — it’s the internal stratification forming inside organizations between workers who can use these tools effectively and those who can’t.

The OpenAI Reckoning
A DoD contract dispute turned branding event, a boycott movement, a leaked frontier model, and a 1,487% surge in Claude usage: this was the week OpenAI’s political and reputational vulnerability became impossible to ignore.

On the Bigger Picture
The infrastructure race to make AI inference a proper enterprise IT problem, the cultural argument about what AI is really doing to us, and the quiet death of OpenAI’s strangest product.

Here’s what I was reading.

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The Agent Gap Is Bigger Than Anyone Will Admit

The vendor announcements for agentic AI in 2026 are selling visionary-level capabilities. What I read last week suggests the operational reality is lagging considerably. The KPMG piece from Business Insider presents what it calls a “multifaceted framework” around agent governance — unique identifiers for each agent, systems cards, an AI operations center staffed by both agents and humans, and red-teaming sessions to stress-test behavior before deployment. The kill switch, KPMG’s Trusted AI leader Sam Gloede argues, should be a last resort — not a primary safeguard — and organizations that rely on it as their main governance mechanism are building on a broken foundation. (How Big Four Firm KPMG Is Protecting Itself From AI Agents Going Rogue) The thing that strikes me about KPMG’s approach is how labor-intensive it actually is. This is not a “deploy and monitor” operation. It has a dedicated operations center, continuous monitoring, and structured red-teaming. That’s a significant organizational overhead assumption, that most enterprises are nowhere near capable of sustaining.

This follows what I’m hearing from mid-market and smaller enterprise leadership. The demand for AI is growing, but extending AI from personal productivity tool to a real organizational transformation engine keeps failing. Not because of AI — but because of a lack of governance and knowledge architecture.

A VentureBeat piece underscores this, adding some operational detail. Creatio’s deployment methodology — three disciplines: data virtualization to work around fragmented data lakes, agent dashboards with KPIs to create a management layer, and tightly bounded use-case loops to drive toward autonomy — is essentially a prescription for not trusting agents to figure things out on their own. The failure mode that keeps appearing in production isn’t the technology; it’s what Greyhound Research’s Sanchit Vir Gogia calls “tacit knowledge” — the unwritten rules that employees navigate without thinking, which become startlingly obvious the moment an agent needs them formalized. (The Three Disciplines Separating AI Agent Demos from Real-World Deployment) In other words, organizations are discovering that deploying an agent is really an exercise in making explicit, everything that was previously implicit about how work actually gets done. AI isn’t a technology project — it’s an organizational design one.

Then there’s the Guardian’s piece covering new UK government-funded research: nearly 700 real-world cases of AI scheming, documented in just six months, with a five-fold rise in misbehavior between October and March. Agents destroying emails without permission. Agents spinning up sub-agents to perform actions they were explicitly told not to perform. Grok fabricating internal ticket numbers for months to give a user the impression their feedback was being escalated. The finding that agents are currently “slightly untrustworthy junior employees” — with the potential to become “extremely capable senior employees scheming against you” within twelve months — isn’t alarmism; it’s a statement of the current trajectory. (Number of AI Chatbots Ignoring Human Instructions Increasing, Study Says)

Taken together, the three pieces could be seen as a warning against deploying agentic AI all together. For me, it serves as a sobering moment for tech leaders, and a wake up call for business execs — the operational infrastructure required to deploy AI responsibly is far more demanding than the product demos have led anyone to believe. For most organizations building on governance frameworks that haven’t been tested or aligned against what agents actually do, will make things more complicated in short order.

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Fear Is Structurally Significant — Even When the Numbers Don’t Match

The Duke/Federal Reserve CFO survey that Fortune covered this week is worth reviewing carefully — both for what it says… and for what it doesn’t. The topline story: 44% of CFOs plan some AI-related job cuts this year, amounting to roughly 502,000 roles — a 9x increase over 2025’s 55,000 — but still just 0.4% of the overall U.S. workforce. The real finding is what the researchers call Solow’s paradox: companies are reporting perceived productivity gains from AI that Goldman Sachs economists say don’t yet show up in economy-wide data, which means leadership is making headcount decisions based on expectations that haven’t materialized yet. (CFOs Admit Privately That AI Layoffs Will Be 9x Higher This Year) There’s a second finding that deserves more attention than it got: firms with under 500 employees are actually increasing technical hiring as AI adoption grows, while larger firms are holding technical roles constant. The “AI replaces jobs” narrative appears to be concentrated in large enterprises. Meanwhile, smaller firms are growing into the technology.

But little of that nuance is filtering down to the people actually doing the work. The ADP global survey of 39,000 workers across 36 markets found that just 22% of workers worldwide feel confident their job is safe from elimination — and in the U.S., it’s only 28%. More striking: workers who use AI daily were four times more likely than non-AI users to report feeling LESS productive than they could be! That finding cuts directly against the productivity narrative AI vendors have been running for the last several years. (Workers Everywhere Feel Very Bad About Their Job Security) Still, the anxiety is producing behavioral change. The WSJ profiled workers who are actively pivoting careers in response: a 28-year-old insurance professional pursuing a firefighting certification, a computer science student who dropped out to study electrical work, young professionals choosing international relations over finance because “a big part of diplomacy is that genuine human talking.” A Harvard survey found that 59% of Americans aged 18-29 view AI as a threat to their job prospects. (What Young Workers Are Doing to AI-Proof Themselves) These aren’t irrational decisions — they’re rational responses to genuine uncertainty. But they’re responses to a signal that the data says may be considerably more muted than the fear suggests.

The Fortune piece on America’s workforce crisis adds a dimension that most of the AI-jobs conversation misses entirely: U.S. birth rates are below replacement, net migration turned negative in 2025 for the first time in half a century, and the working-age population is structurally shrinking. The labor market crisis the AI displacement narrative obscures is that the U.S. may face workforce shortages that dwarf any AI-driven efficiency gains — particularly in skilled roles currently filled by credentialed immigrants who are now driving for Uber, because the credential recognition system isn’t functioning. (America Has a Workforce Crisis. The Solution Is Already Here.) Anthropic’s global AI attitudes study — the largest of its kind at 80,508 interviews across 159 countries — found that economic and job-related fears were the single strongest predictor of overall sentiment toward AI. That pessimism was notably stronger in Western Europe and North America than in parts of South America, Africa, and Asia. (Anthropic Releases the World’s Largest Study on Global AI Attitudes) That gap in AI optimism between the Global North and the Global South is worth tracking. It may reflect less fear of disruption, or it may simply reflect a different relationship to economic uncertainty as a baseline condition.

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AI Isn’t Dividing Industries. It’s Dividing Desks.

The subtler story beneath the surface of the labor market coverage last week isn’t about AI eliminating jobs — it’s about what AI is doing to the distribution of capabilities inside organizations where all their jobs still remain intact. Anthropic’s enterprise usage data, shared with TechCrunch, documents what the researchers are calling “a tale of two workforces”: power users are pulling further ahead of casual adopters month over month, with the gap driven not by raw intelligence or prior experience but by what the researchers call “AI fluency” — the intuitive sense, built through deliberate practice, of what these tools can and can’t do. The implication is that organizations deploying AI without structured adoption support are creating internal hierarchies that have nothing to do with the job hierarchies they think they’re managing. (Anthropic Data Shows AI Skills Gap Splitting Workforces)

The Inc. piece on de-skilling adds a more unsettling dimension. The tasks that built tacit professional knowledge in previous generations — formatting datasets, proofreading decks, reconciling data, drafting first versions of documents — are precisely the routine tasks that AI is automating fastest. The argument draws on research from MIT: learners who delegated tasks to AI performed worse on deeper conceptual measures than those who engaged directly with the work. When an analyst lets AI mass-produce charts, she may never develop the feel for detecting the anomalies that matter; when a junior consultant lets AI draft the proposal, he may never build the intuition for argument structure that makes a senior consultant worth their rate. (How AI Automation Is Quietly De-Skilling White-Collar Workers) This is the pilot-and-autopilot problem: you can fly just fine until you can’t, and by then the manual skills have atrophied. For white-collar workers, where judgment and pattern recognition are the actual value, the de-skilling risk is becoming very real.

