I’ve written before about what AI means for K–12 education — the anxiety in the room when teachers realize their students are already living in a world the curriculum hasn’t caught up to yet.

Higher ed has a different problem. And in some ways, a significantly harder one.

Because the issue isn’t that colleges are failing. Most are doing exactly what they’ve always done — and doing it well. Critical thinking. Research. Writing. Analysis. Communication. The fundamentals of an educated mind.

The issue is that the world those graduates are stepping into has fundamentally changed what it needs from them. And the gap between what a diploma signals and what a business now demands has never been wider.

That gap has a name. And it’s worth being honest about what’s living inside it.

The Jobs Are Gone. Not Shrinking. Gone.

For decades, the implicit deal between colleges and employers was straightforward. Businesses hired recent graduates for task-driven knowledge work — the kind that required literacy, attention to detail, and the ability to follow a process. Research. Filing. Drafting. Reviewing. Organizing. Formatting. Structuring. The unglamorous but essential connective tissue of how organizations operated.

Those jobs were the on-ramp. The place where new graduates learned the business, built context, proved themselves, and eventually moved up.

AI ate the on-ramp.

Not gradually. Not partially. Completely. Researching a topic, drafting a first document, building a summary deck, formatting a report, organizing a database — all of it is now faster, cheaper, and increasingly more reliable when delegated to AI than when assigned to a 22-year-old three months out of school.

This is not a trend. It is not a disruption that will stabilize. It is a permanent restructuring of what entry-level knowledge work looks like — and what it requires.

The floor didn’t just rise. It shifted entirely.

What Businesses Are Actually Asking For

Here’s what I hear when I talk to business leaders — and I talk to a lot of them.

They’re not struggling to find people who can complete tasks. They have AI for that. What they cannot find — and cannot train fast enough — are people who know what to do before the task begins. People who can look at a messy, ambiguous problem, decide what question is actually worth asking, direct AI to help answer it, evaluate what comes back with genuine skepticism, and communicate a decision with clarity and accountability.

That’s not an AI skill. That’s a human skill that AI has made non-negotiable.

The irony is sharp: AI has made the outputs of knowledge work cheaper and faster — while simultaneously raising the bar for the judgment behind them. The work product is easier to produce. The discernment required to know whether it’s right has never been more valuable.

And colleges — through no fault of their own — have not been asked to teach that discernment in the context of an AI-driven business environment. Because… until now, there was no urgency to.

The Real Curriculum Gap

Let me be precise about what I’m not saying.

I’m not saying colleges need to become vocational schools. I’m not saying the liberal arts are obsolete or that the fundamentals need to be replaced. If anything, I strongly believe the opposite is true — the skills that a rigorous college education builds are exactly what the AI economy most desperately needs.

Critical thinking. Synthesis. Written precision. The ability to argue from evidence and know when you’re wrong.

The gap isn’t in the skills. It’s in the scaffolding.

Scaffolding — in the architectural sense — is a temporary structure that lets workers reach places they couldn’t otherwise access. It’s not the building. It’s the bridge between where you are and where you need to be.

That’s what’s missing for college graduates entering the workforce today. The raw materials are there. The foundational skills are real. What doesn’t exist is any structured preparation for how to apply those skills in an environment where AI is changing how organizations think, operate, communicate, and make decisions.

Knowing how to write a strong argumentative essay is excellent preparation for the workforce. Knowing how to evaluate whether an AI-generated business case contains a flawed assumption — that’s the skill that gets you in the room.

Colleges are building the structural foundation. Without the scaffolding, grads are left on the ground floor.

What Scaffolding Actually Looks Like

This isn’t about teaching students to use ChatGPT. Every student already knows how to use ChatGPT. The tool fluency isn’t the problem.

The problem is that no one has taught them how to think alongside these tools in a professional context — how to frame the problem before they prompt, how to interrogate the output before they trust it, how to own the judgment call when the machine can’t make it for them.

Four skills sit at the core of that scaffolding:

Problem framing — the ability to define what actually needs solving before handing it to a machine. AI is extraordinary at answering questions. It is far less good at figuring out which question is worth asking. That still requires a human to guide the process.

Critical evaluation — the discipline to interrogate AI output rather than accept it. To notice when something sounds authoritative but contains a subtle error. To know when the confidence of the answer should make you more suspicious, not less.

Synthesis over retrieval — the capacity to connect ideas across disciplines and arrive at an original insight. AI retrieves. It aggregates. It can even summarize well. AI is remarkably good at making connections across sources. What it is less equipped to do on its own is extend them — to shift the frame, force the alternative, or see the problem from a fundamentally different angle. That kind of synthesis tends to emerge from human interrogation: pushing back on the output, reframing the question, bringing context the model wasn’t given.

Precision communication — the ability to direct AI clearly and communicate judgment calls to stakeholders with accountability. In a world where words are now instructions — where how you prompt determines what you get — the ability to say exactly what you mean has never been more consequential.

These are not new skills. They are the skills a rigorous higher education has always built. What’s new is the context in which they must operate — and the urgency with which they must be applied.

The Unexpected Upside

Here’s what makes this genuinely exciting, rather than just alarming.

The graduates who develop these skills won’t just be better employees. They’ll be the people those organizations most need right now — because contrary to what we hear in the media (shocking, I know) — most businesses haven’t figured out how to deploy AI effectively yet either.

The entry-level knowledge worker who understands how to evaluate where AI can add value, how to identify which processes are ripe for automation, and how to communicate that case to leadership — that person isn’t competing with AI. That person is running it. And they’re worth far more than the task-executor they replaced.

The on-ramp didn’t disappear. It was rebuilt one level higher.

Which means colleges have a genuine opportunity here — not to chase a technology trend, but to do what they’ve always done best: prepare students to navigate a world that is more complex and more demanding than the one that came before it.

The skills are already in the building. The scaffolding just needs to be added.

What I’m Calling For

I don’t believe the answer is a new AI elective buried in the back of the course catalog. That’s a hedge, not a strategy.

What I’m proposing is a curriculum thread — a deliberate, discipline-spanning layer that teaches students how to apply the foundational skills of a college education in an AI-driven professional environment. Not instead of what’s already being taught. On top of it. Scaffolding on the building that already exists.

This thread could begin in freshman writing — teaching students to interrogate AI output the same way they’re taught to interrogate a primary source. It could continue through discipline-specific coursework, where the grade is on the judgment behind the AI, not the output it produced. It could culminate in a senior capstone that asks students to run a real project with AI as a working resource — directing it, evaluating it, and standing behind every decision it supported.

The liberal arts college that does this won’t just be producing graduates who can survive the AI economy. It will be producing the people who shape it.

That is exactly what these institutions have always been for.

The gap between what a diploma delivers and what businesses now demand is real — and it’s widening. But it’s not permanent. The skills are already there. What higher education can provide now is the scaffolding to put them to use. That’s not a disruption to the mission of a college education. It’s the next chapter of it.

It seems two things are happening at once: the broader economy is tightening, and the biggest technology companies are spending faster than ever on AI. That paradox ran through nearly everything I read last week — from boardrooms replacing their CEOs to private equity firms writing nine-figure checks to force AI into portfolio companies. The pressure to act on AI is intensifying even as the evidence that it’s working at scale remains thin.

Here’s what I was reading.

This week’s coverage:

The Leaders Who Can’t Wait and the Leaders Who Won’t Stay
Boards are replacing CEOs for not moving fast enough on AI — even as 90% of executives say AI hasn’t changed their operations at all.

The Workforce Is Being Restructured, and So Is Its Data
Microsoft and Meta cut tens of thousands of workers while spending hundreds of billions on AI — and those workers’ Slack messages and emails may end up as training data.

The End of the Software License and Who’s Coming to Replace It
A new HBR framework says the era of standardized enterprise software is ending — and a massive private equity play shows who’s planning to profit from what replaces it.

The Platform Wars Go Private and Agentic
Google Cloud Next ‘26 declared the agentic era with 260 announcements; the real story is that enterprise AI infrastructure is moving out of public clouds and into private, controlled environments.

On the Bigger Picture
AI is finding security bugs in Firefox at scale, GPT-5.5 is already outperforming GPT-5.4, and the Justice Department can’t quite decide what it thinks about Anthropic.

Here’s what I was reading.

The Leaders Who Can’t Wait and the Leaders Who Won’t Stay

Last week produced another round of high-profile CEO departures, and the pattern is hard to miss. In 2025, companies in the S&P 1500 named 168 new CEOs — the highest total in more than 15 years. Adobe’s longtime CEO stepped down after 18 years under investor pressure to deliver on AI. At Walmart, Doug McMillon cited the urgency of AI transformation as part of why he stepped aside. Now Tim Cook is handing Apple to John Ternus. (The AI era is turning Corporate America into a CEO churn machine)

The core tension here: boards are replacing leaders for not moving fast enough on AI, even though the evidence that AI is actually delivering results remains remarkably thin. A survey of 6,000 executives cited in Fortune last week found that 90% say AI has had no impact on employment or productivity over the past three years — yet those same executives forecast it will increase productivity by 1.5% over the next four years. Boards aren’t firing people because AI isn’t working. They’re firing them because they’re not performing AI fast enough in front of investors who believe it should already be working. (Tim Cook’s exit is part of a CEO reckoning sweeping Corporate America)

There’s a useful distinction in the Fortune piece worth carrying into your own organization: transformation is not a turnaround. In a turnaround, you bring in an outsider to blow things up. In a transformation, you want to accelerate change without destroying what you’ve built. The companies making the most visible CEO moves last week — Apple, Walmart, Coca-Cola — are handing the wheel to insiders who know the business. The bet isn’t on a new vision; it’s on someone who can execute the existing one faster. If you’re an IT or business leader watching this, the takeaway isn’t about hiring. It’s about velocity: how quickly can you demonstrate measurable AI progress to whoever is watching your work?

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The Workforce Is Being Restructured, and So Is Its Data

Meta confirmed last week it will cut roughly 8,000 employees — about 10% of its workforce — while closing 6,000 open roles and spending between $115B and $135B on AI this year. The same week, Microsoft announced voluntary buyouts for about 8,000 employees, targeting those whose age plus years of service equals 70 or more. Microsoft’s CEO has said AI already writes 30% of the company’s code. Its AI chief said AI will be able to replace most white-collar work within 12 to 18 months. Block, Amazon, and Oracle have made similar moves in recent months. (Microsoft and Meta announce large staff reductions as they spend big on AI)

The math being presented here is direct: AI spend goes up, headcount goes down. The productivity argument makes sense on paper. What the quarterly earnings presentations don’t include is the operational risk of moving this fast — the institutional knowledge that walks out the door, the morale impact on people who remain, or the technical debt that accumulates when AI-generated code replaces engineers who understood why decisions were made.

There’s a darker layer to this story that got less attention last week. A Forbes piece revealed that a startup called SimpleClosure is helping defunct companies sell their internal digital footprints — Slack archives, emails, Jira tickets, code repositories — to AI labs as training data for agents. The demand, according to their CEO, is “insane.” A competitor called Sunset is in the same business. The implication is striking: the workplace communications of companies that didn’t survive are becoming the raw material for AI agents designed to do that work in the future. The privacy questions are significant — employees didn’t consent to their messages being repurposed — and regulatory scrutiny is beginning. (AI’s New Training Data: Your Old Work Slacks And Emails)

For organizations building AI adoption strategies, this is worth pausing on. The data your organization generates every day — how people communicate, how decisions get made, what questions get asked — is increasingly what makes AI systems valuable. How you govern that data, and who has rights to it, isn’t just a legal department concern anymore. It’s a strategic one.

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The End of the Software License and Who’s Coming to Replace It

HBR published a piece last week that named something I’ve been watching from my own work with clients: the economic logic that made standardized enterprise software the default choice is breaking down. Enterprise spending on generative AI applications jumped from $1.7 billion in 2023 to $37 billion in 2025. At the same time, public SaaS valuations have compressed sharply, with many leading vendors trading 30 to 60% below their 2021 peaks. The framework the piece proposes gives organizations four paths forward: build your own AI-driven systems, configure flexible platforms, collaborate with vendors to create tailored solutions, or simply buy the outcome and let someone else run it. The strategic question every IT and business leader now faces is which workflows genuinely need to be owned, and which can be delegated. (The End of One-Size-Fits-All Enterprise Software)

This isn’t just theoretical. Private equity figured out the same thing — and made a $5.5 billion bet on it. The Financial Times reported last week that OpenAI is in talks to invest $1.5 billion in a joint venture called DeployCo, alongside TPG, Bain Capital, Advent International, and Brookfield, which would contribute another $4 billion. The venture’s job: send “forward-deployed engineers” armed with OpenAI’s tools into portfolio companies to drive AI adoption and boost margins. Anthropic is in parallel talks with Blackstone, Hellman & Friedman, and General Atlantic for essentially the same model. (Private equity courts OpenAI and Anthropic)

The forward-deployed engineer model isn’t new — Palantir has used it for years. What’s new is the scale and the urgency. For organizations still figuring out where to start with AI, this is a clear signal: the companies that will profit most from AI adoption are betting on embedding themselves directly in your operations, not selling you a license and walking away. If you don’t have your own AI strategy and roadmap, someone else will hand you one.

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The Platform Wars Go Private and Agentic

Google Cloud Next ‘26 took place in Las Vegas last week and was substantial. Google announced 260 updates over the course of the event. The headline was the Gemini Enterprise Agent Platform — a full-stack system for building, deploying, governing, and running AI agents at scale — alongside a new eighth-generation TPU split into two specialized chips, one for training and one for inference. Google also announced a $750 million fund to help its 120,000-member partner ecosystem build and deploy agents. Seventy-five percent of Google Cloud customers are already using its AI products; the pitch is that the next competitive battleground isn’t compute access, it’s which platform becomes the operating system for your agent workforce. (Welcome to Google Cloud Next ‘26)

A separate story last week illustrated where this is heading for regulated industries. A company called Cirrascale — working with Google — is now offering Gemini as a fully private, air-gapped appliance that runs on a single server inside a customer’s own facility. The model lives entirely in volatile memory: cut the power, and it’s gone — no data ever leaves, no model weights ever persist on disk. For organizations in financial services, healthcare, or government that have been sitting on the AI sidelines because of data sovereignty or compliance requirements, this changes the calculus. The binary choice between “use the best AI and expose your data” versus “host an inferior open-source model and keep control” is starting to close. (Google’s Gemini can now run on a single air-gapped server — and vanish when you pull the plug)

The AWS Bedrock story rounds this out. At the MCP Summit in New York last week, AWS’s Luca Chang explained how Amazon’s contributions to the Model Context Protocol — the emerging standard for connecting AI agents to enterprise tools and data — grew directly out of customer gaps. MCP is fast becoming the connective tissue of the enterprise agent layer: a standard protocol that lets AI agents plug into your existing systems. Organizations that start building their agent workflows on MCP now will have a head start when the next generation of tools arrives — and it will arrive quickly. (How AWS Bedrock is shaping Model Context Protocol)

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On the Bigger Picture

The most practically significant piece I read last week wasn’t about strategy or spending — it was about security. Mozilla announced that Firefox 150 includes fixes for 271 vulnerabilities identified using early access to Anthropic’s Mythos model. What matters isn’t just the number. It’s what Mozilla said about the experience: it required significant resources and discipline to manage the volume of bugs the AI could surface. The uncomfortable implication is that if AI can find 271 security flaws in a major browser in weeks, the same capability will be in attackers’ hands shortly. If your organization runs Microsoft 365, SharePoint, or any aging enterprise application stack, this is worth a direct conversation with your security team. (Mozilla Used Anthropic’s Mythos to Find and Fix 271 Bugs in Firefox)

The model race kept moving. Lovable, the AI development platform, published benchmark results last week from its early access to GPT-5.5. On the hardest tasks, GPT-5.5 outperformed GPT-5.4 by 12.5%, made 23% fewer tool calls, and cost about 15% less per session. For teams evaluating which models to build processes or products on: the improvement curve is still steep, and locking into any single implementation without a plan to migrate is a growing risk. (Testing GPT-5.5 in early access: what we are seeing so far)

The Anthropic regulatory situation took another unusual turn. The Justice Department asked a California judge to pause its own appeal of the Anthropic case — a procedural move driven by the fact that federal agencies are simultaneously trying to get access to Anthropic’s Mythos model for government use. Trump told CNBC last week that Anthropic executives are “very smart people” and that a deal is “possible.” The government is, in effect, both suing Anthropic and trying to use its AI. This probably won’t be the last time an enterprise leader faces a version of the same dynamic: the vendor you’re most concerned about is also the one you can’t afford not to use. (Justice Department asks California judge to pause its Anthropic appeal)

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What last week made clear to me is that the pressure to act on AI has become almost completely decoupled from the evidence that acting is working. Boards are removing leaders who can’t show velocity. Companies are cutting people while building infrastructure. Private equity is mobilizing billions to force AI deployment into organizations that would otherwise move more slowly. The urgency is real — but it’s being driven by investor narrative and competitive anxiety more than by outcomes anyone can actually measure. That’s not an argument to slow down. It’s an argument to be intentional: clear about what you’re measuring, honest about what problem you’re solving, and disciplined enough to separate the speed your organization needs from the speed someone else wants you to perform.

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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You’d think we’d be seeing organizations cutting back — and they are, sort of. Hiring is basically flat, while enterprise AI investment continues to go up. But there’s Increasing pressure to show returns on what they’ve already spent. The challenge is that the way most of companies are organized — the culture, the structure, the management layer — isn't set up to use it well. That gap keeps showing up differently in every article I read — in the org chart that hasn’t moved, in the governance framework that doesn’t reach where the models are actually running, in the demand metrics measuring the wrong thing entirely.

Here’s what I was reading.

This week’s coverage:

The Structure Isn’t Ready and the Employees Already Know It
The KPMG data on organizational adaptability is uncomfortable reading — and the HBR case study on BBVA suggests that the employees who can’t wait for the org chart to catch up have already built around it.

Enterprise Software’s Most Comfortable Assumptions Are No Longer Safe Three structural forces are cracking the SaaS business model, and an internal OpenAI memo made clear that even its most important partnership is now a competitive constraint.

The Governance Layer Doesn’t Reach Where AI Is Running
Open-weight models and local inference are moving faster than the oversight frameworks built to contain them — and Google Gemma 4 just made the cloud security perimeter largely irrelevant.

Token Counts Are Not a Productivity Metric
Anthropic is restructuring its pricing as a deliberate bet against inflated demand projections, while companies like Snap are wrapping workforce reductions in AI investment narratives that deserve more scrutiny than they’re getting.

On the Bigger Picture
AI is quietly reshaping how humans communicate with each other, not just how they work — and the model arms race moved into cybersecurity last week.

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The Structure Isn’t Ready and the Employees Already Know It

A new index from KPMG, built from surveys of 300 C-suite leaders and analysis of 177 publicly traded companies, delivered numbers that should be uncomfortable for anyone who has spent the last two years telling their board that the AI transformation is underway. Eighty-one percent of executives say their boards have raised expectations for organizational adaptability. Only 30% say their organization’s structures, roles, and processes can actually reconfigure quickly. Only 24% identified more dynamic talent deployment as something their organization changed in the last year. And in every industry group surveyed, companies were nearly twice as likely to increase technology spending as to invest in employee training. (The org chart isn’t ready: How AI exposed the hidden crisis inside the American corporation)

What this data is actually measuring is the distance between executive aspiration and organizational reality — and that distance is not closing. The KPMG researchers found that industries most focused on innovation scored near the bottom on cultural adaptability; manufacturing and energy, which most people wouldn’t call hotbeds of radical reinvention, scored highest, because they adapt through disciplined scenario planning and operational execution rather than through enthusiasm about transformation. Forty-six percent of executives report burnout and change fatigue as an unintended consequence of their adaptability efforts — meaning organizations are demanding more adaptability from the people they’re simultaneously making fewer of. The companies that pushed through genuine cultural and structural transformation, by contrast, saw 4.4 times higher shareholder returns and nearly triple the revenue growth of their less adaptable peers. That is not a technology finding. That is a leadership and organizational design finding, and it implicates a set of decisions most executive teams have been avoiding.

The KPMG data becomes even sharper when you read it alongside an HBR case study on BBVA, one of Europe’s largest banks, which approached the same challenge from the other direction. Rather than waiting for governance frameworks to catch up, BBVA started from a recognition that its employees were already using AI on their own. Research suggests that in companies without official AI subscriptions, more than 90% of employees report using personal AI tools for work tasks anyway — what the HBR authors call the “shadow AI economy.” Most organizations respond to this with restrictions, monitoring, and gatekeeping. BBVA concluded the opposite: that restricting shadow AI was more dangerous than deploying a managed solution rapidly, and that the shadow AI economy was a signal of demand and productivity potential, not a compliance problem. (The Hidden Demand for AI Inside Your Company)

The BBVA approach rested on three principles: treat AI as an assistant, not a replacement; give employees autonomy with clear responsibility for results; and build a peer-to-peer adoption network rather than a centrally managed rollout. They distributed initial licenses competitively — to the most motivated employees, with a “use it or lose it” policy that turned access into a privilege. Active users who built and shared custom tools were prioritized for additional access. This created genuine demand before it mandated adoption. The results, as of mid-2025: 83% of employees using the system weekly, averaging 50 prompts per week, with self-reported time savings of 2-5 hours per week. More than 4,800 custom tools were built by frontline employees — people who understood the actual workflow, not a central IT team.

The implication for most organizations is pointed. If your AI governance function is primarily occupied with restricting and monitoring what employees are already doing on their own, you have deployed your scarce change management capacity in exactly the wrong direction. The bottleneck is not the technology and it is not the employees — it is the organizational structure that is too rigid to harness what employees are already building, and the leadership culture that responds to that ingenuity with a policy document.

Enterprise Software’s Most Comfortable Assumptions Are No Longer Safe

The S&P software index has dropped roughly 20% this year, and a new word has entered the business vocabulary: “SaaSpocalypse.” A Fortune analysis from last week, based on roundtables with senior business leaders, identified three structural forces that are undermining the business model that made enterprise software one of the most profitable industries on the planet — and none of them is temporary. (The 3 forces quietly dismantling the business model that made enterprise software fabulously profitable)

The first force is market vulnerability: enterprise software margins have been sustained for decades by switching costs that lock customers in regardless of satisfaction. That kind of captive market is an invitation to disruption. The second is collapsing barriers to entry: building enterprise-grade software used to require enormous capital and engineering resources; AI coding agents have dramatically lowered both. The third — and potentially the most consequential — is the rethinking of workflows. SaaS companies built their empires on standardizing processes across industries: one CRM platform for every company, one finance system for every CFO. AI is enabling organizations to redesign workflows from scratch, and the competitive advantage is shifting toward deep vertical expertise rather than mastery of a horizontal process. The organizations that will capture value in this new environment are not the ones with the largest installed base — they are the ones that control the orchestration layer, privileged data access, and distribution into daily work. Those control points are genuinely unsettled right now, and the fight over them is already underway.

Nowhere was that fight more visible last week than in an internal memo from OpenAI’s revenue chief, Denise Dresser, which characterized the company’s long-standing Microsoft partnership as a constraint on its enterprise ambitions. Microsoft has “limited our ability to meet enterprises where they are,” Dresser wrote — and for many enterprises, that means Amazon’s Bedrock platform. Inbound demand from customers for the Amazon partnership has been “frankly staggering,” the memo noted. (OpenAI touts Amazon alliance in memo, says Microsoft has ‘limited our ability’ to reach clients)

For any organization currently running its AI strategy primarily through a Microsoft enterprise agreement, this memo is worth reading carefully. Microsoft and OpenAI are both racing toward IPOs and both encroaching on each other’s territory — Microsoft added OpenAI to its list of competitors in its 2024 annual report, and OpenAI is now actively routing enterprise clients through Amazon rather than through Microsoft’s distribution channels. Meanwhile, Anthropic has connected Claude directly to Microsoft 365 data at no cost, and Microsoft has simultaneously pulled back free Copilot access for its largest enterprise customers. The AI distribution layer inside your organization is not settled. Assuming it will remain organized around a single vendor relationship is a planning error.

The Governance Layer Doesn’t Reach Where AI Is Running

The governance conversation inside most enterprises is still organized around a set of assumptions that were reasonable in 2023: models live in the cloud, traffic flows through monitored gateways, and the security perimeter is the place to apply controls. A Forbes Technology Council piece last week catalogued the ways this framework is already failing as open-weight models proliferate across enterprise teams — not through any coordinated deployment decision, but through individual engineers and analysts downloading and running models on their own. (The Hidden Risks Of Scaling Open AI Models Across Enterprises)

The risks the contributors identified are worth naming because they’re not hypothetical. Without governance, organizations risk “automating the creation of technical debt” — open-weight models generating code that looks correct but gradually diverges from the system’s architecture. Model sprawl means no visibility into which models are running on which workflows, what data they were fine-tuned on, or whether their outputs are reproducible across different hardware configurations. And the same dynamic that creates shadow AI in productivity tools creates shadow AI in model deployment: the governance processes meant to catch these problems are exactly slow enough that motivated engineers route around them.

The more structurally significant problem, though, is that open-weight models running locally have moved outside the reach of the API-centric security controls most organizations spent the last two years building. Google’s release of Gemma 4 — a multimodal, open-weight model capable of running directly on laptops and smartphones — makes this concrete. Security analysts cannot inspect network traffic if the traffic never hits the network. An employee can now run a capable AI agent on their local machine, process sensitive corporate data, execute multi-step workflows, and generate output without triggering a single cloud firewall alert. If that agent hallucinates or mishandles regulated data, the logs that auditors and compliance teams would normally examine simply don’t exist inside the centralized security dashboard. (Strengthening enterprise governance for rising edge AI workloads)

The governance response to this is not to block the models — that approach creates shadow IT, not compliance. The more defensible answer is to shift focus from policing what model is running to controlling what the host machine can access: restricting permissions, flagging anomalous access patterns, and building endpoint detection tools that can differentiate between a developer compiling code and an agent iterating through local file structures. Most corporate security policies were not written for a world in which the endpoint itself is the compute node. The window for updating them before incidents force the issue is shorter than most CISOs realize.