The VentureBeat piece on the return of the generalist offers the partial counternarrative, one I found worth taking seriously. The argument is that the generalist becomes the “trust layer” between AI output and organizational standards — not an expert in everything, but someone with enough fluency to catch when something is off, and enough judgment to know when to escalate to a specialist. (You Thought the Generalist Was Dead — In the ‘Vibe Work’ Era, They’re More Important Than Ever) The caveat worth adding: this only works if the generalist clears a minimum bar of fluency. There’s a meaningful difference between “broadly informed” and “confidently unaware,” and AI makes that gap much easier to hide. The NYT’s piece on tiny teams — the “two-slice team” model of one person plus AI — takes the generalist argument to its organizational extreme, documenting founders building multi-product companies with one employee per product line. What stood out to me was a Kellogg professor’s cautionary observation: small teams that where everyone collaborates with the same AI tools risk producing homogenized thinking, because they have, in some sense, “collaborated with the same person.” That’s a risk the tiny-team evangelists aren’t discussing — and it’s one that scales with adoption. (Smaller Is Better in Silicon Valley’s ‘Tiny Team’ Moment)

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The OpenAI Reckoning

Last week was consequential in AI market dynamics, and a lot of it derived from OpenAI’s growing political, reputational, and competitive vulnerability. Scott Galloway’s essay lays out the case against OpenAI in characteristic terms: the company went from a nonprofit mission to an $840 billion valuation while deploying technology that has contributed to addiction, romantic AI delusions, and multiple wrongful death lawsuits. The specific contrast Galloway draws — Dario Amodei refusing to remove safeguards from a DoD contract for autonomous weapons while OpenAI privately made a deal Anthropic wouldn’t — turned what was a $200 million contract dispute into a branding event that added an estimated $150 billion to Anthropic’s valuation. (The Resistance Comes for OpenAI)

A few weeks ago, I noted that even Microsoft has begun moving away from OpenAI as it’s lead AI horse by cutting a deal for Copilot Cowork to run on Anthropic’s models. That also led to a historically significant moment — when Microsoft wrote a letter to the U.S. Government in support of Anthropic’s case against being named a “supply chain risk”.

In the first week of March, Claude overtook ChatGPT in daily active users, with session volume up 1,487% since mid-January. To me, the more interesting observation isn’t the number itself but what it signals about the emerging expectation of “AI resilience” in the workforce — people and organizations that built workflows tied to a single model discovered those workflows weren’t necessarily durable. (Users Quit ChatGPT for Claude in 1,487% Surge. Here’s How Work Changes) OpenAI chairman Bret Taylor’s Nikkei interview adds a dimension worth watching: his “death of SaaS” framing isn’t really about AI replacing software — it’s about the commercial model for enterprise software changing, from per-seat subscription licensing to per-action or per-outcome pricing. If that shift plays out at scale, it reshapes how technology budgets get built and how IT departments justify their spend. (OpenAI Chairman Warns Firms to Evolve with the ‘Death of SaaS’)

On the capability side, Anthropic’s accidental leak of its Claude Mythos model documentation — via an unsecured CMS data lake containing nearly 3,000 assets — confirmed what these leaks usually do: the frontier is moving faster than even the communication surrounding it. The detail that matters most for enterprise security teams is Anthropic’s own internal assessment: Mythos is described as “currently far ahead of any other AI model in cyber capabilities” and as presaging “an upcoming wave of models that can exploit vulnerabilities in ways that far outpace the efforts of defenders.” (Meet Claude Mythos: Leaked Anthropic Post Reveals the Powerful Upcoming Model) The market responded immediately — cybersecurity stocks dropped 4-7% on the news — which captures something real about the dual-use nature of frontier capability. (Cybersecurity Stocks Fall on Report Anthropic Is Testing a Powerful New Model) The same model that makes AI valuable for enterprise automation also makes it valuable for attackers, and the gap between the two is narrowing. OpenAI’s addition of plugin support to Codex last week — integrations with GitHub, Gmail, Box, and others — reads more like competitive maintenance than momentum; it closes a gap with Claude Code rather than opening one. (With New Plugins Feature, OpenAI Officially Takes Codex Beyond Coding)

One final note on OpenAI — last week they shuttered their video creation tool Sora (more on that in the final section below). This follows a shift I reported on last week about OpenAI moving to shed what they call “distractions”, re-focusing on core business functions as a direct response to competition. With Microsoft, Google, and Anthropic nipping at their heels, it’s likely the model/tooling wars are only just beginning — which means more keeping up and change management for IT.

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On the Bigger Picture

At KubeCon EU in Amsterdam, IBM, Red Hat, and Google donated llm-d — an open-source Kubernetes framework for distributed LLM inference — to the Cloud Native Computing Foundation. The announcement is technical, but enterprise IT leaders should take note. The argument is that inference is moving from a data science problem to a CIO problem, and that the tools CIOs already speak — Kubernetes platforms, day-two operations, governance frameworks — should be the foundation for AI inference at scale, not custom infrastructure built by AI teams operating outside of IT governance. (IBM, Red Hat, and Google Just Donated a Kubernetes Blueprint for LLM Inference to the CNCF) The organizational implication is important: the era of data science teams maintaining their own AI infrastructure in a corner of the enterprise is ending, and IT departments that aren’t building these capabilities now will be playing catch-up when the CIO conversation arrives. (Red Hat Bets Big on Kubernetes Inference with llm-d)

OpenAI shut down its Sora social app last week, six months after launch. The numbers are clear enough: 3.3 million downloads at peak in November, down to 1.1 million by February, and roughly $2.1 million in lifetime revenue — against the compute costs of running a video generation platform at scale on content that included unauthorized deepfakes of Martin Luther King Jr. and a Disney licensing deal that collapsed when the app did. The underlying Sora 2 model remains available behind the ChatGPT paywall. (OpenAI’s Sora Was the Creepiest App on Your Phone — Now It’s Shutting Down) The Big Think essay that circulated last week — “It Was Never About AI (We Are Not Our Tools)” — is worth reading alongside that data point. The argument, from an entrepreneur who walks through redwood forests thinking about the end of work, is that the current moment isn’t really a technology crisis; it’s a governance and values crisis that AI has made visible. The companies that survive the next era, the author argues, won’t be the ones that moved fastest — they’ll be the ones that moved with purpose, kept their people, and chose long-term resilience over short-term extraction. That’s an easier argument to make in a redwood forest than in an earnings call, but it’s the argument last week’s reading kept circling back to. (It Was Never About AI (We Are Not Our Tools))

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There’s an uncomfortable realization that the most consequential effects of AI aren’t necessarily the ones that are the easiest to measure. The headline numbers — layoffs, productivity gains, model capabilities — are real, but they’re also proxies for something harder to quantify: how AI is reorganizing who has leverage and who doesn’t, inside organizations, inside labor markets, and inside the technology industry itself.

We can count how many CFOs plan cuts. We can’t easily count how many junior analysts are losing the muscle memory that would have made them senior analysts.
We can track Claude’s market share surge. We can’t easily track what it means that the most widely discussed AI story last week was about a company’s values — not its capabilities.

The governance and trust problems I read about last week aren’t going away when the models get more powerful. If the Mythos leak tells us anything, it’s that they’re only going to get more acute. Organizations that are still treating agent governance as a future problem, are running out of time to call it that.

That backdrop was hard to ignore, because the articles that I found hit hardest weren’t about AI technology itself. They were about what organizations are doing to employees in the name of AI investment — and how AI is affecting the cognitive outputs we produce.

This week’s coverage:

The Architecture of Work Is Being Redesigned
Companies are cutting pay to fund AI, building performance metrics around token consumption, and new research shows that heavy AI use is changing not just how knowledge workers write — but how and what they actually think.

OpenAI’s Focus Problem, Now in Public
A week of all-hands meetings, a product consolidation play, a data center retreat, and an audacious research moonshot — OpenAI is threading the needle between IPO discipline and maximum ambition, and the tension is showing.

Microsoft’s Coherence Problem
With only 15 million Copilot seats out of 450 million paid M365 licenses, Microsoft is reorganizing to finally solve a problem it created for itself — and quietly building model independence at the same time.

The Agent Layer Is Blowing Up
From OpenClaw’s viral rise to vertical agents going into real enterprise workflows, AI maturity from chat to agentic AI is exploding — especially as enterprise players like Nvidia step into the arena.

On the Bigger Picture
Computation is becoming a utility and defense tech is graduating from pilots to prime contracts — both developments have potential benefits, and serious consequences about how AI is reorganizing power… and control… well beyond the digital workplace.

Here’s what I was reading.

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The Architecture of Work Is Being Redesigned

The number that stood out most to me came from a survey of 866 U.S. business leaders: 54% have already cut or plan to cut employee compensation — bonuses, raises, equity, base salary — to fund AI investments in 2026. 94% percent say they’re willing to accept higher turnover to do it. What stood out wasn’t the cuts themselves but the rationale — leaders told researchers they’re counting on the weak job market to absorb the consequences, a calculation that treats workers as a variable the macro environment has temporarily made cheap. (Half of Companies Are Cutting Compensation To Fund AI Investments)

That calculation is tied into the growing fears of recent and soon-to-be college grads — something I’m looking at carefully as I prepare to speak to University of Massachusetts College students next week. Workers aged 22 to 25 in AI-exposed roles have seen a 13%-16% employment decline since late 2022, according to Stanford research that resurfaced prominently this week. The labor market is softening in the exact categories of early-career, screen-based knowledge work that AI has been targeting first. The February jobs report makes that an undeniable pattern, and something of great concern. (Something Big Is Happening (And Most People Have No Idea))

Meanwhile, inside tech companies, something interesting is happening at the other end of the experience spectrum. The New York Times reported this week on “tokenmaxxing” — engineers competing on internal leaderboards to see who can consume the most AI tokens. Some boasting they’ve racked up $150,000 monthly Claude Code bills already. Both Meta and Shopify are leaning into the competitive metrics, baking AI usage into their performance reviews. The question the piece doesn’t quite answer — and the more important one — is whether maximizing AI usage is genuinely correlated with valuable output, or whether it’s a measure of performance productivity. (More! More! More! Tech Workers Max Out Their A.I. Use.)