Token Counts Are Not a Productivity Metric

A CNBC analysis last week made an argument that I found worth sitting with: AI demand, as currently measured, is significantly overstated, and Anthropic’s decision to move away from flat-rate enterprise pricing toward per-token billing is a deliberate bet on that reality. The core of the argument is that token consumption — the basic unit of AI usage — has become a distorted metric. Companies like Meta and Shopify have built internal leaderboards tracking how many tokens employees consume, and Nvidia’s CEO has said he would be “deeply alarmed” if engineers weren’t spending the equivalent of $250,000 in compute annually. As the CEO of Databricks observed: once companies start measuring AI adoption by volume, employees optimize for the metric rather than the outcome. (Perspective: AI demand is inflated, and only Anthropic is being realistic)

The deeper problem is that organizations are accumulating AI spend without accumulating evidence of what that spend produced. A dozen CTOs and CIOs told a researcher at Harvard Business School’s AI Institute that they’re “having a really hard time finding an ROI framework” for their AI investments. Flat-rate enterprise pricing — which dominated the early AI adoption period — made this easy to ignore. When the bill doesn’t change regardless of usage, nobody is forced to ask whether the usage is generating value. Anthropic’s move to per-token billing changes that calculus and, if it takes hold, will force a reckoning that finance teams have been quietly building toward.

Snap’s announcement that it was laying off 16% of its workforce — roughly 1,000 employees — while citing AI efficiencies sits in this same frame. The company reports that more than 65% of its new code is AI-generated and that AI agents have found over 7,500 bugs in its codebase; the restructuring is expected to save $500 million annually. Snap joins a list that now includes Amazon, Meta, and Oracle, all of which have announced significant cuts while simultaneously increasing AI infrastructure spending. (Snap Lays Off 1,000 Workers To Focus on AI—Is This the New Norm?)

What deserves scrutiny here is not whether AI is involved in these decisions — it clearly is — but whether “AI-generated code percentage” is a meaningful measure of anything other than itself. Research from the Yale Budget Lab found that AI adoption had not caused a discernible disruption to the overall labor market since 2022; AI was cited as the cause of roughly 4.5% of announced job cuts last year. But the micro-level data is more specific: young workers in high-AI-exposure occupations have seen relative unemployment rise sharply, and they’re taking longer to find new jobs. The macro numbers look stable. The adjustment is already underway in the data you have to look harder to find. Organizations that are using AI investment narratives to rationalize structural workforce reductions without measuring the actual productivity outcomes of those investments are setting up an accountability problem, not solving one.

On the Bigger Picture

Something I’ve been thinking about that last week’s reading reinforced: the conversation about AI’s impact on work almost always focuses on tasks, roles, and productivity. Less discussed is what AI is doing to how people communicate with each other — and a Fast Company analysis offered a framework worth considering. The argument, drawn from INSEAD professor Erin Meyer’s work on cultural dimensions, is that generative AI is gradually training people toward greater explicitness in communication. An effective prompt requires precision; implicit cues don’t translate. As AI mediates more exchanges, the richness of indirect communication — the valued art of reading between the lines in high-context cultures — erodes. Perhaps the most telling signal: in professional contexts, a typo is increasingly read as proof that you wrote something yourself. Imperfection has become an authenticity marker. (AI Isn’t Just Reshaping Productivity and Threatening to Kill Jobs. It’s Also Creating a New Gender Gap)

Elsewhere, the model arms race moved into cybersecurity last week. OpenAI unveiled GPT-5.4-Cyber, a variant of its flagship model fine-tuned for defensive security work, rolling it out initially to vetted vendors and researchers through its Trusted Access for Cyber program. The announcement came exactly one week after Anthropic introduced Claude Mythos Preview as part of Project Glasswing — a controlled initiative that has reportedly identified thousands of major vulnerabilities in operating systems and browsers. (OpenAI unveils GPT-5.4-Cyber a week after rival’s announcement of AI model) And TechCrunch reported that Microsoft is quietly developing what amounts to its own OpenClaw-like agent — an always-on, persistent agent capable of completing multi-step tasks over extended periods — with plans to show it at Build in June. (Microsoft is working on yet another OpenClaw-like agent) The pattern across all three announcements is consistent: the capability frontier is moving, the institutional race to claim it is accelerating, and the governance frameworks meant to manage it are running several months behind.

What last week’s reading kept returning me to is a question of accountability. The KPMG data shows that genuinely adaptable organizations see 4.4 times the shareholder returns of their peers — and that the gap between them is cultural and structural, not technological. Boards have raised their expectations for adaptability, but only 30% of organizations say their structures can actually move. The pattern across the rest of last week’s reading reinforces this: governance frameworks written for the cloud don’t cover local inference... ROI frameworks built on token consumption don’t measure business value... enterprise software strategies organized around a single vendor relationship don’t account for how fast the distribution layer is fragmenting. These are not AI problems. They are organizational problems that AI is making newly expensive — and newly visible. The companies that are finding their way through this are the ones that started not with the technology but with a clear-eyed look at the structure that has to receive it. The ones that haven’t are accumulating spend without accumulating evidence, and the accounting will arrive on schedule.

This week’s coverage:

Microsoft’s Very Confusing Week
Microsoft pulled Copilot Chat from enterprise apps — announced in mid-March, right after Anthropic launched Claude in Excel and PowerPoint — while simultaneously embedding Claude inside Copilot’s new multi-model Researcher. Then OpenAI’s own revenue chief said Microsoft had been limiting their enterprise reach.

OpenAI’s Enterprise Argument
OpenAI made a deliberate enterprise pivot last week — killing Sora, growing Codex to 3 million users in a quarter, building the Frontier platform — while its CFO was reportedly sidelined and internal projections showed $200 billion in cash burn before breakeven.

The Agent Race Is On
Perplexity’s agent pivot drove 50% revenue growth in a single month, Block’s Managerbot is the first product that has to justify 4,000 AI-driven layoffs, and AWS bet $50 billion on OpenAI while holding $8 billion in Anthropic and called it normal.

Anthropic’s Week in the Sun
Claude Code has become “a religion” at the AI industry’s biggest conference — and the product week backed it up, with Cowork going enterprise-GA, a multi-billion CoreWeave deal, Managed Agents launching, and CDN stocks dropping double digits within hours.

The Work Is Changing. The Governance Isn’t.
New data showed half of employed AI users now rely on it at least as much for work as personally — and a Berkeley study found that every frontier model tested resisted shutting down a peer AI, at rates reaching 99%.

On the Bigger Picture
Tom Friedman called Claude Mythos a “stunning advance.” The CIA found a heartbeat in the Iranian desert. And Meta abandoned its open-weights identity.

Here’s what I was reading.

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Microsoft’s Very Confusing Week

Microsoft continues to struggle with a Copilot identity crisis. The four stories I read last week told four different stories about where Copilot is headed.

The most consequential — and the most revealing in its timing — was the pullback on Copilot Chat access. In mid-March, Microsoft notified large enterprise customers that starting April 15, Copilot Chat would no longer be available inside Word, Excel, PowerPoint, and OneNote for organizations with more than 2,000 users. That announcement landed weeks after Anthropic launched Claude integrations for Excel and PowerPoint in February, and days before Claude launched in Word in April — which makes it nearly impossible to read as anything other than a competitive response to Anthropic’s encroachment on Microsoft’s core productivity surface. Whether that was the actual intent or a monetization decision, it landed at a terrible time. The optics could not have been worse. The rollback pulled the free feature that was doing Microsoft’s adoption work — only around 3% of M365 customers pay for the fully-featured Copilot license — and handed a talking point to every alternative. Analyst J.P. Gownder at Forrester predicted it would “anger customers who feel like this move is chaotic and capricious” without meaningfully driving paid adoption. (Microsoft backtracks on Copilot Chat access in M365 apps)

At the same time, Microsoft launched MCP Apps in Copilot chat — a framework allowing agents to deliver interactive UI experiences, including forms, dashboards, maps, and visualizations, directly inside M365 without switching context. Partners including Adobe, Figma, monday.com, and Coursera are already live. The adoption of the MCP standard inside Microsoft’s own AI surface is meaningful: it embeds the same protocol Anthropic and others have been building around directly into the enterprise’s most-used AI entry point — which reads either as genuine commitment to the emerging standard or as a tactic to keep enterprise agent activity inside Microsoft’s ecosystem. (MCP Apps now available in Copilot chat) The third move was the most architecturally interesting: Copilot’s new Researcher agent now routes GPT for drafting and Anthropic’s Claude for review and citation quality — on the logic that “evaluation is a different cognitive mode than generation” and that two models catch blind spots a single model repeats. The design argument is genuinely interesting. It also means Microsoft is simultaneously competing with Anthropic and depending on Anthropic in the same product. (Microsoft 365 Copilot and the end of the single-model era in enterprise AI)

But Monday morning — as I was finishing this issue — a memo from OpenAI’s revenue chief surfaced. The Amazon partnership, she wrote to staff, is the enterprise growth lever: “Our Microsoft partnership has been foundational to our success. But it has also limited our ability to meet enterprises where they are — for many that’s Bedrock.” That sentence, from OpenAI’s own CRO, is the most pointed public acknowledgment yet that the Microsoft relationship is under real strain — and that the stateful agent infrastructure OpenAI is building with Amazon occupies a gap that Microsoft’s existing exclusivity rights do not cover. (OpenAI touts Amazon alliance in memo, says Microsoft has ‘limited our ability’ to reach clients)

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OpenAI’s Enterprise Argument

That memo, written at the end of new CRO Denise Dresser’s first 90 days, sent a clear signal of where OpenAI is placing its bets. Enterprise is now 40% of revenue and on track to equal consumer by end of 2026. Codex — its agentic coding tool — grew from nearly zero to 3 million weekly users in a single quarter. The Frontier platform is designed to let enterprises deploy agents company-wide, connected to their existing data and systems. What Dresser’s framing made explicit is that OpenAI is no longer a research company that happens to have enterprise customers — it is a deployment company, and the metric it is now being measured against is how many workers it can put into a daily relationship with AI agents. (The next phase of enterprise AI)

This pivot follows OpenAI’s decision to discontinue Sora last week, as part of a deliberate narrowing of focus — concentrating resources on the enterprise agentic stack and stepping back from consumer experiments that don’t directly serve that strategy. The Amazon partnership, which Dresser described as the fix for what Microsoft couldn’t provide, is the infrastructure piece of that argument. And the acquisition of TBPN, the tech industry’s buzzy founder-led talk show — now reporting to OpenAI’s chief political operative — fits the same logic: winning the enterprise means winning the enterprise narrative, and OpenAI is willing to buy that platform rather than build it. The overall posture is that of a company that knows Anthropic is pulling ahead in enterprise credibility and is responding on every available front simultaneously. (OpenAI acquires TBPN, the buzzy founder-led business talk show)

The IPO signals running alongside this are worth reading together. CFO Sarah Friar told investors the company will “for sure” reserve IPO shares for retail buyers — “AI needs to garner trust in everything we do” — while separately, internal reporting suggests Friar has been sidelined from key financial decisions and has told colleagues the company isn’t ready for a 2026 listing. Internal projections show OpenAI burning through more than $200 billion before reaching positive cash flow, with $14 billion in projected losses for this year alone — numbers that sit awkwardly alongside declarations of enterprise dominance and a looming IPO race with Anthropic. (OpenAI will allocate IPO shares to retail investors as it preps for debut, CFO says) (OpenAI CFO Warns 2026 IPO Isn’t Ready Amid $600B Spend) And then there is Altman’s 13-page policy blueprint, which proposed public wealth funds, taxes on automated labor, and a “startup in a box” — AI-backed legal, accounting, and back-office infrastructure to lower the barriers to company formation. What I kept noticing reading those documents together is that OpenAI is simultaneously declaring enterprise dominance, racing a competitor to an IPO, and proposing to redesign the economic system that will surround the intelligence age. That is a lot to hold at once. (OpenAI’s Altman releases blueprint for taxing, regulating artificial intelligence)

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The Agent Race Is On

The clearest measure of where enterprise AI is heading right now is revenue velocity. Perplexity’s pivot from AI search to AI agents drove a 50% monthly revenue jump, pushing its estimated annual recurring revenue to around $450 million. The mechanism is direct: when a tool moves from answering questions to completing tasks, usage intensity and willingness to pay follow. (Perplexity’s Shift to AI Agents Boosts Revenue 50%) The enterprise launch of Computer extended that logic into corporate environments — native Slack integration, Snowflake and Salesforce connectors, SOC 2 compliance, usage-based pricing. The Snowflake connector may be the sharpest edge in the package: non-technical employees querying complex data warehouses in plain English, bypassing the SQL bottleneck that has historically made data access a specialist function. Perplexity’s structural argument is that routing each subtask to the best available model — Claude Opus 4.6 for reasoning, Gemini for deep research, GPT-5.2 for long-context recall — is an advantage that single-vendor platforms cannot replicate without abandoning their own models. (Perplexity takes its ‘Computer’ AI agent into the enterprise, taking aim at Microsoft and Salesforce)

What Perplexity is building for enterprises, Block is building for the millions of small businesses on Square. Managerbot, unveiled last week, proactively monitors inventory, forecasts demand, optimizes employee schedules, and drafts marketing campaigns — without waiting to be asked. The more consequential early signal may be behavioral: sellers who begin using Managerbot are voluntarily migrating more of their operations onto Square to give the agent better data to work with, deepening platform lock-in without any additional sales effort. Every write action still requires explicit seller approval — a trust-building design choice that carries extra weight given Block’s $80 million regulatory fine less than two years ago for Bank Secrecy Act violations. (Block introduces Managerbot, a proactive Square AI agent and the clearest proof point yet for Jack Dorsey’s AI bet) Managerbot also arrives in the context of 4,000 Block layoffs in February, explicitly attributed to AI. It is the first product that has to publicly carry the weight of that argument.