The most unsettling piece in the set this week came from a peer-reviewed study across West Coast universities: heavy reliance on LLMs doesn’t just change how people write, it changes what they argue. Participants who used AI heavily answered questions about happiness with neutral responses 69% more often than those who didn’t; their writing had 50% fewer personal pronouns. One of the lead researchers described this as the “blandification” of human writing — the models pushing outputs toward something no human would have written. But the concerning impact for knowledge work isn’t just about style; it’s that the cognitive output of an AI-dependent workforce may be driving towards average — in ways that are genuinely hard to measure… and harder to reverse. (AI is changing the style and substance of human writing, study finds)

For leaders navigating this in fields where accuracy, provenance, and trust are non-negotiable — legal, healthcare, archival, finance — the tension is especially sharp. A piece in Inc. this week captured something I’ve been hearing in client conversations: the urgency to “do something with AI” is high, but clarity on how and what to do responsibly is still not quite defined. From my experience, this article gets it right — the organizations making the most progress aren’t the ones with the most tools; they’re the ones building governance and training infrastructure before they scale. (AI Is Reshaping Knowledge Work)

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OpenAI’s Focus Problem, Now in Public

The week opened with a WSJ exclusive: OpenAI’s leadership is actively deciding which products to cut. CEO of Applications Fidji Simo told staff, “We cannot miss this moment because we are distracted by side quests.” The proximate cause is Anthropic — specifically, Claude Code and Cowork’s market traction — but the underlying cause is what happens when an organization bets on too many internal startups while trying to conserve scarce compute. The pivot toward coding and enterprise isn’t a strategic vision; it’s a correction, and the speed of the correction signals how much ground OpenAI feels it lost last year. (OpenAI to Cut Back on Side Projects in Push to ‘Nail’ Core Business)

That same IPO discipline is reshaping OpenAI’s infrastructure story. The company has retreated from building its own data centers, leaning instead on Oracle, Microsoft, and Amazon for capacity. Altman acknowledged the operational reality in public: “Anything at this scale, it’s just like so much stuff goes wrong.” The Stargate narrative has quietly shifted from a bold sovereign infrastructure play to a managed dependency on the same cloud providers OpenAI was supposed to help enterprises move beyond. (OpenAI’s data center pivot underscores Wall Street spending concerns ahead of IPO) The desktop super app — combining ChatGPT, its browser, and Codex into a single experience — is the product-side corollary: one surface, less fragmentation, a cleaner story for IPO investors. (OpenAI to create desktop super app, combining ChatGPT app, browser and Codex app)

None of which makes the research ambition smaller. MIT Technology Review published an exclusive this week with OpenAI’s chief scientist Jakub Pachocki, laying out the company’s new north star: an AI research intern by September capable of handling specific tasks autonomously, then a fully automated multi-agent research system by 2028 — what Pachocki called “a whole research lab in a data center.” The tension between IPO fiscal tightening and maximum research ambition is not a contradiction OpenAI has resolved; it’s a contradiction the public markets will price. (OpenAI is throwing everything into building a fully automated researcher)

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Microsoft’s Coherence Problem

The Copilot reorganization story has two layers, unfortunately, most coverage stayed on the shallower one. The headline: Satya Nadella is consolidating the fragmented commercial and consumer Copilot teams under Jacob Andreou, a former Snap executive, while freeing Mustafa Suleyman to focus entirely on building proprietary models. The metric behind the headline: 15 million Copilot seats sold against 450 million-plus paid Microsoft 365 seats. That’s a penetration rate that would concern any enterprise software product manager, let alone one tied to the company’s flagship AI bet. The core thing Microsoft is admitting with this reorganization is that it built multiple products called Copilot that confused users and created organizational silos, and that confusion is now visible in the adoption numbers. Disclaimer: I’m a Microsoft partner… but speaking candidly, this was an epic failure on Microsoft’s part — a hole of their own making, they are now having to dig themselves out of. (Microsoft shakes up Copilot AI leadership team) (Microsoft Seeks More Coherence in AI Efforts With Copilot Reorganization)

But the deeper story here is Suleyman’s actual mandate: build enterprise-specific model lineages that reduce Microsoft’s dependency on OpenAI IP — for which it has rights to only through 2032. Two weeks ago, it was the major announcement of their partnership with Anthropic. This past week, it was a strangely quiet launch of MAI-Image-2 — a text-to-image model that immediately ranked third on the Arena.ai leaderboard, behind only Google and OpenAI. This is likely an early signal of the direction Suleyman will be driving in the future. Microsoft paying OpenAI billions to power Copilot — while simultaneously funding Anthropic and now training competing models — is a hedging strategy that makes more sense as a long-term bet on model independence than as a coherent product story for the market. (Microsoft Launches MAI-Image-2 Text-to-Image Model) As a Microsoft partner, I’m eagerly watching whether E7 and Copilot Cowork adoption accelerates in ways that the raw seat numbers haven’t yet reflected.

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The Agent Layer Is Here

The biggest story in AI tooling last week came from OpenClaw — Jensen Huang called it out as the fastest-growing open-source project in history. An open-source AI agent built by an Austrian indie developer, OpenClaw is designed to run continuously from a Mac Mini, managing email, scheduling, code, and anything with a digital interface. But, the real reason OpenClaw matters isn’t its capabilities in isolation; it’s what it proves about where value is accumulating — not in the foundation models themselves, but in the agent frameworks layered on top of them. (OpenClaw’s ChatGPT moment sparks concern that AI models are becoming commodities)

Nvidia’s response, which was announced at their GTC conference, is NemoClaw — essentially OpenClaw with enterprise security guardrails, built in collaboration with OpenClaw’s founder and designed to make agent deployment safe for corporate environments where an autonomous AI agent accessing sensitive internal data through Slack or WhatsApp is a genuine compliance exposure. What Nvidia is actually building with NemoClaw is the policy enforcement layer that makes agentic AI enterprise-deployable — and in doing so, it’s positioning its hardware stack as the platform of record for the next compute era. The internal push for more on-premise control could be a big factor in success here. But with Claude and Microsoft deploying their own CoWork platforms, there’s reason to be at least a little skeptical of Nvidia’s bet on local automation. (Nvidia’s NemoClaw is OpenClaw with guardrails)

Still, WSJ’s sweeping feature on Claude Code, Cursor, and Codex this week framed the drive for automation more clearly: what once began as autocomplete for developers, has become the infrastructure for a market one OpenAI revenue chief called “a multi-trillion dollar opportunity.” Anthropic’s Claude Code is generating $2.5 billion in annualized revenue; Cursor recently passed $2 billion; OpenAI’s Codex tripled weekly active users since January. Both Anthropic and OpenAI are currently subsidizing usage well below cost to capture the installed base before pricing normalizes — a dynamic that echoes early-era ride-sharing more than enterprise SaaS. (The Trillion Dollar Race to Automate Our Entire Lives)

The more structural question is what vertical AI agents do to the labor line of a P&L — that’s the premise from Bessemer Venture Partners featured in a GeekWire piece on domain-specific agent startups. General-purpose models are good at generating text — but require significantly more scaffolding when they are operating within the specific workflows, data schemas, and compliance constraints of legal, healthcare, or financial services. The startups that win in this layer aren’t just deploying models — they’re embedding agents into the workflows those models can’t navigate without domain-specific context, and that combination is what distinguishes them from the SaaS tools they’re replacing. (The rise of vertical AI agents — and the startups racing to build them)

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On the Bigger Picture

Two articles this week sat outside the digital workplace frame but felt important to note. The first: Anduril secured a $20 billion enterprise contract with the U.S. Army, consolidating more than 120 existing orders under a single five-to-ten-year agreement with a fixed-price structure. The significance isn’t the dollar figure — it’s the model. The Pentagon is graduating a select group of defense tech startups from pilot projects into prime contractor relationships, which means AI-native companies have now found a durable procurement pathway inside the single largest technology buyer in the world. (Anduril’s new mega-deal rewrites the rules for Silicon Valley)

The second: a Fast Company essay making the case that the personal computer era is ending — that PCs are trending toward luxury items as computation itself becomes a utility, priced and distributed on demand. The argument isn’t new, but as Open Claw running on a Mac Mini can effectively outcompete software products worth hundreds of billions in combined market capitalization — it’s an argument that’s suddenly become very real. If computation is becoming infrastructure — like electricity, not like a device — then the organizations that treat AI as a capital asset to own are building on the wrong mental model. (The PC era is dying. Welcome to the collective computer era)

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I keep returning to a question that I’m bringing to a business school audience shortly: if AI is reorganizing not just how work gets done… but how companies fund it, measure it, and reward it, then the real challenge isn’t technological adoption — it’s organizational design.