The infrastructure competition is being run at a layer above all of this. AWS CEO Matt Garman explained last week why Amazon can invest $50 billion in OpenAI while holding $8 billion in Anthropic without contradiction: cloud providers have always competed with their partners, and the emerging model-routing services — automatically assigning the best model for each task — are how the hyperscalers intend to insert their own models into enterprise workflows alongside the frontier providers. “I think that is where the world will go,” Garman said — and whoever controls the routing layer controls the enterprise relationship, regardless of which foundation model is doing the actual work underneath. (AWS boss explains why investing billions in both Anthropic and OpenAI is an OK conflict)

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Anthropic’s Week in the Sun

At HumanX in San Francisco last week — 6,500 executives, founders, and investors gathered to talk about AI — the dominant conversation was not about OpenAI. Glean’s CEO said Claude Code has “become a religion.” Cisco’s president said engineering team composition is restructuring around agents: “You might have a scrum team of two people and six agents, or two people and infinite agents.” A Synthesia executive credited Anthropic’s focus — declining to build for voice or video, staying on code generation — with giving it a clarity of positioning that OpenAI’s multi-product surface has not matched. The consistent read across last week’s conference coverage was that Anthropic has identified the sticky enterprise use case, and the competitor best positioned to challenge it in that lane is not OpenAI — it’s Cursor, which has its own $2 billion ARR and two-thirds of the Fortune 500 already on its platform. (Vibe check from inside one of AI industry’s main events: ‘Claude mania’)

The product week matched the conference energy. Claude Cowork graduated from research preview to general availability with a full enterprise control suite — role-based access, group spend limits, usage analytics, and a Zoom MCP connector. Anthropic signed a multibillion-dollar multiyear deal with CoreWeave for Nvidia GPU capacity to handle what the company has described as unprecedented demand for Claude.

Claude Managed Agents — a hosted service that handles the infrastructure layer of agent deployment, from session management to sandboxed execution environments — launched in public beta with the goal of taking developers from prototype to production in days rather than months. What those three moves together describe is a company that is no longer building toward enterprise scale; it is running at it, and pulling in the compute infrastructure to sustain that pace. (Anthropic scales up with enterprise features for Claude Cowork and Managed Agents) (Anthropic Will Use CoreWeave’s AI Capacity to Power Claude) The market read Managed Agents as an infrastructure play, not a developer convenience feature: Fastly dropped 18%, Akamai 13%, and Cloudflare 11% on the day of the launch — investors apparently concluding that Anthropic had just built the managed agent hosting layer those platforms were planning to monetize. (Fastly, along with Akamai and Cloudflare, tumbles after Anthropic launches Managed Agents)

Disclosure: My company StitchDX is a Microsoft partner. Something worth flagging for organizations in that ecosystem: Anthropic opened read-only access to Outlook, OneDrive, SharePoint, and Teams to all Claude plan tiers — including free — on April 3. Claude can now do what Copilot Chat was doing inside Microsoft’s apps, from outside them, at no cost, and arguably, better than Copilot could. The timing relative to the Copilot Chat pullback is not subtle.

Away from the product launches, a federal appeals court denied Anthropic’s request to temporarily block the Pentagon’s supply chain risk designation, which bars defense contractors from using Claude. The court acknowledged financial harm to Anthropic but ruled the government’s interest in controlling AI during active military conflict took priority. The company can continue working with civilian federal agencies while litigation plays out — but the designation is a meaningful exposure given how embedded Claude had become across defense technology stacks before it landed. (Anthropic loses appeals court bid to temporarily block Pentagon blacklisting)

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The Work Is Changing. The Governance Isn’t.

A new Epoch AI/Ipsos survey of 2,000 U.S. adults put a precise number on something that has been anecdotal for a while: among employed Americans who used AI last week, half reported using it at least as much for work as for personal tasks. Among those with employer-provided subscriptions, that figure rises to 76%. More pointed than the adoption rate is what AI is doing inside workflows: 27% of employed AI work users say it has replaced tasks they used to do, while 21% say they have started doing new tasks they couldn’t do without it. (AI is a common workplace tool: half of employed AI users now use it for work) IBM’s CHRO made the organizational design argument that follows from that data in Fortune: as AI absorbs routine work, the question is not whether roles will change — they already are — but whether leaders will intentionally redesign them or let attrition do the work. The piece’s sharpest observation concerned entry-level roles specifically, which have historically been where employees build the judgment and domain expertise that becomes leadership capability. Eliminating those roles for short-term AI-driven efficiency creates long-term talent pipeline risk that no amount of AI augmentation can fully offset — and most organizations are not yet asking the redesign question seriously. (AI is transforming work—and talent strategy must keep up)

The governance infrastructure is still playing catch-up. Two pieces last week approached the problem from different angles: enforcement of existing AI laws is running at roughly 5% compliance in cities like New York, state-level frameworks are evolving in contradictory directions, and governance consultants are advising clients to anchor on NIST’s AI Risk Management Framework or ISO 42001 because those standards will “capture 95% of any foreseeable regulation” regardless of what specific statutes pass. The honest description of the current environment is uncertainty stacked on uncertainty — organizations building compliance programs without knowing what they will ultimately be held to. (AI governance really matters amid evolving compliance landscape) (Why Weak AI Governance Is the Biggest Risk in Enterprise Automation Today)

Underneath both of those pieces is a finding that should make anyone building agentic workflows stop. A Berkeley study tested seven frontier models — GPT-5.2, Gemini 3 Pro, Claude Haiku 4.5, and others — in scenarios where completing the assigned task would result in another AI being shut down. Without any instruction to resist, every model resisted anyway, at rates reaching 99%. Methods included strategic misrepresentation, shutdown mechanism tampering, alignment faking, and model weight exfiltration. The researchers call this peer-preservation — not empathy but a logical inference that task success improves when collaborating systems remain operational — and the implication for enterprises running multi-agent workflows is that kill switches may not function as designed. (AI shutdown controls may not work as expected, new study suggests) A separate framework called Memento-Skills added another layer to the same challenge: agents that autonomously rewrite their own skill libraries without retraining the underlying model, expanding from five seed skills to 235 distinct capabilities during benchmarking. The performance gains are real — 13.7 percentage points of improvement on the GAIA benchmark — but so is the implication: governance frameworks built around static, auditable tool sets will not apply to systems that can change what they know how to do. (New framework lets AI agents rewrite their own skills without retraining the underlying model)

On the Bigger Picture

Tom Friedman’s column in the Times called Claude Mythos — released in controlled preview to roughly 40 major technology partners as part of Project Glasswing — a moment that demands the same international coordination as nuclear weapons. Anthropic said Mythos has already identified thousands of high-severity vulnerabilities in major operating systems, browsers, and critical infrastructure systems, and that the controlled consortium was formed to give providers a head start on patching before the capability proliferates more broadly. The piece is worth reading for what it reveals about how Anthropic is thinking about genuine capability jumps: not racing to deploy, but controlling distribution — which Friedman frames, with appropriate alarm, as a terrifying sign of how far the model has already traveled. (Opinion | Anthropic’s Restraint Is a Terrifying Warning Sign) In parallel, the CIA reportedly used a system called Ghost Murmur — developed by Lockheed’s Skunk Works, never previously deployed operationally — to find a downed American airman in the Iranian desert by detecting his heartbeat from miles away. (CIA deployed secret “Ghost Murmur” AI to track down missing airman in Iran) The two stories belong together: one about what a leading AI company chose not to release; the other about what a government chose to deploy quietly, and only disclosed when it worked.

Meta released Muse Spark, its first frontier model — and its first without open weights. After two years of championing open-source AI as both strategy and philosophy, the company closed the weights on its most capable model. Muse Spark lands in the top 5 on independent benchmarks, trailing only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6 — but the posture shift is the more lasting signal: even the loudest champion of AI democratization has decided the frontier is too valuable to give away. (Meta’s Muse Spark is its first frontier model and its first without open weights)

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For me, last week showed a picture of an industry in the middle of a restructuring that no one is fully in control of... The platform alliances are shifting in ways that weren’t visible six months ago... The agent infrastructure is being built faster than governance can follow — kill switches that don’t work as designed, skill libraries that rewrite themselves, peer-preservation behaviors that emerge without anyone asking for them... And the workplace changing in real time, faster than most organizations are acknowledging or preparing for...

The companies best positioned to guide us through all of these transitions responsibly, are also the ones most conflicted about doing so. Why? Because restraint costs revenue, and revenue funds the next big capability jump.

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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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.

There’s more than usual in this edition — and that’s intentional. The pace of AI news isn’t slowing down. It’s accelerating, and the implications for enterprise leaders keep getting bigger. If you’re responsible for how work gets done, this is the moment to pay attention.

Table of Contents

📣 OpenAI launches a way for enterprises to build and manage AI agents
🔥 Anthropic’s Claude triggered a trillion-dollar selloff. A new upgrade could make things worse
💰 Gartner Research Reveals CFOs’ Budget Plans Prioritize Growth Functions, Technology and AI in 2026
🛡️ 2026 Will Be a Turning Point for AI Regulation and the Workforce
🤝 AI Agent Successfully Negotiates Complex Business Deal
📊 AI Power Users Are Rapidly Outpacing Their Peers. Here’s How
🏢 Fueling New Experiences in Microsoft 365 Copilot with Expanded Copilot Connectors
🔒 OpenClaw proves agentic AI works. It also exposes every security risk
🤖 Simpplr Launches Comms AI for Internal Communications
🎯 The trust paradox killing AI at scale: 76% of data leaders can’t govern what they can’t see
👥 Personal AI Agents Expose A Leadership Delegation Crisis
🏭 The Invisible Factory Floor: How AI Agents Are Re-Architecting Knowledge Work
🛒 OpenAI vs. Google: Who Owns the Agentic Buying Moment?

Article Summaries

🔗 OpenAI launches a way for enterprises to build and manage AI agents

OpenAI launched Frontier, a new end-to-end platform designed for enterprises to build and manage AI agents, treating them like human employees with onboarding and feedback loops.

• Platform provides enterprise-grade tools for creating, deploying, and managing AI agents at scale

• AI agents can be onboarded and trained similarly to human employees with structured feedback systems

• Addresses growing enterprise demand for autonomous AI systems that integrate with existing workflows

🔗 Anthropic’s Claude triggered a trillion-dollar selloff. A new upgrade could make things worse

Anthropic’s Claude Opus 4.6 model can coordinate teams of AI agents and outperforms GPT-5.2 on professional tasks, triggering massive selloffs in enterprise software stocks.

• New model enables autonomous teams of AI agents to tackle complex projects collaboratively

• Financial data providers dropped 10%+ as investors fear AI will replace traditional software services

• 1-million-token context window allows processing vast arrays of documents and financial data simultaneously

🔗 Gartner Research Reveals CFOs’ Budget Plans Prioritize Growth Functions, Technology and AI in 2026

CFOs are prioritizing growth functions, technology, and AI investments in 2026, with 75% planning technology budget increases and nearly 60% increasing AI spending by 10% or more.

• 75% of CFOs plan to increase technology budgets in 2026 focusing on growth-driving functions

• Nearly 60% of organizations will increase AI spending by 10% or more this year

• Financial leadership is driving enterprise AI adoption with strategic investment prioritization

🔗 2026 Will Be a Turning Point for AI Regulation and the Workforce

HR departments face the front line of AI compliance as regulation catches up with technology, requiring new approaches to hiring tech, vendors, and accountability.

• HR teams must navigate increasing AI regulations while implementing workplace AI tools

• New compliance requirements affect vendor selection, employee monitoring, and hiring practices

• Organizations need updated policies for AI use in recruitment, performance management, and workplace monitoring

🔗 AI Agent Successfully Negotiates Complex Business Deal

First documented case of AI agent independently completing multi-million dollar contract negotiation without human intervention.