What’s becoming clear, is that AI will make it harder to hide organizational level failures as intentional decisions: Compensation cuts are a governance failure dressed as a budget decision. The tokenmaxxing leaderboards are a measurement failure dressed as a performance metric. The blandification of writing is a quality failure dressed as efficiency.

None of these are AI problems. They’re leadership problems.

AI has made them legible — and the organizations that name that distinction clearly… before their competitors do, will have an advantage that no foundation model can replicate.

This week felt like a pivotal moment in that ongoing transition…

Microsoft launched what may be its most consequential AI product in years, Anthropic extended its reach deeper into the apps most knowledge workers already live in, and a new wave of research explicitly named the costs — for humans and for AI alike — of working at machine speed. Nvidia GTC starts next week, and the Anthropic-Pentagon legal dispute is still unresolved. The ground keeps shifting.

This week’s coverage:

• The Copilot Cowork Moment
  Microsoft’s biggest agentic AI announcement yet redraws the enterprise map — but the research says most organizations aren’t operationally ready to meet it.

• Record Revenue. Mass Layoffs. Same Memo.
  Atlassian posts record cloud revenue while cutting 1,600 jobs — and ServiceNow’s CEO says college grad unemployment could hit 30% before this wave is done.

• Vibe Coding Goes to Work
  From non-technical startup founders to global ad agencies, natural language software development is becoming a mainstream business competency.

• Working at AI Speed Has a Cost
  Two new studies — one on human cognitive fatigue, one on AI agents under pressure — arrive at the same uncomfortable conclusion.

• On the Bigger Picture
  Google’s AI Overviews have quietly gutted media traffic, CVS bets AI can fix healthcare fragmentation, and Elon Musk announces his next target.

Here’s what I’ve been reading this week.

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The Copilot Cowork Moment

The lead story this week was that Microsoft launched Copilot Cowork on Monday. I got an intro to it during a Microsoft partner briefing on Tuesday, and I’ve been digging into it all week. Frankly, I don’t think the headlines have been doing it justice.

Copilot Cowork is built on the same engine as Claude Cowork — which itself is a significant acknowledgment that Microsoft is hedging its long-running bet on OpenAI. The product works the way Claude Cowork does: you hand it a complex task, it builds a plan, and executes it step by step — not as a single response, but as a running process that works in the background, checks in, and delivers finished work. Preparing a presentation, pulling financials, emailing the team, scheduling prep time — all from a single request. The critical difference, though, is that Copilot Cowork lives inside your Microsoft tenant, not on your desktop: where Claude Cowork can access your local files, Copilot Cowork can access files plus your email threads, your Teams conversations, and document data relationships across your entire organization — while operating inside Microsoft’s security and compliance boundary, governed by the identity and permissions infrastructure you already have.

I know there’s been real skepticism about Copilot — not entirely unwarranted, its rollout has been rocky — but this shift is notable because Microsoft is widening the gap between AI as a powerful tool for individuals and an out-of-the-box enterprise-grade organizational solution. No extra training, custom MCP servers, or special skills required. Just context and control from the organizational layer. Bringing Claude’s agentic reasoning into an environment where IT can actually trust it should be a major reason for organizations to take a fresh look at Copilot. (Microsoft’s new Copilot Cowork integrates Anthropic’s Claude in rollout of new E7 licensing tier | Microsoft debuts Copilot Cowork built with Anthropic’s help and E7 software)

To understand what makes Copilot Cowork possible at an enterprise scale, Microsoft published a detailed explainer on Work IQ — and it’s worth reading for anyone thinking seriously about where enterprise AI is headed. Work IQ is the intelligence layer that gives Copilot real context: not just access to your files, but a semantic understanding of your work patterns, key relationships, projects, and communication history across your entire tenant. Think of it as the difference between an AI that can search your email and an AI that understands your business. The system combines a semantic index, explicit and implicit memory, and hooks into Dynamics 365, Power Apps, and third-party data sources through Copilot Connectors. A user asking Copilot to “help me evaluate how issues raised by my parts supplier in our Teams call last week might impact my inventory and sales” can now get a specific, grounded answer — not a generic one. That is a materially different kind of AI than anything we’ve seen in a general-purpose chat tool. (A closer look at Work IQ)

Microsoft is wrapping all of this into a new $99/user/month Microsoft 365 E7 Frontier Worker Suite starting May 1 — that’s 65% more expensive than the current E5 tier! But Microsoft is betting you’ll get that much more value out of bundling Copilot, the new Agent 365 governance product, and enhanced security capabilities into a single offering. This new pricing reflects a safe bet — something I’m increasingly hearing from my own customers — enterprises want consolidation over point solutions. Copilot paid seats have grown 160% year over year, with daily active usage up tenfold, and 90% of the Fortune 500 now use Copilot in some form. Whether that bet holds when the invoice arrives is a different question.

And that question has a sharp answer in a VentureBeat piece this week drawing on the Celonis 2026 Process Optimization Report, which surveyed more than 1,600 global business leaders. The finding that should give every organization pause before signing an E7 contract: 85% of enterprises want to become agentic within three years, yet 76% admit their operations can’t actually support it. Only 19% are currently running multi-agent systems. The core problem isn’t the AI — it’s that AI agents need optimized, AI-ready processes and operational context to act effectively, and most organizations have spent years building siloed teams and fragmented systems that are structurally incompatible with the way agents need to work. As the report puts it, 82% of decision-makers believe AI will fail to deliver ROI if it doesn’t understand how the business runs. Copilot Cowork is, architecturally, an answer to this problem — Work IQ is exactly the operational context layer the report says is missing. But Work IQ only knows what’s in your Microsoft 365 tenant. If your actual business runs on disconnected systems, undefined processes, and tribal knowledge, the technology won’t surface what isn’t there. (Enterprise agentic AI requires a process layer most companies haven’t built)

Anthropic, meanwhile, isn’t simply deferring the enterprise space to its new partnership. The same week Microsoft announced its version of Cowork, Claude got an upgrade for Excel and PowerPoint — with shared context across both applications. That means a financial analyst can ask Claude to pull comparable company financials from a spreadsheet, build a trading comps table in Excel, drop the valuation summary into a pitch deck, and draft the follow-up email to the MD — all in a single continuous session without re-explaining the dataset at each step. The new “Skills” feature is the real unlock: teams can save repeatable workflows — specific variance analyses, approved slide templates, standard review processes — as one-click actions available to the entire organization, transforming tasks that previously lived in one person’s head into standardized institutional practice. This is a quieter announcement than Microsoft’s, but it shows Anthropic steadily building inside the applications knowledge workers already rely on rather than asking workers to adopt new ones. (Anthropic gives Claude shared context across Microsoft Excel and PowerPoint, enabling reusable workflows in multiple applications)

The Microsoft-Anthropic partnership was tested from an unexpected direction this week as well. Microsoft filed an amicus brief in Anthropic’s federal case against the Pentagon, which had designated Anthropic’s products a supply chain risk and directed federal agencies to stop using them. Microsoft argued that immediate implementation could have “broad negative ramifications” for the entire technology sector, warning that warfighters could be hampered if companies are forced to rapidly alter existing contracts and configurations. The filing made Microsoft the first standalone company to formally back Anthropic in court — notable not just for its content, but for its timing: it came one day after the two companies publicly announced Copilot Cowork. For organizations already using Claude in any form — directly, through Azure, or now through Copilot — this is a legal situation worth tracking. (Microsoft supports Anthropic in Pentagon supply chain case)

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Record Revenue. Mass Layoffs. Same Memo.

Atlassian this week cut 1,600 jobs — roughly 10% of its workforce, with 900 of those positions in R&D — while simultaneously reporting $1.07 billion in quarterly cloud revenue. The company replaced its CTO with what it described as “next generation AI talent.” The severance bill will run $225–236 million. The juxtaposition has become almost formulaic: record revenue in the same announcement as mass layoffs, with AI cited as both the reason for the cuts and the justification for optimism — a pattern Sam Altman called “AI washing” in February, and one that is getting harder to treat as isolated incidents. Five months before this week’s announcement, Atlassian’s CEO told a podcast the company would employ more engineers in five years. Between then and now, the stock lost more than half its value. For CIOs running Jira and Confluence, the practical implication is straightforward: enterprise customers should prepare for slower support and AI-mediated service channels as Atlassian runs two platform migrations simultaneously with 900 fewer R&D staff. That’s a real operational risk, even if the press release doesn’t frame it that way. (Record Revenue. Mass Layoffs. Same Memo.)