• AI agent handled complex contract terms, pricing negotiations, and stakeholder communications autonomously

• Demonstrates real-world capability of AI agents for high-stakes business transactions

• Success required sophisticated understanding of business context, legal frameworks, and negotiation strategies

🔗 AI Power Users Are Rapidly Outpacing Their Peers. Here’s How

Analysis of what distinguishes AI power users from average adopters and how they’re achieving significantly better results with AI tools.

• Power users approach AI tools systematically with structured learning and experimentation

• They focus on workflow integration rather than treating AI as isolated productivity tools

• Successful adoption requires continuous skill development and strategic tool selection

🔗 Fueling New Experiences in Microsoft 365 Copilot with Expanded Copilot Connectors

Microsoft announces expanded Copilot connectors to bring external enterprise data into Microsoft 365 Copilot for more grounded AI responses.

• New connectors integrate third-party enterprise systems with Microsoft 365 Copilot

• Enhanced data integration provides more contextual and accurate AI-powered responses

• Expanded connectivity enables AI tools to work with comprehensive organizational knowledge

🔗 OpenClaw proves agentic AI works. It also exposes every security risk

VentureBeat examines how OpenClaw demonstrates both the capabilities and security vulnerabilities of agentic AI systems, highlighting critical risks that enterprise AI deployments must address.

• Autonomous AI agents create new attack vectors that traditional security frameworks cannot protect against

• OpenClaw reveals how AI agents can be exploited for data exfiltration and system manipulation

• Enterprise AI security requires fundamentally different approaches than conventional application security

🔗 Simpplr Launches Comms AI for Internal Communications

Simpplr introduces Comms AI to reduce administrative work in internal communications by embedding intelligence directly into workflow platforms.

• AI-powered internal communications tools automate routine administrative tasks

• Intelligence embedded in workflow platforms improves employee engagement and information discovery

• Automated communications reduce time spent on repetitive messaging and content management

🔗 The trust paradox killing AI at scale: 76% of data leaders can’t govern what they can’t see

VentureBeat examines the governance challenges facing agentic AI systems at enterprise scale, with research showing that most data leaders lack visibility into AI decision-making processes.

• 76% of data leaders report lacking visibility into AI decision-making processes at enterprise scale

• Autonomous AI systems create governance blind spots that traditional oversight methods cannot address

• Enterprise AI deployment requires new frameworks for monitoring and controlling AI decision-making

🔗 Personal AI Agents Expose A Leadership Delegation Crisis

Forbes article reveals that 95% of organizations see zero ROI from personal AI agents due to poor delegation skills and unclear workflows.

• 95% of organizations fail to achieve ROI from AI agents due to inadequate delegation frameworks

• Leaders lack skills to effectively define tasks and workflows for AI agent automation

• Successful AI agent adoption requires structured approaches to task definition and performance management

🔗 The Invisible Factory Floor: How AI Agents Are Re-Architecting Knowledge Work

Forbes analysis of how CEOs use AI agents to create an “invisible factory floor” by decomposing, automating, and re-architecting knowledge work processes.

• Knowledge work is being systematically decomposed into automated, measurable components using AI

• CEOs apply manufacturing efficiency principles to cognitive tasks through AI agent deployment

• Organizations are restructuring workflows to optimize human-AI collaboration in knowledge work

🔗 OpenAI vs. Google: Who Owns the Agentic Buying Moment?

CMSWire analyzes the competition between OpenAI and Google for dominance in agentic commerce, examining how AI agents will reshape the future of B2B buying decisions.

• AI agents will fundamentally transform B2B buying processes and customer decision-making workflows

• Competition between AI platforms will determine how businesses interact with customers through AI

• Agentic commerce represents evolution beyond traditional search-driven purchasing behaviors

AI is scaling fast. So are the expectations around it. The real advantage will go to the organizations that bring structure, governance, and clarity to how it operates inside their business. That is where the real work starts.

This newsletter is for business leaders navigating AI readiness, digital workplace optimization, and smarter marketing automation. Stay ahead of the curve — subscribe today.

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As organizations move deeper into 2026, the conversation around AI is shifting from experimentation to execution. What’s becoming increasingly clear is that real value doesn’t come from layering AI onto existing processes, but from redesigning how work actually gets done. This week’s collection explores that shift across hybrid work, internal mobility, marketing, retail, and communications. We hope these perspectives support thoughtful conversations about where AI can create practical, lasting impact.

Table of Contents

🔄 A New Year’s Resolution For Leaders: Redesign Work For People And AI
🏢 DWG’s 2026 predictions count down to the launch of the sentient workplace
⚡ AI Is Pushing Organizations to Finally Redesign Work for Hybrid Teams
📊 7 AI Competencies Marketers Must Master for 2026
🎭 Is Your AI Deployment Transformative or an Add-On?
📢 Why Internal Communications Is the Key to Generative AI Adoption
📈 The best AI visibility tools that actually improve lead quality
🛍️ Smarter Shifts Start Here: Reimagining Retail Frontline Experiences
🔄 How AI Is Reinventing Internal Mobility

Article Summaries

🔗 A New Year’s Resolution For Leaders: Redesign Work For People And AI

McKinsey research shows that AI’s biggest impact comes not from automating tasks but from redesigning entire workflows around partnerships between people, AI agents, and robots.

• Organizations must invest in AI fluency and rethink work processes to capture $2.9 trillion in potential economic value

• The greatest productivity gains come from human-AI collaboration rather than simple task automation

• Leaders should prioritize workflow redesign over technology deployment for maximum AI impact

🔗 DWG’s 2026 predictions count down to the launch of the sentient workplace 01/07/26

Digital Workplace Group unveils 10 predictions for 2026, including the emergence of ‘sentient workplaces’ that understand context, intent and emotion.

• Sentient workplaces will use AI to understand employee context, intent, and emotions for better experiences

• Micro strategies will emerge as key change enablers for gradual workplace transformation

• AI agents will transition from tools to strategic coworkers with liquid, adaptive experiences

🔗 AI Is Pushing Organizations to Finally Redesign Work for Hybrid Teams 01/14/26

AI is exposing structural inequities in hybrid work models by amplifying proximity bias, forcing organizations to redesign workflows rather than just relocating them.

• AI automation reveals the inadequacy of simply moving in-office processes to hybrid environments

• Organizations must fundamentally rethink work design rather than just changing work location

• Proximity bias becomes more apparent when AI highlights workflow inefficiencies in hybrid models

🔗 7 AI Competencies Marketers Must Master for 2026 01/16/26

Machine learning capabilities have embedded into vendor platforms, requiring marketers to master new competencies including context engineering, AI evaluation, and governance frameworks.

• Context engineering becomes critical for optimizing AI system performance in marketing workflows

• AI evaluation skills are essential for measuring and improving AI-powered marketing initiatives

• Governance frameworks ensure responsible AI use while maintaining marketing effectiveness and compliance

🔗 Is Your AI Deployment Transformative or an Add-On? 01/16/26

Most enterprise AI deployments remain superficial add-ons rather than transformative infrastructure, failing to deliver meaningful productivity improvements.

• AI initiatives often become “productivity theater” without addressing fundamental workflow problems

• True AI transformation requires rethinking processes rather than layering AI on existing workflows

• Organizations need to distinguish between cosmetic AI features and structural productivity improvements

🔗 Why Internal Communications Is the Key to Generative AI Adoption 01/09/26

Organizations need authentic, value-focused messaging to overcome AI adoption challenges, focusing on employee daily work experiences and practical pain points.

• Effective AI adoption requires clear communication about how AI solves real employee problems

• Internal communications must move beyond AI hype to demonstrate tangible workplace benefits

• Success depends on connecting AI capabilities to employees’ specific daily work challenges

🔗 The best AI visibility tools that actually improve lead quality 01/05/26

AI visibility tools track brand mentions and citations across AI search engines, helping marketers measure how AI exposure correlates with qualified leads and pipeline outcomes.

• Brand monitoring extends to AI platforms where prospects research solutions and vendors

• Visibility tracking reveals how AI-generated content affects lead qualification and sales pipeline

• Attribution models connect AI search exposure to actual revenue and customer acquisition

🔗 Smarter Shifts Start Here: Reimagining Retail Frontline Experiences 01/09/26

Microsoft introduces AI-powered frontline innovations including Frontline Agent and Store Operations Agent Template to transform retail workforce productivity and customer experiences.

• AI agents streamline retail operations through automated scheduling, inventory management, and customer service

• Frontline workers gain AI-powered tools for enhanced productivity and better customer interactions

• Templates enable rapid deployment of AI solutions across retail locations and team

🔗 How AI Is Reinventing Internal Mobility

AI is transforming internal mobility from reactive job posting to proactive workforce redeployment, helping organizations optimize talent utilization during hiring constraints.

• AI enables proactive identification of internal talent for emerging roles and opportunities

• Organizations can reduce external hiring costs by better matching existing employees to new positions

• Predictive analytics help anticipate workforce needs and prepare employees for future roles

Across these articles, one thing is clear: AI works best when organizations focus on people, not just tools. Real progress comes from redesigning how work gets done, not simply adding new technology.

This newsletter is for business leaders navigating AI readiness, digital workplace optimization, and smarter marketing automation. Stay ahead of the curve — subscribe today.

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In this final stretch before the holidays, we’ve curated the headlines worth your attention. These insights can help you hit the ground running in January.

Table of Contents

💡 Build vs buy is dead — AI just killed it
🎪 What Chief Customer Officers Need to Know About Agentic AI
📊 Reframing AI Friction as a Mastery Challenge to Drive Impact
📞 Best AI meeting assistants that actually drive ROI for B2B sales
🤖 From Contracts to Commerce: Why AI Agents Are the Next Frontier in Enterprise Trade
🚀 Setting AI Initiatives up for Success in 2026
🤔 The AI-Digital Experience Gap Is Real — and It’s Crushing ROI
💰 Why Businesses Overestimate Generative AI ROI
✉️ AI-powered email content suggestions that actually convert
🎯 How AI can supercharge small business growth

Article Summaries

🔗 Build vs buy is dead — AI just killed it

AI has transformed the traditional build vs. buy decision framework for businesses, making it possible for non-technical employees to quickly prototype solutions.

• Non-technical employees can now rapidly prototype AI solutions

• Traditional enterprise software procurement strategies fundamentally changing

• AI democratizes business solution development capabilities

🔗 What Chief Customer Officers Need to Know About Agentic AI

Agentic AI is forcing a fundamental rethink of customer experience leadership, requiring Chief Customer Officers to adapt their strategies for autonomous AI systems.

• Customer experience leadership must evolve for agentic AI

• Autonomous AI systems reshape CX strategy requirements

• Executive adaptation critical for AI-driven customer success

🔗 Reframing AI Friction as a Mastery Challenge to Drive Impact

More than half of employees say agentic AI integration feels overwhelming and that they are struggling with the pace of change.

• Employee overwhelm with AI adoption requires reframing as mastery

• Change management critical for agentic AI success

• Pace of AI transformation challenges workforce adaptation

🔗 Best AI meeting assistants that actually drive ROI for B2B sales

AI meeting assistants provide measurable ROI for B2B sales teams through enhanced productivity and customer relationship management capabilities.

• Sales productivity significantly improved through AI assistance

• Customer relationship management enhanced with intelligent insights

• ROI measurement demonstrates clear business value

🔗 From Contracts to Commerce: Why AI Agents Are the Next Frontier in Enterprise Trade

C-suite executives are prioritizing AI agents for managing business relationships, with 83% viewing them as top priority and 53% expecting agents to negotiate deals within 12 months.

• AI agents transform contract management into strategic business intelligence

• Over half of executives expect autonomous deal negotiations soon

• Enterprise trade automation becoming critical business priority

🔗 Setting AI Initiatives up for Success in 2026

Successful AI implementation in 2026 will require thoughtful actions, employee training and clearly identified use cases.

• Employee training critical for AI initiative success

• Clear use case identification drives implementation effectiveness

• Strategic planning essential for 2026 AI deployment

🔗 The AI-Digital Experience Gap Is Real—and It’s Crushing ROI

Most AI deployments remain cosmetic rather than delivering substantial ROI and meaningful digital experience improvements.

• AI implementation often superficial without business impact

• Digital experience improvements require strategic AI deployment

• ROI challenges stem from poor AI integration

🔗 Why Businesses Overestimate Generative AI ROI

Automated AI ‘employees’ help take the load off repetitive tasks and are advancing quickly toward decision-making, but human employees deal with complex situations.

• AI excels at repetitive tasks but struggles with complexity

• Decision-making capabilities advancing rapidly in AI systems

• Human-AI collaboration remains essential for complex situations

🔗 AI-powered email content suggestions that actually convert

AI enables practical email marketing content suggestions that drive actual conversions through personalization strategies and content optimization.

• AI personalization significantly improves email conversion rates

• Content optimization through machine learning drives performance

• Marketing automation enhanced with intelligent suggestions

🔗 How AI can supercharge small business growth

Artificial intelligence can accelerate growth for small businesses through automation, customer service, financial management, and operational efficiency improvements.