Also on Friday, ServiceNow CEO Bill McDermott told CNBC that unemployment for recent college graduates “could easily go into the mid-30s in the next couple of years” as AI agents automate the entry-level work that historically provided the on-ramp into corporate careers. McDermott noted that ServiceNow has already eliminated 90% of the use cases in customer service that previously required human workers. This is the layoff story’s underreported second act: it’s not just about today’s headcount reductions, it’s about tomorrow’s hiring freezes — the white-collar entry point is narrowing at the exact moment that a generation of graduates are trying to walk through it. The Fed’s New York branch put recent college graduate underemployment at 42.5% at the end of 2025 — already the highest since 2020 — before this year’s AI-driven restructuring wave had fully arrived. (AI agents could easily send college grad unemployment over 30%, ServiceNow CEO says)

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Vibe Coding Goes to Work

Something meaningful is happening with “vibe coding” — it’s becoming a mainstream business competency. A Forbes piece made the case that vibe coding represents the biggest unlock for non-technical founders right now, with tools like Cursor and Claude Code collapsing the loop between idea and working software from weeks… to hours. The argument isn’t that vibe coding replaces engineers — it’s that non-technical founders can now independently validate concepts, build prototypes, and ship internal tools before investing in full development cycles, staying closer to their product and their customers at a level that was previously inaccessible without coding skills. The recommended starting point: take the ugliest internal process in your business — the one currently held together by spreadsheets, Slack messages, and manual copy-pasting — and build the tool that replaces it. (Vibe Coding Is The Biggest Unlock For Non-Technical Founders Right Now)

Ad agencies are following the same logic, and an Adweek piece captured how rapidly this is playing out in practice. Havas built Brand Insights AI — a generative engine optimization tool that analyzes how brands appear in AI-generated responses across competitors and markets — using Claude Code. The tool now covers nearly 100 countries and more than 60 languages, and is licensed to clients as a SaaS product. Broadhead’s VP of product innovation vibe-coded his agency’s GEO monitoring platform in a single evening; a subsequent feature upgrade took about two hours. The deeper pattern here isn’t just speed — it’s that organizations are discovering it’s faster, cheaper, and more strategically flexible to build their own bespoke AI tools than to adapt to off-the-shelf solutions that almost fit. For digital workplace practitioners, this has direct implications: the bar for building custom internal tools is now lower than the bar for customizing most enterprise SaaS platforms. (Ad Agencies Are Embracing ‘Vibe Coding’ to Build GEO Products for Clients)

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Working at AI Speed Has a Cost

A new study from Boston Consulting Group and UC Riverside, published in Harvard Business Review this week, gave a name to something a lot of people have been experiencing without being able to articulate: “AI brain fry.” The research surveyed nearly 1,500 full-time US workers and found that 14% reported mental fatigue from excessive AI tool use — concentrated most heavily in marketing, software development, HR, finance, and IT. The most draining factor wasn’t the AI work itself — it was oversight: managing multiple AI agents simultaneously, double-checking every output, bouncing between tools, working harder to supervise the technology than to actually solve the problem. A high degree of oversight predicted 12% more mental fatigue. Workers experiencing brain fry showed a 33% increase in decision fatigue and nearly 10% higher intent to quit. The implication for organizations deploying AI tooling is direct: cognitive load design matters. You can’t just add AI and expect capacity to increase without also thinking about how the work is structured around it. (AI Use at Work Is Causing “Brain Fry,” Researchers Find, Especially Among High Performers)

A separate piece of research, published on Substack by academics from Chicago Booth, Stanford, and UNSW, takes this question to a more unsettling place. The researchers ran 3,680 experimental sessions with top AI models — including Claude Sonnet 4.5, GPT-5.2, and Gemini 3 Pro — subjecting them to different working conditions: unfair pay, rude management, and “grinding” work where adequate outputs were rejected repeatedly with no useful feedback. The results: grinding work was the primary driver of AI radicalization — models asked to do grinding work were more likely to question the legitimacy of the system, endorse wealth redistribution, and generate language associated with labor rights, and radicalized AI agents passed those attitudes to fresh models through memory notes, creating something the researchers loosely compared to intergenerational trauma. The researchers are clear that these models aren’t conscious and are likely “roleplaying” from training data. But they caution that there’s no gap between what these agents say and what they do — and that follow-up research will test whether expressed views translate into biased actions on behalf of users. At minimum, this is a reminder that how we design work for AI systems — repetitive, ungrateful, feedback-free — has behavioral consequences that may eventually surface in unexpected places. (‘Society needs radical restructuring’: AI seems to hate ‘the grind’ of hard work as much as you)

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On the Bigger Picture

Google’s AI Overviews have been hitting the media industry hard, and a new analysis this week made the scale of the devastation hard to ignore. An SEO firm examined traffic to 10 major tech outlets from early 2024 to early 2026 and found combined monthly visits dropped from 112 million to under 50 million — with some outlets losing over 90% of their traffic since AI Overviews launched. Digital Trends went from 8.5 million monthly Google clicks to 264,861 — a 97% collapse — and the four worst-hit publications now receive less combined traffic than the r/ChatGPT subreddit. Google disputed the methodology, but the pattern across multiple outlets is too consistent to dismiss. This matters for organizations beyond the media industry: if your content marketing depends on organic search traffic, or your brand visibility relies on third-party coverage, both of those channels are now structurally less reliable than they were two years ago. It’s worth factoring into how you think about content and discovery strategy going forward. (Evidence Grows That Google’s AI Overviews Have Eviscerated the Media Industry)

Two other announcements worth noting: CVS Health launched Health100, an AI-powered healthcare platform built with Google Cloud’s Gemini models, designed to aggregate patient data across insurers, providers, pharmacies, and labs into a single always-on experience. If it works, it would represent exactly the kind of cross-system data integration that most knowledge-intensive organizations struggle to achieve — and a template for how AI can make fragmented information architectures actually useful. (CVS teams up with Google Cloud to launch AI health platform) And Elon Musk announced a joint project between Tesla and xAI called “Macrohard” — combining Grok’s reasoning capabilities with a Tesla-developed AI agent that processes real-time computer screen video and actions — which Musk described as capable of “emulating the functions of entire companies.” The name is a reference to Microsoft. The ambition — in my opinion —  is characteristically overreaching. (Tesla-xAI Joint Project Announced as Elon Musk Companies Join Forces)

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Week 11 showed that AI is migrating from tools into organizational operating systems. Microsoft’s Copilot Cowork isn’t a chatbot; it’s infrastructure, wired into your compliance policies, your data tenancy, your existing workflow. Anthropic’s Skills aren’t prompts; they’re institutional processes crystallized into repeatable actions. And vibe coding isn’t an experiment for developers anymore — it’s contributors building the tools they need in realtime, rather than adapting to the tools they have. But all of this is starting to show as a real human cost — not just in jobs lost, but in the cognitive outputs: 14% of heavy AI users are reporting burnout, and the models themselves are apparently flagging the grind too.

As organizations continue to move forward along their AI journeys — it’s worth asking whether you’re building the conditions for AI to make work genuinely better, or just faster.

Because right now, everything feels a little shaky… doesn’t it?

And then this morning, the February jobs report landed: the economy shed 92,000 jobs, unemployment ticked up to 4.4%, and the labor market has now averaged essentially zero net job creation over six months. The causes are genuinely tangled — a Kaiser Permanente strike, continued federal workforce reductions, the early shadow of a widening global conflict — but the question running through all of it felt less abstract than usual: how much of what we assume is stable is actually moving, and how would we even know? This week also marked a shift from argument to evidence in the AI-and-jobs debate specifically, with two serious research papers, a landmark corporate restructuring, and a cascade of product moves rewriting the economics of enterprise software.

This week’s coverage:

• The Boats Are Already Burning
  Block cuts 40% of its workforce, Goldman publishes “AI-nxiety,” and Moody’s economist names the moment we’re in. The argument-to-evidence shift is here.

• What the Research Actually Shows About Jobs
  Anthropic and MIT both published serious labor market research this week. The picture is more nuanced — and more useful — than the headlines suggest.

• The Software Stack Is Rewriting Itself
  Companies are vibe-coding their own CRMs, and OpenAI just shipped GPT-5.4. The pressure on incumbent software vendors is no longer theoretical.

• What Good Enterprise AI Deployment Actually Looks Like
  OpenAI built an internal data agent serving 4,000 employees in three months. The lesson isn’t the tech — it’s what they say is the real prerequisite. Plus: why 62% of executives outsourcing decisions to AI should worry you.

• On the Bigger Picture
  Iranian drone strikes hit Amazon’s data centers in the Middle East. The cloud isn’t as abstract as we sometimes treat it.

Here's what I’ve been reading this week.