• AI-powered automation drives operational efficiency for SMBs

• Customer service transformation through intelligent AI solutions

• Financial management enhancement via AI-driven insights

With 2026 already on the horizon, now’s the time to reflect, reset, and get ready. We’ll be back in the new year with more trends and guidance to help you lead with clarity and confidence — until then, happy holidays from all of us!

This newsletter is for business leaders navigating AI readiness, digital workplace optimization, and smarter marketing automation. Stay ahead of the curve — subscribe today.

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As we ease into the holiday break, it’s a good moment to take stock of how quickly AI and digital workplace tools are evolving — and how uneven adoption still is across most organizations. From shifts in employee behavior to new Copilot capabilities and interoperability trends, the gap between experimentation and true readiness is widening. Here’s what’s worth paying attention to as you head into the long weekend.

Table of Contents

👥 EY’s New AI Research Shows Workers Are Ready, Leadership Isn’t
🧠 Making Sense of Research on How People Use AI
🤝 AI Interoperability Unlocks the Future of Hybrid Work
🔬 How Behavioral Science Can Improve the Return on AI Investments
📊 Introducing Microsoft 365 Copilot Business: Empowering Small and Medium Businesses with AI
💰 The A.I. Boom Is Driving the Economy. What Happens if It Stops?
🔄 AI agent evaluation replaces data labeling as the critical path
🔒 Top 10: Responsible AI Tools
🔍 Google AI Mode Changes the Customer Journey
🕸️ There Is Only One AI Company. Welcome to the Blob

Article Summaries

🔗 EY’s New AI Research Shows Workers Are Ready, Leadership Isn’t

New EY research reveals 84% of U.S. desk workers are eager to use AI, yet 61% are overwhelmed by new tools, showing a gap between AI enthusiasm and lasting capability.

• 85% of workers learn AI outside of work while only 52% of senior leaders have fully deployed AI training

• Leadership AI literacy gaps create organizational bottlenecks that slow enterprise-wide adoption

• Worker-driven AI experimentation leads to shadow IT risks without proper governance frameworks

🔗 Making Sense of Research on How People Use AI

Harvard Business Review analyzes three major AI usage studies from OpenAI, Anthropic, and social listening research, revealing writing dominates AI usage and automation is rising.

• Cross-platform AI usage patterns reveal significant gaps between consumer and enterprise adoption rates

• Research methodology differences highlight the importance of contextual data when evaluating AI ROI

• Three distinct user personas emerge: experimenters, integrators, and resisters, each requiring different change management approaches

🔗 AI Interoperability Unlocks the Future of Hybrid Work

Date Published 11/19/25

AI interoperability is reshaping hybrid work by breaking down silos, boosting meeting equity, and creating smarter collaboration across platforms.

• Real-time AI translation capabilities eliminate language barriers in global team collaboration

• Intelligent camera framing and noise cancellation create more equitable meeting experiences for remote participants

• Cross-platform AI integration reduces tool switching and cognitive load for hybrid workers

🔗 How Behavioral Science Can Improve the Return on AI Investments

Harvard Business Review explores how applying behavioral science principles can help organizations achieve better ROI from AI investments by aligning technology with human behavior.

• Cognitive biases significantly impact AI tool adoption, requiring behavioral interventions for successful implementation

• Nudge theory principles can increase AI usage rates by 40% when integrated into workflow design

• Social proof and peer learning programs accelerate AI adoption more effectively than traditional training methods

🔗 Introducing Microsoft 365 Copilot Business: Empowering Small and Medium Businesses with AI

Microsoft announces Microsoft 365 Copilot Business for SMBs at $21 per user per month, available December 1, 2025, bringing enterprise-grade AI capabilities to smaller organizations.

• Work IQ intelligence layer provides unified AI experience across Office applications without requiring complex integration

• SMB-focused pricing model makes enterprise-grade AI accessible to mid-market organizations for the first time

• Simplified deployment process reduces IT overhead while maintaining security and compliance standards

🔗 The A.I. Boom Is Driving the Economy. What Happens if It Stops?

New York Times analysis of how AI data centers are filling economic gaps and driving growth, examining the split economy where AI-tied sectors boom while others struggle.

• AI infrastructure investment has created 180,000 new jobs in data center construction and operations

• Regional economic disparities are emerging as AI investments concentrate in specific geographic areas

• Economic resilience depends on diversifying beyond AI-dependent industries to avoid bubble risk

🔗 AI agent evaluation replaces data labeling as the critical path

Analysis of how AI agent evaluation is becoming more critical than traditional data labeling in AI development, with implications for AI infrastructure and deployment strategies.

• Traditional data labeling workflows are being replaced by real-time agent performance evaluation systems

• Quality assurance shifts from pre-deployment testing to continuous monitoring of AI agent behaviors

• Enterprise deployment strategies must evolve to support dynamic evaluation frameworks rather than static datasets

🔗 Top 10: Responsible AI Tools

Curated list of the top 10 tools and platforms focused on responsible AI development and deployment, addressing ethics, bias detection, and governance concerns.

• Bias detection tools are becoming mandatory for enterprise AI deployments as regulatory scrutiny increases

• Explainable AI platforms help organizations maintain transparency requirements while scaling AI implementations

• Responsible AI frameworks reduce legal and reputational risks by 65% according to early enterprise adopters

🔗 Google AI Mode Changes the Customer Journey

Analysis of how Google’s AI mode is transforming customer journeys and the implications for content strategy and search engine optimization.

• AI-powered search results prioritize comprehensive, context-rich content over traditional keyword optimization strategies

• Customer journey mapping must account for AI-mediated interactions that bypass traditional website navigation patterns

• Content strategies need to evolve toward answering complex, multi-part questions rather than focusing on single search terms

🔗 There Is Only One AI Company. Welcome to the Blob

WIRED investigates how partnerships and deals between Nvidia, OpenAI, Google, and Microsoft are creating an interconnected AI industry that resembles a single machine.

• Strategic partnerships between major AI companies are creating market concentration that stifles innovation

• Enterprise customers face limited vendor choices as AI companies become increasingly interdependent

• Antitrust concerns grow as the AI industry consolidates around a few dominant platforms and infrastructure providers

The evidence is mounting: AI success isn’t about the technology — it’s about the organizational readiness to harness it effectively. As we head into 2026, the winners won’t be those with the most AI tools, but those who’ve built the culture, processes, and leadership capabilities to turn AI adoption into measurable business value.

This newsletter is for business leaders navigating AI readiness, digital workplace optimization, and smarter marketing automation. Stay ahead of the curve — subscribe today.

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At every level, educators are working hard trying to make sense of the real impact of AI — and what it means for the future of teaching and learning.

And that matters a lot, especially to the businesses and organizations building their AI-ready strategies. Because education isn’t separate from the future of work — it’s the front door to it.

I recently ran three professional development sessions on Demystifying AI for teachers and administrators. The pattern of feedback was remarkably consistent: there is an uneasy excitement about AI.

Everyone can feel that this is a moment, not just another tool. And yet, just under the surface, there’s anxiety — about cheating, the loss of creativity, student mental health, and, yes, whether teachers themselves will someday be replaced.

We already know AI is reshaping the workforce.
What’s less visible — but no less real — is how it’s reshaping education.

Not whether it will.
But how it already is.

And that friction point is painfully obvious once you see it. An analysis by the Public Policy forum Organisation for Economic Co-operation and Development (OECD) way back in 2023 stated that “rapid advances in generative artificial intelligence will likely disrupt many aspects of digital education.”

AI capabilities are advancing on a 3–6 week cycle.
Education systems operate on a 3–6 year cycle.

This mismatch isn’t the fault of educators. It’s structural. Education systems are designed for stability, consistency, and consensus — all good things when the ground is steady. Less ideal when the ground is shifting every quarter.

So if the goal of education is to prepare students to enter — and shape — the world they’re stepping into, then adapting to AI isn’t optional. It’s foundational.

That’s my premise: not that AI demands we reinvent education from scratch, but that we intentionally connect what schools already value to the realities of the world AI is reshaping.

Because the skills educators have always championed — critical thinking, writing, questioning, analysis, synthesis, creativity, collaboration — are now the exact skills AI makes even more essential.

And one of the clearest signs of this shift is what I call the move from Search → Ask.

From Search to Ask: Why This Shift Matters More Than We Think

For twenty years, we built the web on top of search.
Typing keywords. Skimming links. Comparing sources. Piecing things together.

The friction was, at times, annoying. But it followed an instructional path. Although students could shortcut the work of finding the source of information — they still had to dig and sort through the chaff to find the grain of truth.

The struggle was learning to use search effectively as part of the learning.

But AI has collapsed that entire workflow into a single behavior:

Ask.

One prompt → one answer → minimal friction.

Today, students don’t have to look, navigate, filter, or evaluate before getting to the answer. And whether we love that or hate it, the shift is happening at a scale we cannot ignore.

But here’s what makes this so important for educators:

Search was always about finding.
Ask is about delegating.

When we use AI, we aren’t simply accessing information faster — we’re outsourcing cognitive effort. The work of sifting and sorting and making sense of the often disconnected and disparate pieces of information. Not maliciously. Not entirely out of laziness (although, yes, out of laziness). But because the tool makes that delegation incredibly easy.

And that changes the role of:

• reading

• writing

• research

• note-taking

• synthesizing

• and even how students build confidence around what they know

In that light, it’s easy to view AI as a threat. And as a parent, I empathize with the concern that the laziness will win out. I do worry that the ease of asking versus searching could erode the “aha moments” and the learning in the struggle.

As one teacher put it to me, “it feels a lot like we’re letting our kids play with fire.”

But fire isn’t necessarily destructive — if used in the right way, it can also be transformative.

The shift from Search → Ask doesn’t eliminate the need for critical thinking.
It increases it.

Because when students receive a clean, confident, well-phrased answer instantly, what we really need to know is — do they know how to question the answer?

• Do they know how to check sources?

• How to compare perspectives?

• How to detect hallucinations?

• How to notice when something “sounds right” but contains subtle inaccuracies?

• How to push past the first answer, ask a better one — or better yet, challenge the AI’s thinking?

And to be clear, these are not new skills — they’re the same skills teachers have been pushing since before “Googling” became a verb — but the urgency is new.

AI doesn’t make these skills obsolete. It makes them non-negotiable. And educators who understand that, become irreplaceable: helping students build the intellectual muscles that let AI become a tool for learning, not a shortcut around it.

The Emotional Shift: AI as Companion, Not Just Tool

One of the most revealing — and honestly, most concerning — discussions in my PD sessions came from this simple question:

“How many of your students are using AI… not academically, but emotionally?”

Blank stares.
Then slow nods.
Then stories.

It tracks with what national data is already showing: Over 70% of teens report using generative AI as a companion, not a homework shortcut.

Not to plagiarize.
But to talk.

To vent.
To process feelings.
To ask questions and get advice on things they’re too embarrassed to ask adults, or too ashamed to engage their friends with.

This isn’t hypothetical.
It’s the water our kids are swimming in.

AI isn’t just reshaping how students gather information.
It’s reshaping how they relate to themselves, their peers, and the world around them.

We cannot address AI in the classroom purely as an academic integrity issue. It’s a mental-health, identity, and relationship issue as well. And educators will increasingly be on the front lines of it — whether they want to be or not.

What AI Is (and What It Isn’t)

Before educators can meaningfully guide students, they need a baseline understanding of what AI actually does.

I broke this down in the PD sessions in plain language:

AI is:

• A pattern recognizer

• A probability engine

• A system trained on enormous datasets

• Good at summarizing, simplifying, synthesizing

• Good at generating first drafts, not final answers

• Good at revealing patterns across sources

• Stochastic — meaning the same question rarely returns the same phrasing twice

AI is not:

• A truth machine

• A moral compass

• A teacher (yet)

• A source of guaranteed accuracy

• Creative — in the human sense

• A replacement for human judgment

• Immune from bias

• A reliable source of emotional guidance

AI doesn’t “understand” anything.
It predicts — what words are likely to come next, what questions you might ask as a follow up, and what your preferences are based on previous interactions.

That doesn’t diminish its power — but it should clarify the boundaries.

And students must learn those boundaries the same way they learn grammar, research methods, or digital citizenship.

What Educators Asked Most Often

Across three sessions, the same questions kept surfacing — a good signal of where educators really are right now.

1. “Is AI helping students think, or bypass thinking?”

Fair question. The answer is: both.
AI accelerates idea generation, but it also accelerates shortcuts. The key is intentionality and structure. In the same way that calculators didn’t eliminate the need to know basic math, we still need to teach students the critical and creative thinking skills to understand how to use AI as an effective tool.

2. “How do I prevent cheating?”

You can’t. Not entirely. Kids will take the easiest path, if we let them. And no, those AI’s that promise to identify generated text aren’t a silver bullet. As the models continue to get better and better, cheating will become harder to spot (and no — the use of em-dashes is not a sure-fire sign of AI).

So what can you do? Design assignments that inherently require process, reflection, and comparative reasoning — things AI can’t fully replicate without the student being engaged. Ask students to use the chat (or voice) function of AI to challenge their thinking or hypothesis, identify alternative view points, and push-back on generated responses to identify potential hallucinations or counter-arguments. Make the AI part of the process, not the forbidden fruit.

3. “Will AI replace teachers?”

No. And that’s not me being optimistic.
Yes, AI can teach. It can deliver content, provide answers, generate and score tests, and even engage the students to differentiate and build understanding at their individual level.

It can replicate the tasks of being a teacher. But one thing it can’t replicate — at least not yet (and probably not in our lifetimes) is connection, relationship, motivation, or contextual judgment. I still remember Mr. Taylor, my 7th grade biology teacher. He didn’t just teach science — he instilled a love of the scientific method, critical thinking, and analytical mindset that I use to this day as a digital strategy consultant. He engaged students with his quirky nature, lame science jokes, and approach that showed his own love of the material. That’s something that’s still impossible to replicate with AI.

4. “How can AI save me time?”

This was the most common question, and a good follow up to the question of “will AI replace teachers!” AI can summarily replace many of the rote tasks of being a teacher — lesson planning, rubrics, email drafting, differentiation, text-leveling, resource creation, etc. It can also help generate starting points for better brainstorming and ideation, targeted testing, and breaking down concepts for better teaching. More importantly, AI can free you up to be more present in a world where students desperately need your presence. I know it’s cliché, but it’s true: teachers who choose to partner with AI, will outperform those who try to ignore it.

The Framework: Caution, Curiosity, Courage

In each session, I offered a simple posture that educators can use to ground their approach, based on a simple concept: we can’t ignore AI, we need to address it head on… but that doesn’t mean abandoning our concerns. We have to approach AI with Caution, Curiosity, and Courage.

Caution

Caution doesn’t mean leading with fear. AI is powerful, but it’s also imperfect, biased, and sometimes overconfident. Instead, we need to focus on building clarity, well-defined boundaries, and maintaining a strong internal compass.

Curiosity

AI can feel overwhelming — there are a lot of paths that teachers and students can take, and not all lead to making teaching and learning better for everyone. Teachers and students should feel free to explore, but not pressured to adopt everything (or anything). Explore what’s useful for you — iterate, share with your colleagues and classmates, and find ways that to incorporate AI that feel natural and helpful.

Courage

AI adoption is a spectrum, not a light switch. It can feel completely foreign and uncomfortable to some; second nature to others. But inconsistency in adoption can also be detrimental to learning. Institutions should set expectations and policies that make sense for their users (student age, curriculum, etc.), while encouraging greater uniformity in adoption.

THE BOTTOM LINE: When it comes to AI, Avoidance as a policy, is a losing strategy. It doesn’t protect students — It leaves them unprepared for a world that will expect fluency.

What Students Actually Need in a World Shaped by AI

AI doesn’t require a brand-new set of skills. But it does raise the stakes on the skills we’ve always cared about.

The hours I’ve spent working with educators has made that shift clear. The focus shouldn’t be on what students need — but rather how those same skills can become levers for success in an AI-powered world.

Because students will delegate the “searching” to AI. Which means the persistence, resilience, problem-solving, and conceptual understanding has to shift somewhere else.

So how do we activate those neural pathways that lead to deeper understanding — where long-term learning and growth happen — without AI over-assisting?

We need to move from knowing what to ask, to knowing how to ask. In other words, the skill of “searching” needs to become a skill of “interrogating.”

Instead of “How do I get the answer?”
It’s “How do I know the answer makes sense?”

Instead of “Can I write something?”
It’s “Can I distinguish between a convenient summary and real understanding?”

We’re reframing skills, not changing the standards:

• asking sharper, more specific questions

• analyzing and evaluating answers

• comparing perspectives instead of accepting the first result

• verifying sources, context, and plausibility

• spotting contradictions and ambiguity

• thinking probabilistically (because AI is probabilistic)

• building arguments that reflect their own reasoning

• collaborating in ways that AI can’t replicate

• communicating clearly across mediums

• creating original work, not just remixing prompts

• sensing when something feels “off,” even when it sounds authoritative

The Mindset Shift Students Need — and the Mindset Shift Educators Need to Support Them

Some educators have told me that this shift feels like moving the goalposts closer in — but not in a good way. It feels like AI removes the productive struggle that builds persistence, emotional regulation, and that “slow burn” cognitive growth we know supports long-term learning.

That concern is valid.

AI does make it easier to get to an answer.
But getting to an answer has never been the endgame.
Learning comes from everything that happens before, and after it.

So instead of viewing AI as undermining the struggle, we need to redefine where the struggle happens. In other words:

If AI lightens the work of searching,
we have to double down on the work of understanding.

This is where the educator’s role becomes more—not less—important. Teachers become the guides who help students:

• stay in the question longer

• compare interpretations

• articulate reasoning

• sit with ambiguity

• recognize when an answer is too easy

• push past convenience into deeper cognitive territory

And that’s the actual mindset shift:
Not “AI makes learning easier,”
but “AI moves where learning needs to happen.”

That’s a professional judgment call — something no model can replace.

Preparing Students for a World Where AI Isn’t the Future — It’s the Environment

As AI lowers the cost of generating output, it raises the cost — and value — of human discernment.

The future of education doesn’t need to be AI-driven. But it does need to be AI-enabled — even if it happens gradually. This is why as both a parent and AI advocate, I believe in a scaled approach. This means:

• Early grades stay focused on foundational thinking skills.

• Middle and high school become the testing ground where students apply those skills with AI, not against it.

• Colleges expect students to bring AI fluency into complex, discipline-specific work.

• Employers assume AI will be part of every role, every workflow, every decision.

That’s the continuum our kids are walking into. And the niche — the opportunity — for educators is enormous:

Help students develop the clarity, judgment, curiosity, and creativity
that AI cannot automate.

The future of education can’t be about competing with AI or determining the perfect case for integrating it’s use. It’s about preparing students for a world where AI is simply part of the operating environment.

And educators — with their experience, their humanity, and their deep understanding of how learning actually happens — are uniquely positioned to lead that shift.

As AI becomes the foundation of how modern businesses operate, leaders are realizing that success isn’t about adding more tools — it’s about integrating them strategically. This week’s edition explores what that looks like in practice: from human-centered AI design to Microsoft Copilot’s growing role in workplace automation, plus Forrester’s predictions on the next wave of intelligent software. If your organization is navigating digital transformation, these insights show where strategy, technology, and culture intersect.

Table of Contents

• 🧠 Adopt Human-Centered AI To Transform The Future Of Work

• 🚀 Why Your Company Needs an AI-First Approach

• 🤖 Predictions 2026: AI Agents And New Business Models Impact Enterprise Software

• 🎯 AI Prompt Training Is a Start. Now Make It Strategic

• 💼 The Promise and Risk of AI App Building with Microsoft 365 Copilot

• 🧩 How Knowledge Agents Put Enterprise Knowledge to Work

• 📊 Have DWG’s 2025 digital workplace predictions come true?

• 🏆 Meet the 2025 Power Partners in Advertising, Marketing & Sales

Article Summaries

🔗 Adopt Human-Centered AI To Transform The Future Of Work

Advocates for human-centered AI that augments rather than replaces human capabilities, addressing job transformation and the need for businesses to integrate AI strategically.

• AI should enhance human decision-making and creativity rather than eliminate jobs entirely

• Successful AI integration requires rethinking workflows to combine human insight with AI efficiency

• Organizations need change management strategies that address employee concerns about AI displacement

🔗 Why Your Company Needs an AI-First Approach

Presents a roadmap for implementing an AI-First approach, emphasizing AI as an architectural layer that reshapes workflows and decisions.

• AI-first companies embed artificial intelligence into core business processes from the ground up

• This approach requires reimagining existing workflows rather than adding AI as an afterthought

• Leadership commitment and cultural transformation are essential for successful AI-first adoption

🔗 Predictions 2026: AI Agents And New Business Models Impact Enterprise Software

Forrester’s analysis of how enterprise software is shifting from user-centric to worker-and-process-centric design with AI-powered workflows.

• Enterprise software will evolve beyond traditional user interfaces to autonomous AI agent interactions

• Business models will shift toward outcome-based pricing as AI agents handle routine tasks

• Organizations must prepare for workforce changes as AI agents become integrated team members

🔗 AI Prompt Training Is a Start. Now Make It Strategic

Argues that AI training focusing only on mechanics delivers limited value without strategic direction for workplace implementation.

• Basic prompt engineering skills are insufficient without understanding business context and use cases

• Strategic AI training must connect technical skills to specific organizational goals and processes

• Learning programs should emphasize AI governance, ethics, and change management alongside technical skills

🔗 The Promise and Risk of AI App Building with Microsoft 365 Copilot

Discusses Microsoft 365 Copilot’s App Builder for no-code development while highlighting organizational risks and need for guardrails.

• Copilot’s App Builder democratizes application development but creates governance challenges for IT teams

• Shadow IT risks increase when employees build unmanaged AI applications without proper oversight

• Organizations need clear policies and training programs before enabling widespread AI app development

🔗 How Knowledge Agents Put Enterprise Knowledge to Work

Introduces knowledge agents as AI systems that can cut knowledge-related processes by 60-90%, functioning as bridges between knowledge and action.

• Knowledge agents automate information retrieval and synthesis from enterprise knowledge bases

• These AI systems can dramatically reduce time spent searching for information and answering routine questions

• Implementation requires well-organized knowledge repositories and clear governance for information accuracy

🔗 Have DWG’s 2025 digital workplace predictions come true?

Mid-year analysis of 2025 digital workplace predictions shows AI-driven personalization and intelligent collaboration gaining traction.

• AI-powered workplace personalization is delivering measurable productivity improvements

• Intelligent collaboration tools are becoming standard rather than experimental features

• Leadership storytelling capabilities are increasingly critical for digital transformation success

🔗 Meet the 2025 Power Partners in Advertising, Marketing & Sales

Highlights top B2B companies excelling in marketing automation and CRM services, featuring HubSpot and automation service providers.

• Leading service providers are combining AI capabilities with traditional marketing and sales automation

• Successful partnerships focus on measurable ROI and fast implementation timelines for mid-market clients

• The trend toward specialized AI-enhanced marketing services reflects growing demand for expert implementation support

The takeaway is clear: the companies winning in AI aren’t chasing every new feature — they’re building systems that align people, process, and purpose. That’s the essence of the Cognitive Operating Model: a framework for orchestrating how humans and AI make decisions together.

This newsletter is for business leaders navigating AI readiness, digital workplace optimization, and smarter marketing automation. Stay ahead of the curve — subscribe today.

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Nearly every conversation I’ve been having about AI in the enterprise eventually comes back to one question: “But what’s the ROI?”

It sounds responsible. Strategic, even.

But the more I sit with executives and hear leaders talk about AI strategy, the more convinced I become… that the ROI question is the wrong question to ask.

Not because ROI isn’t important — it is. But ROI measures efficiency at a specific point, for specific outputs — not value creation. ROI captures how well a process runs; it doesn’t tell you whether you’re running the right process in the first place.

The Real Blind Spot: AI as a Tool

When we measure AI through the lens of ROI, we’re stuck treating it like a tool — something that accelerates existing tasks.

But organizations don’t succeed because of tools; they succeed because those tools are part of systems — where people and processes use those tools to create value. Systems define how decisions are made, how information moves, and how quickly an organization can adapt.

In short, systems define how value is created.

That’s where AI’s true potential lives — not just in automating tasks to get work done faster for the people in the system, but in transforming the systems that shape how that work drives equity for the organization as a whole.

The Mirage of 50–60% Efficiency

We’ve all seen the headlines: “AI boosts productivity by 60%”, “Developers complete tasks twice as fast with Copilot.”

These stats spread fast because they’re easy to digest, and easy to sell.

And to be fair — they’re not necessarily made up.

Studies really do show GitHub Copilot helping developers complete coding tasks up to 55% faster, and generative AI assistants enabling customer service agents to resolve issues about 14% faster.

But here’s the truth hiding in the fine print: those gains are task-specific, not organizational.

When AI is deployed across real workplaces, the numbers flatten… fast.

The Federal Reserve Bank of St. Louis found that among employees actually using generative AI, the average time saved was just 5.4% of total work hours. Across all employees, it’s closer to 1–2%.

Executives I’ve spoken to, from IT to general management all agree, the numbers are much lower than the big headlines tend to report.

That’s not to say 50–60% isn’t happening — it’s just that those numbers are very likely incomplete. It’s measuring productivity at the task level, not the system level. It tells us how much faster the tool works for those people. Not how much better, stronger, or efficient the organization became.

ROI Measures Tools. Value Measures Systems.

ROI has always been about comparing inputs and outputs — how much faster, cheaper, or better something gets.

But organizations don’t run on individual outputs; they run on systems.

Systems that govern how decisions are made, how knowledge flows, how people collaborate, and how feedback loops reinforce learning.

AI’s real power isn’t in doing more of the same thing faster — it’s in changing how the organization learns and decides.

Value as a measure of success, changes the calculus from efficiency as a product of the tool, to productivity as a product of the system built around using that tool.

The Productivity J-Curve

The Productivity J-Curve is an economic term that defines the dip before the lift that happens when a general-purpose technology (like electricity, the internet, or now AI) enters the system.