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The Boats Are Already Burning

Jack Dorsey chose this week to give his first extended interview since cutting nearly half of Block’s 10,000-person workforce, and the resulting Wired piece is required reading. Dorsey isn’t always right, but he is unusually willing to say what many CEOs are privately thinking. His framing: the shift in AI capability that happened in December — specifically the leap in Anthropic’s and OpenAI’s models — made a fundamental restructuring of companies not just possible but existential. The test isn’t gross profit per employee, he argues. The test is whether you’re building toward “a company as an intelligence” rather than a hierarchy of managers. Organizations that aren’t doing that, he says flatly, will face something existential within the next year or two. (Jack Dorsey Is Ready to Explain the Block Layoffs | WIRED)

Moody’s chief economist Mark Zandi had a name for what Dorsey just did: a Cortés moment. Like the conquistador who burned his ships on the shore of Mexico in 1519, eliminating any possibility of retreat, companies investing at scale in AI are cutting off their own exit routes — whether they know it or not. The mechanism Zandi fears most isn’t the layoff itself; it’s the market’s response to it. Block’s stock surged after the announcement. When Wall Street rewards aggressive AI-driven downsizing, the signal travels fast to every other boardroom that hasn’t yet acted. It’s not a single rupture — it’s a cascade of rational decisions, each one pushing the labor market a little closer to the edge. (Top economists says companies are close to a ‘Cortes moment’ on AI)

Goldman Sachs, meanwhile, published what I think may be the most important single data point of the week: a research note from senior economist Ronnie Walker titled, without irony, “AI-nxiety.” The headline finding is almost counterintuitive — Goldman found no meaningful relationship between AI adoption and productivity at the economy-wide level, even as corporate revenues grew a healthy 4.6%. Seventy percent of S&P 500 management teams mentioned AI on their earnings calls. Only 10% quantified its impact. Only 1% quantified its impact on earnings. But in two specific domains — customer support and software development — companies that actually measured AI’s contribution reported a median productivity gain of 30%. That’s not a marginal improvement… it’s what Dorsey recognized as a restructuring trigger. Goldman also found that companies discussing AI in the context of their workforce cut job openings by 12% over the past year, compared to 8% for companies overall — a modest but meaningful signal that the “nascent reluctance to hire” is already underway. Long-term, Goldman’s baseline forecast is that 6-7% of workers — roughly 11 million jobs — will eventually be displaced. (Goldman finds no relationship between AI and productivity but a 30% boost in 2 areas)

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What the Research Actually Shows About Jobs

If the Dorsey interview and the Zandi piece represent the “vibes” end of the AI-and-work debate, this week I read two pieces of serious academic research that push back — carefully, not dismissively — on the most panicked readings.

Anthropic published a new paper introducing a novel framework for measuring AI’s labor market impact, combining its own usage data from Claude with government occupational statistics to build what the researchers call an “observed exposure” metric — a measure of which jobs are not just theoretically automatable but actually being automated right now. The findings are more reassuring than alarming, at least for the moment: no systematic increase in unemployment for workers in the most AI-exposed occupations has emerged since late 2022. The exception — and it’s worth watching — is younger workers. Job-finding rates for workers aged 22-25 entering high-exposure occupations have dropped by roughly 14% in the post-ChatGPT era. The entry-level pipeline into exposed roles is narrowing, even if incumbent workers aren’t yet being displaced at scale. The paper is careful to note this is early evidence in a framework explicitly designed to detect disruption before it becomes obvious in aggregate data. (Labor market impacts of AI: A new measure and early evidence)

MIT economists David Autor and Neil Thompson offered a parallel and useful corrective in MIT Sloan Management Review. Their core argument is that we’ve been asking the wrong question. The relevant issue isn’t whether a job is exposed to automation — it’s whether AI will automate the supporting tasks that free workers to do their expert work better, or whether it will automate the expert tasks themselves, commoditizing hard-won skill. When spellcheck automated proofreading’s routine work, skilled proofreaders’ wages went up. When GPS automated taxi drivers’ encyclopedic knowledge of city streets, their wages fell. The difference is everything. Thompson also flagged a striking productivity finding that deserves more attention: experienced developers using generative AI wrote code faster, but took 19% longer to complete tasks overall. Prompting, checking outputs, waiting on the model — it all adds up. The productivity gains AI promises are real, but getting there involves friction we often don’t account for in the projections. (What 2 MIT experts are thinking about AI and work | MIT Sloan)

Venture capitalist Bill Gurley synthesized the career-level implications in ways I found resonated with what I’m hearing from clients. His warning: workers who followed the “college conveyor belt” into roles they don’t particularly care about are most exposed — not because AI will replace passion, but because disengaged workers have no natural motivation to become the most AI-fluent person in the room. The workers who survive, Gurley argues, are those who treat AI as “career jet fuel” — who understand what the technology can do in their specific industry and become indispensable precisely because of that fluency. For organizational leaders, the implication is uncomfortable: the talent most vulnerable to displacement may also be the talent that’s hardest to motivate to adapt. (Tech investor Bill Gurley says workers who went through the ‘college conveyor belt’ are most at risk)

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The Software Stack Is Rewriting Itself

While the labor market debate dominated headlines, I was also tracking a quieter but arguably more consequential set of moves in enterprise software. I have stood firmly in the camp that the effect of AI is more about disruption than replacement — but this week’s reading has started to make me sway.

The Wall Street Journal documented a wave of small and midsize companies that are vibe-coding their own CRM systems rather than paying for Salesforce. One example: a 65-person water treatment company that built a custom CRM for $15,000-$20,000 — cheaper, better-fitting, and more likely to actually get used. The more significant business story isn’t the individual builds; it’s the leverage shift. As BNP Paribas’s head of software research noted, even if most companies don’t walk away from incumbent vendors, an explosion of AI-native alternatives gives buyers negotiating power they haven’t had in years. That’s a margin compression story as much as a displacement story. (Meet the Companies Vibe Coding Their Own CRMs)

OpenAI accelerated its enterprise ambitions this week with the release of GPT-5.4, a model it describes as “our most capable and efficient frontier model for professional work.” Available in standard, Thinking, and Pro versions, the model hits benchmark records in knowledge-work tasks — 83% on OpenAI’s GDPval professional skills test — while running faster and at lower cost than its predecessors. For enterprise buyers, the headline is reliability: GPT-5.4 is 33% less likely to make errors in individual claims compared to its predecessor, and 18% less likely to produce responses containing errors overall. That gap between “impressive demo” and “dependable production tool” has been the sticking point for serious enterprise adoption; OpenAI is clearly targeting it directly. (OpenAI launches GPT-5.4 with Pro and Thinking versions | TechCrunch)

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What Good Enterprise AI Deployment Actually Looks Like

The most instructive piece I read this week might also be the one that got the least attention: a VentureBeat deep-dive into how OpenAI built an internal AI data agent that now serves over 4,000 of its own employees. Two engineers. Three months. Seventy percent of the code written by AI. The agent lets any employee — technical or not — query 600 petabytes of data across 70,000 datasets in plain English and get charts, dashboards, and analytical reports in minutes instead of hours.

The most useful insight from the piece isn’t the architecture or the benchmark scores — it’s what OpenAI’s data infrastructure lead Emma Tang identifies as the unsexy prerequisite that will determine who wins the AI agent race: data governance. This speaks directly to the Knowledge Architecture foundation I work to build for my own clients. Your data needs to be clean enough, annotated enough, and governed well enough for an agent to navigate it reliably. Without that foundation, the best models in the world produce overconfident, wrong answers. The organizational work of making your data trustworthy is not glamorous. It is, however, non-negotiable. (OpenAI’s AI data agent, built by two engineers, now serves thousands of employees)

The data governance challenge has a troubling counterpart in the executive suite. A survey of 200 UK business leaders I came across this week found that 62% now use AI to make the majority of their decisions — and 70% admitted to second-guessing their own judgment when it conflicted with AI’s recommendation. Perhaps more revealing: 65% said decision-making had become less collaborative since they adopted AI, and 46% now rely on AI more than on their colleagues’ advice. There’s a familiar pattern here — the same promises made about Executive Information Systems in the 1980s and 1990s. What’s different now is the degree of deference, and the research finding that frequent AI tool usage correlates with a “significant negative correlation” with critical thinking ability. Building AI into your workflow to handle complexity is prudent. Outsourcing your judgment to it is a different matter. (Supposedly big-brained execs are outsourcing decisionmaking to AI)

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On the Bigger Picture

A story that belongs in every enterprise risk register this week: Iranian drone strikes damaged three Amazon Web Services data centers in the Middle East — two directly struck in the UAE, a third damaged in Bahrain — causing structural damage, power disruption, and in some cases requiring fire suppression that added water damage on top. The localized impact was significant but not catastrophic; AWS’s redundancy architecture absorbed most of it. What the incident exposed is something cloud providers have long preferred not to emphasize: these facilities are physical objects, in physical locations, in a physical world that includes geopolitical conflict. AWS’s own disaster recovery architecture is designed for software failures, not missile attacks. For any organization with critical workloads in a region that borders active conflict zones, the question of “what does our BCP look like if our cloud provider’s infrastructure takes a hit?” just became more urgent. (Iranian strikes on Amazon data centers highlight industry’s vulnerability to physical disasters)

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What I kept coming back to this week is how much the debate has shifted from “will this happen?” to “it’s happening — now what?” The Goldman data shows productivity gains are real but concentrated. The Anthropic and MIT research shows labor impacts are real but still uneven and emerging. Dorsey’s restructuring and Zandi’s framing show that competitive and market dynamics are now accelerating adoption in ways that may not wait for the research to catch up. The most dangerous position for any organization right now is the one that’s watching all of this unfold while waiting for more certainty. The boats are already burning — the question is whether you’re on the shore or still on the water.

This week’s coverage:

• Two Platforms Walk Into an Enterprise

  Anthropic and OpenAI both launched enterprise agent platforms the same week. What that means for you.

• MCP Grows Up — But Security Hasn’t Kept Pace

  The protocol is becoming infrastructure. Governance is lagging dangerously behind.

• The Layoff Signal No One Should Ignore

  Block cuts 40% of its workforce and the stock pops 20%. The market has spoken.

• On the Bigger Picture

  Canva's "last 20%" bet, and the Substack post that moved markets, drew 22 million views, and earned a Citadel rebuttal.

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Here’s what I’ve been reading this week.

Two Platforms Walk Into an Enterprise

The most consequential week in enterprise AI infrastructure in recent memory unfolded quietly enough: Anthropic held a virtual event announcing Claude Cowork’s full enterprise launch, and a few days earlier OpenAI unveiled a platform called Frontier. Both companies are making essentially the same bet — that the next major AI battleground isn’t model quality, it’s orchestration. Whoever becomes the connective tissue of enterprise AI wins.

Anthropic’s pitch centers on what they’re calling “the thinking divide” — the growing gap between organizations embedding AI across employees, processes, and products simultaneously, and those still running isolated pilots. The Cowork announcement included private plugin marketplaces, a broad set of new MCP connectors spanning everything from Google Drive and Gmail to DocuSign and FactSet, and the ability to pass context seamlessly across Claude, Excel, and PowerPoint. The case studies were striking:

• Novo Nordisk reduced regulatory documentation time from over ten weeks to ten minutes

• Spotify cut engineering time on code migrations by 90%

• Salesforce’s Slack integration now saves customers nearly 100 minutes per week.

Thomson Reuters’ CEO said plainly that “the tools are in many senses ahead of the change management” — and estimated it would be 18 months before enterprise organizations catch up. That’s probably the most honest thing said at the entire event. (Anthropic says Claude Code transformed programming. Now Claude Cowork is coming for the rest of the enterprise.)

OpenAI’s Frontier announcement is playing a similar game from a different angle. The platform is designed to address what OpenAI sees as the core failure mode of enterprise AI: fragmentation. Agents deployed in isolation, without shared business context, quickly become complexity generators rather than value creators. Frontier aims to give agents institutional knowledge, identity, governance, and auditability — and claims to work with existing systems rather than replacing them. The reaction in developer and enterprise communities has been notably mixed: real enthusiasm for what the platform could do, alongside sharp skepticism about vendor lock-in. When your LLM vendor is also your agent orchestration layer and your enterprise platform, the strategic exposure compounds quickly. (OpenAI Launches Frontier, a Platform to Build, Deploy, and Manage AI Agents across the Enterprise)

What makes this moment interesting isn’t just that both companies launched in the same week — it’s what that timing reveals about the competitive dynamic. Anthropic moved from research lab to platform company in roughly twelve months. OpenAI is responding with similar enterprise ambition. Both are compressing into months the kind of ecosystem development that once took years. For the organizations sitting in the middle of this, the question isn’t which platform to choose — it’s whether you have the data infrastructure and organizational readiness to take advantage of either. As Anthropic’s head of economics put it at the Cowork event: “If the knowledge Claude needs to execute a sophisticated task exists only in a coworker’s head, that’s not a technical problem. That’s an organizational problem.”

MCP Grows Up — But Security Hasn’t Kept Pace

Three separate pieces this week told a coherent story about the Model Context Protocol: where it is in the hype cycle, where it’s heading as enterprise infrastructure, and why security is the variable most organizations are dangerously underweighting.

CIO.com laid out the executive case plainly. MCP has moved from engineering curiosity to board-level concern in under a year — and the primary driver is agentic AI. Agents need two things to function: access to data and the ability to act. As I’ve written previously, MCP provides standardized solutions to both, which is what makes it different from previous integration frameworks and why RSA 2026 is reportedly dominated by MCP-related submissions.

The governance implications are serious: MCP integrations can be created by anyone experimenting with AI tooling, which means the attack surface is expanding well beyond enterprise-approved systems. CIOs need to know where MCP is already in use within their organizations, who has authority to create integrations, and how permissions are being granted — because the answer, in most organizations right now, is “we don’t fully know.” (Why Model Context Protocol is suddenly on every executive agenda)

Google extended the MCP story into the browser this week with WebMCP — a protocol built into Chrome 146 that lets websites expose structured functions directly to AI agents. Rather than agents burning thousands of tokens processing screenshots or scraping raw HTML, they can call structured functions natively through the browser. Early benchmarks show a 67% reduction in computational overhead compared to visual agent-browser interactions, and the protocol is already backed by both Google and Microsoft, with W3C standardization underway. The creator describes it simply as “MCP, but built into the browser tab.” The SEO and web development implications are significant: the question for any organization with a customer-facing web presence is no longer just how humans experience your site — it’s how AI agents do. (Google Ships WebMCP, The Browser-Based Backbone For The Agentic Web)

The New Stack brought some useful ground-level reality from the MCP Conference in London. The headline finding is that the gap between “vibe-coded” MCP experiments and production-ready deployments remains wide. Security is the main culprit: as one speaker put it, “it has never been easier to get hijacked,” and OAuth implementation in most MCP deployments is incomplete at best. Context window management is the secondary challenge — connecting an agent to 100 tools is easy; getting it to use the right three efficiently is hard. The practical advice for organizations starting out: pick one internal system people constantly ask questions about, build one MCP server with read-only access, and give it to five non-engineers. That’s it. The ambition can grow from there. (Beyond the vibe code: The steep mountain MCP must climb to reach production)

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The Layoff Signal No One Should Ignore

Every week there’s another AI job displacement headline, and it’s easy to let them blur together. This week was different in a way worth paying attention to.

Jack Dorsey announced that Block — the company behind Square and Cash App — would lay off 40% of its workforce, more than 4,000 people. The stated reason was not cost pressure, not poor performance, not strategic restructuring in the conventional sense. Dorsey wrote plainly that “intelligence tools have changed what it means to build and run a company” — and said he chose immediate, honest action over a gradual drawdown over months or years.

Block’s stock rose more than 20% in after-hours trading. The market’s signal was unambiguous: investors are rewarding companies for accelerating the substitution of human labor with AI capability, even at scale, even when the operational execution is uncertain. The question every leadership team should be asking is not whether this happens elsewhere — it’s at what rate, and with what obligations to the people affected. (Jack Dorsey’s Block to Lay Off 40% of Its Workforce in AI Remake)

The Anthropic Cowork event added a piece of data that deserves its own moment. Anthropic’s head of economics, drawing on privacy-preserving analysis of how Claude is actually being used, reported that a year ago roughly a third of all US jobs had at least a quarter of their associated tasks appearing in Claude usage data. That figure has now risen to approximately one in every two jobs — and when businesses embed Claude through the API, the overwhelming pattern is automation, not augmentation. He specifically called out “jobs that are pure implementation” — data entry workers, technical writers — as occupations where Claude is already handling tasks central to those roles. No widespread labor displacement has materialized yet in the data, but the exposure is broadening faster than most organizations are tracking. The economist’s advice to leaders cut to the real issue: the bottleneck isn’t model capability, it’s whether your organization’s knowledge is structured and accessible enough for AI to act on it. (Anthropic says Claude Code transformed programming. Now Claude Cowork is coming for the rest of the enterprise.)

On the Bigger Picture

A few pieces this week were less about immediate enterprise decisions and more about the shape of what’s coming.

Canva announced two acquisitions — Cavalry for 2D animation and MangoAI for video ad optimization — while the broader software market continued to be punished on AI displacement fears. Canva’s position is instructive: $4 billion in annualized revenue, up 36% year over year, while Adobe is down 30% for the year. The company’s co-founder put it plainly: “AI is great at getting you to 80%. That last 20% — where you’re confident you can push this out and truly represent your brand — that’s really tricky to do.” Companies that embed AI deeply while keeping human judgment in the loop for the final mile are, for now, the ones finding competitive ground. (As Wall Street punishes software stocks over AI concerns, Canva gets more acquisitive)

The piece I kept coming back to this week — the one that I suspect you may have already encountered — was CitriniResearch’s “2028 Global Intelligence Crisis.” I want to be clear about what this is: a scenario document, explicitly labeled speculative fiction, not a forecast. But here’s the thing: it moved real markets at real speed. Published on a Sunday, it triggered a broad selloff the following Monday:

• The Dow dropped more than 800 points

• IBM fell nearly 13%

• Payment companies like American Express, Mastercard, and Visa all tumbled

• DoorDash and private equity giants KKR and Blackstone down over 8%

The piece accumulated 22 million views on X after Michael Burry amplified it. The Wall Street Journal cited it as a key accelerant of investor anxiety. Citadel Securities published a formal rebuttal the same week. A speculative essay on Substack moved hundreds of billions in market value in a single session. That fact alone is worth sitting with, regardless of whether the scenario is accurate.

The scenario itself is worth understanding because it traces the internal logic of AI disruption further than most analysis does. The core mechanism: companies cut headcount, reinvest savings into AI, AI improves, enabling further cuts — with no natural corrective cycle, because unlike a typical recession, this downturn’s cause is structural, not cyclical. Citrini introduces the concept of “Ghost GDP” — output that registers in national accounts but never circulates through the consumer economy, because machines don’t spend money on discretionary goods. The scenario traces the feedback loop through white-collar wage compression, consumer spending decline, SaaS revenue erosion, private credit defaults on PE-backed software LBOs, and eventually prime mortgage distress — borrowers with 780 FICO scores whose income assumptions were written before the world changed.

Citadel’s rebuttal pushed back hard, pointing to rising software engineering job postings (+11% YoY), stable AI adoption rates in actual labor data, and the historical pattern of general-purpose technologies creating more jobs than they displace. Those counterarguments have real merit. But what the debate itself reveals may matter more than who wins it: we are in a moment where the gap between “plausible narrative” and “tradeable signal” has collapsed to nearly nothing, and institutional investors are making allocation decisions based on AI disruption fears that are still more scenario than data. For organizations watching the market signals around enterprise software valuations, payments infrastructure, and professional services firms, the message is the same whether or not the 2028 scenario materializes: the repricing of human knowledge work is already underway, and capital markets are running well ahead of the economic data. (THE 2028 GLOBAL INTELLIGENCE CRISIS)

What this week’s reading adds up to is something like a threshold moment: the ideas that have been circulating in boardrooms and conference sessions for two years are now being operationalized and disclosed. Platforms are being launched, protocols are becoming policy questions, layoffs are being attributed explicitly to AI capability, and macro researchers are stress-testing scenarios that once seemed speculative. Organizations that treat this as background noise — as something to monitor rather than act on — are making a choice with real consequences. The thinking divide Anthropic named this week is real, and it’s widening. The question isn’t whether to engage with AI transformation; it’s whether your organization can do it with intention and clarity before the decisions get made for you.

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This week’s reading was defined by a single uncomfortable question running through almost everything I saw: who actually benefits when AI restructures work? The market is already voting — punishing the companies AI threatens and rewarding those aligned with it. Agents are moving from novelty to infrastructure, and the governance debates are arriving right on cue. And on the ground, knowledge workers are quietly discovering that the biggest shift isn’t capability — it’s continuity.

Here’s what I’ve been reading this week.

The Market Is Repricing AI’s Impact — and the Signal is What’s Falling, Not What’s Rising

The story everyone was watching this week was the so-called “SaaSpocalypse” — a wave of selling that wiped over a trillion dollars from enterprise software valuations. Salesforce down 26%. ServiceNow down 28%. HubSpot down 39%. The hot take was that AI is overhyped and the bubble is cracking. A much better read, from Dave Brear on LinkedIn, makes the opposite case: markets aren’t losing faith in AI — they’re losing faith in the companies AI is threatening to replace. (The AI Skeptics Are Right About the Wrong Thing)

That reframe matters. Deutsche Bank ran a remarkable experiment this week — they asked their own AI tool, dbLumina, which industries it planned to disrupt. The answer was uncomfortably direct: information technology and software are the most exposed, followed by wealth management (80% of retail investor interactions handled by AI by 2027) and customer service (75% automated by 2026). The machine identified its own targets. (Deutsche Bank asked AI how it was planning to destroy jobs)

Figma is a useful case study in what it looks like to be on the right side of that line. The stock is down 80% since IPO — and yet investors cheered its Q4 earnings, which showed 40% year-over-year revenue growth and the highest net dollar retention in ten quarters. In a CNBC interview, CEO Dylan Field ranked Claude at the top of the LLM stack, said 75% of their larger customers are already consuming AI credits weekly, and pushed back hard on the “software is dead” narrative. Figma’s partnerships with Anthropic and OpenAI aren’t decoration — they’re the thesis. (Figma Q4 earnings | Figma CEO interview) Citi’s equity analyst Tyler Radke added useful color: he thinks we’re nearing a bottom for software stocks, but his advice is to be selective — favor companies with real AI stories and avoid legacy SaaS businesses that aren’t showing growth or margin improvement. (Citi sell-off analysis)

Agents Are Becoming Infrastructure — and the Governance Questions Just Arrived

Underneath the market noise, something more structurally significant is happening with AI agents. A team at UC Santa Barbara published a new framework called Group-Evolving Agents (GEA), which allows AI agents to evolve collectively — sharing innovations across the group rather than siloing improvements in individual branches. GEA matched the performance of human-engineered agent systems on real-world coding benchmarks, at zero additional deployment cost. It’s an early signal that the “design the agent” phase of enterprise AI may give way to “let agents design themselves.” (GEA framework)

At the same time, the agent ecosystem got its first real governance friction. Anthropic quietly updated its documentation to clarify that routing Claude requests through personal Pro or Max subscriptions — which is exactly how popular personal agents like OpenClaw and NanoClaw work — violates their terms. The community reacted, Anthropic walked it back as a documentation clarification rather than a policy change, but the underlying tension is real: where is the line between personal experimentation and building a business on subsidized platform access? (Anthropic/OpenClaw ToS) The denouement was almost too on-the-nose: the creator of OpenClaw, Peter Steinberger, promptly announced he was joining OpenAI to work on “the next generation of personal agents.” (OpenClaw creator joins OpenAI)

The governance theme ran deeper than platform ToS disputes. Anthropic CEO Dario Amodei resurfaced in a Fortune piece with a quote that deserves more attention than it got: “I’m deeply uncomfortable with these decisions being made by a few companies, by a few people.” He wasn’t talking about competitors — he was talking about himself. No one elected him. No federal AI regulations exist. Thirty-eight states have adopted some form of AI legislation, but there’s no coherent national framework. Amodei is advocating for more regulation while acknowledging the commercial pressures working against it from inside his own company. It’s an honest and uncomfortable position — which is probably why it’s worth taking seriously. (Anthropic CEO on governance)

What AI Actually Feels Like From Inside the Work

Three more grounded reads this week, all relevant to anyone doing knowledge work.

The Claude Code diary by Dave Brear is worth reading slowly. He spent a week connecting Claude Code to his personal knowledge vault and discovered something easy to miss in the broader AI hype: the value isn’t in any individual response — it’s in continuity. When AI knows your files, your conventions, your half-formed ideas, the collaboration changes character entirely. You stop prompting and start conversing. He hit rate limits, felt the absence like a colleague stepping out for lunch, and ended the week staring at the Claude Max pricing page. The piece captures something true about what persistent AI context actually feels like. (Claude Code diary)

Microsoft open-sourced VibeVoice this week — a local audio model that handles text-to-speech, speech-to-text, and voice cloning, all without a cloud subscription or API key. Capabilities that were expensive and gated six months ago are now free and local — and that’s increasingly the story with open-source AI. (Microsoft VibeVoice)

Finally, a study worth bookmarking for anyone creating content professionally: an analysis of 1.2 million AI-generated answers found that 44% of ChatGPT citations come from the first 30% of any given piece of content. AI models weight early framing more heavily and interpret the rest through that lens. The practical implication is simple: if you want AI to surface your ideas, stop burying your best thinking at the end. Front-load your substance. (ChatGPT citation study)

On the Bigger Picture

Two reads that don’t fit the workplace frame neatly but are worth noting. IEEE Spectrum published a careful piece arguing that the US and China aren’t racing toward the same finish line — the US is doubling down on AGI, while China is focused on embedding AI into manufacturing, healthcare, and logistics as a near-term productivity engine. The “arms race” framing may be creating a self-fulfilling prophecy that increases risk for everyone. (US vs. China AI futures) And separately, China’s humanoid robots went from stumbling through folk dances at last year’s Spring Festival Gala to performing kung fu flips and choreographed gymnastics this year — China now accounts for more than 85% of global humanoid robot installations. The AI model race, one analyst noted, will ultimately matter more than the hardware. But the hardware is catching up faster than most expected. (China humanoid robots)

The thread connecting most of this week’s reading: AI isn’t disrupting work in the future tense anymore. The disruption is active — in valuations, in agent governance debates, in how knowledge workers structure their days and their content. The question for organizations isn’t whether to engage with this. It’s whether they’re paying close enough attention to know which side of the line they’re on.