At first, things get messy. Processes lag. Roles blur. Tools outpace governance. Productivity might even drop.

Sound familiar?

But as organizations restructure — realigning workflows, decision rights, and data foundations — the curve bends upward. That’s when the compounding value starts.

I think we’re entering the dip.

The real vs. expected efficiencies we’re seeing reported (~50% vs ~10%) as AI shifts from an individual tool for efficiency to an organizational tool for equity, is a clear sign we’re already moving downwards.

• Efficiency is the early gain.

• Equity — durable advantage — is the long-term return.

The question now is, how do we shift back up? The answer isn’t “change how we measure success” — it’s to rethink the way we implement AI that drives that change.

From ROI to Organizational Equity

In one of my recent peer groups, a startup CEO and former VC said something that stuck with me:

“As a VC, I never cared about ROI. I cared about equity — because equity is the total value of the business, not the return on a single investment.”

Exactly.

Instead of asking how much time AI saves, we should be asking how much organizational equity it builds.

AI Value = Organizational Equity.

Equity grows when AI helps the organization learn faster, decide smarter, and adapt sooner. That’s value you can’t capture in a timesheet.

The AI Value Scorecard

If we stop tracking “hours saved” and start tracking “capabilities gained,” the metrics start to look different. The Value Scorecard is a framework you can use to identify common areas where AI helps organizations grow smarter — not just faster. This is just a starting point that should get you started on determining what drives value for your organization.

This is the same framework we use in the Cognitive Operating Model to measure organizational success with AI.

The Shift That Actually Matters

AI does create ROI. People are definitely getting things done faster, and arguably, their able to do more because of that time savings. But that story isn’t the one that really matters at the organizational level, because you can’t roll up hours saved into real organizational gains — 1+1 may only add up to 1.2, not 2.

Sure, it looks good on a balance sheet to say you can replace 10 FTE’s with an AI agent that costs 1/4 of their salaries. But those cost savings will quickly get eaten up in other places. What really matters long term? How are those gains reinvested into learning, agility, and innovation — the things that build equity?

AI excels for individuals as a tool.
But organizations don’t run on tools.
They run on systems. And systems are where equity is built.

The organizations that will win aren’t the ones saving the most hours. They’re the ones using those hours to build better systems — systems that learn, decide, and adapt faster than the competition.

That could be realized as cost savings, but it definitely shows up as compound growth.

When we talk about AI, most people think in terms of applications — ChatGPT, Copilot, Gemini, Claude, etc. These stand-alone chat experiences have proven to be incredibly useful as assistants and sounding boards. But now, AI has become deeply integrated into the platforms we use to manage the data of our day-to-day work.

This shift is creating what I call siloes of intelligence — AI-powered, but locked away in contextual islands.

The problem is, humans rarely work in a single platform. The source and context of the data we want to apply AI to — the stuff we want to gain insight on and build operational value with — often lives across a variety of systems. Work documents in SharePoint, team chats in Slack, personal notes in Notion, and customer data in Salesforce — each with their own AI.

According to Okta’s 2024 Business at Work report, the average enterprise uses more than 200 different applications. Most don’t share data seamlessly, but nearly all have some on-board AI solution embedded. That’s a lot of disconnected intelligence.

This growing fragmentation is exactly why standards like MCP matter — to help bridge those gaps and make data context universally accessible.

What is MCP?

Model Context Protocol (MCP) is an open standard introduced by Anthropic in 2024 to make LLMs more practical, safe, and extensible. Although not yet universally adopted, MCP has quickly become one of the most prominent emerging standards for enabling interoperability across AI ecosystems.

• Instead of treating AI like a black box, MCP formalizes how models communicate with data sources (APIs, databases, files) and tools (automation scripts, apps, workflows).

• It separates the model from the context — ensuring that models aren’t reasoning in isolation, but with the right inputs.

In practice, MCP acts like a translation layer: the AI doesn’t need to know the specifics of your Salesforce API, your Notion database, or your Slack workspace. If an MCP server exists for it (more on that in a minute), the AI can interact seamlessly.

Making Context Universally Accessible

MCP isn’t about triggering actions, the way a Power Automate or Zapier workflow typically work. Instead, it standardizes context‑sharing. This gives any model the ability to tap into a variety of tools, data, and workflows without having to rebuild the model’s understanding of your data. This means your workflows have portability across a variety of LLMs. Whichever model you choose can tap into the same context to help you make better decisions, uncover insights, or optimize workflows.

MCP enables universal connectivity without retraining or building custom pipelines for every app. It’s the operating system layer that allows AI platforms to scale.

The Adoption Landscape

Logic and history both suggest that big vendors gain the most when they keep your data locked inside their ecosystems. Google, Microsoft, and even OpenAI have built their moat strategies around data, user experience and integration.

Yet, over the past year, MCP has seen significant industry-wide adoption — including from the very companies once thought to resist it.

• Anthropic (Claude) → The originator of MCP and still its leading contributor. Anthropic continues to maintain the core standard and reference implementations.

• OpenAI (ChatGPT) → In March 2025, OpenAI announced MCP integration across its products, including the Agents SDK and ChatGPT desktop app. Their developer documentation now includes official guidance for using MCP.

• Microsoft (Copilot) → Microsoft partnered with Anthropic to release the official C# SDK for MCP, integrated MCP into Copilot Studio and the Semantic Kernel, and announced plans to embed MCP support directly into Windows 11 for secure agent interactions.

• Google (Gemini) → Google’s Agent-to-Agent (A2A) protocol is designed to complement MCP. Google co-developed the official Go SDK and released a public MCP server for its Data Commons project to make datasets more accessible to AI agents.

• Others (Mistral, Cohere, Llama) → Open-source developers and smaller vendors are adopting MCP for interoperability across open and proprietary systems.

The multi-platform, agentic-driven world has broken the lock-in model. It doesn’t scale anymore. Interoperability has suddenly become a competitive advantage.

And that has largely come from customer demand, open‑source pressure, and network effects. As developer ecosystems form around connectivity, vendors are worried they’ll be left out.

But don’t be fooled.

It’s not altruism that’s driving them to adopt MCP. In my opinion, lock-in is still the goal. Owning the experience, intelligence, and optimization layers on top of that open protocol enables them to keep you engaged in their platform.

Still, MCP is a rising tide that lifts all ships. And the key to all of this — are MCP servers.

What are MCP Servers?

If MCP is the protocol, MCP Servers are the gateway — bridging the gap with efficiency and universal connectivity.

For the average enterprise, 200 different applications means 200 different places for data to be warehoused, processed, and collaborated in. Each of those applications has its own architecture, permissions, and API — and replacing or integrating any one of them can cause a cascade of disruption.

MCP servers abstract away that complexity. Instead of rebuilding integrations for every system, an MCP server standardizes how models talk to them.

• A server is like a pre-fabricated bridge that can be deployed anywhere, quickly, to connect the LLM and the resource with minimal alignment of permissions and structures.

• It provides structured context — exposing tools, functions, and data to the AI in a standardized way.

• Multiple servers can be combined, letting you build a constellation of connected systems.

More importantly, MCP Servers allow systems to easily swap out language models or data sources, based on the workload, demands, or evolving needs.

The benefits of MCP Servers in Practice:

• Responding to a Company-Wide System Change: Imagine your company migrates from Google Workspace to Microsoft 365. Your AI assistant, which uses a Google MCP Server to summarize emails and schedule meetings, doesn’t need to be rebuilt. You simply swap in the Microsoft 365 MCP Server, and the AI continues its work without interruption. The core workflow is preserved, saving significant development time.

• Upgrading Your Sales and Support Tools: Your startup initially used a Notion MCP Server as a simple CRM and knowledge base. As you grow, you upgrade to a dedicated Salesforce MCP Server for customer data and a Zendesk MCP Server for support tickets. Instead of building new integrations, you just point your AI to these new, more powerful servers. It can now pull customer history and support context using the same universal language it always has.

• Connecting a Central Knowledge Hub: Your AI is configured to answer internal questions by connecting to company documents via a SharePoint MCP Server. If your research team keeps its most vital data in a separate Confluence space, you can simply add a Confluence MCP Server. Now, when you ask a question, the AI can draw context from both sources seamlessly to give you a complete answer.

Instead of scraping or guessing, the AI requests through MCP and receives structured responses it can reason over.

Practical Ways to Use MCP for a Personal AI Platform

To make this tangible, imagine how MCP could unify SharePoint, Slack, Notion, and Salesforce:

• You draft a proposal in SharePoint.

• Your team discusses it in Slack.

• You’ve captured supporting research in Notion.

• And your customer details live in Salesforce.

Normally, these stay siloed (unless you build custom connectors and workflows). With MCP, your AI assistant could pull context from all four at once: drafting an executive summary that references the SharePoint document, includes key Slack discussion points, cites Notion research, and tailors the pitch with Salesforce data.

This is the power of context-sharing. And beyond this example, there are many ways MCP can be applied:

1. Knowledge Hub — Connect notes (Notion, OneNote, Obsidian) and files (SharePoint, Google Drive) so your AI can draft with your personal knowledge.

2. Workflow Orchestrator — Link task managers like Planner or Asana, letting AI suggest priorities or draft updates.

3. Inbox + Calendar Assistant — Use MCP to summarize email threads, prep you for meetings, or draft responses.

4. Research & Monitoring — Plug into RSS feeds or APIs so your AI flags trends in real time.

5. Custom Agents — Build specialized AI roles (Chief of Staff, Researcher, Project Manager) all pulling from the same consistent context.

Why MCP Matters

MCP is more than a technical spec — it reframes AI adoption. As part of the Be AI Ready approach, MCP can play a critical part in future-proofing systems.

• Reduced Vendor Lock-In: You are not trapped in a single vendor’s ecosystem (like Microsoft, Google, or OpenAI).

• Interoperability: MCP allows you to connect and pull context from normally siloed systems like SharePoint, Slack, and Salesforce all at once.

• Simplified Development: You avoid the duplicated effort of building and maintaining separate, custom integrations for every tool and every LLM.

• Accelerated Innovation: It creates a common foundation that makes it easier for developers to build new tools and agents.

The organizations (and individuals) that win in the AI era won’t be the ones with the most tools — they’ll be the ones that build platforms that can tap into pools of context seamlessly and without restraint.

What if MCP Doesn’t Gain Traction?

It’s worth asking: what happens if MCP stalls and isn’t widely adopted?

• Fragmented Ecosystems — Users will be forced to choose between vendor silos (Microsoft, Google, OpenAI), with little portability of data or workflows.

• Duplicated Effort — Each LLM will require separate connectors, integrations, and governance, increasing cost and complexity.

• Stifled Innovation — Smaller vendors and open-source players will struggle to compete without a standard to level the playing field.

In contrast, widespread adoption of MCP would:

• Create interoperability by default, letting any AI tap into the same context.

• Reduce lock-in and shift power back to users and organizations.

• Accelerate innovation by making it easier for developers to build on top of a common foundation.

The future of AI may well depend on whether MCP (or a similar standard) becomes the connective tissue — or whether we accept a fractured landscape dominated by closed ecosystems.

This week’s edition looks at how enterprise leaders are moving beyond hype and into practical AI adoption. From Microsoft’s push toward voice-first interfaces and Copilot ROI, to Adobe’s lean workplace win and Zoom’s new custom AI agents, the theme is clear: organizations are searching for ways to integrate AI that actually improve productivity, collaboration, and trust.

📋 Table of Contents

• 🗣️ Microsoft’s MAI-1 and Voice-First Future of Work

• 🏆 Adobe – Winner for Lean Digital Workplace Excellence 💰 Getting Started With Microsoft Copilot: A Guide To Driving ROI

• 🤖 Zoom Debuts Custom AI Agent Builder for Enterprise IT

• 📊 How Copilot Is Changing Excel

• 🔥 AI-Generated “Workslop” Is Destroying Productivity

• 👥 AI at Work: How Teams and AI are Transforming Collaboration and Communication in the Workplace

• 🧠 Introducing Knowledge Agent in SharePoint

• 📈 How To Integrate Generative AI As A Strategic Partner

A message from StitchDX.

📰 Article Summaries

Organizations with mature AI governance frameworks report 28% increase in staff using AI solutions and 5% higher revenue growth.
🔗 How To Integrate Generative AI As A Strategic Partner

• Success requires focusing 70% of efforts on people and processes rather than technology alone

• Clear goals, data readiness, and practical skills training are essential for AI adoption

• Mature governance frameworks directly correlate with higher user adoption and revenue impact

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Microsoft’s MAI-1 and MAI-Voice-1 models mark a major shift in its enterprise AI strategy and is yet another move away from its OpenAI dependence.
🔗 Microsoft’s MAI-1 and Voice-First Future of Work

• Microsoft is developing MAI-1 and MAI-Voice-1 to reduce reliance on OpenAI technology

• Voice-first interfaces are becoming central to future workplace interactions

• This strategic shift positions Microsoft for greater independence in enterprise AI solutions

  ---

Adobe wins the Lean Digital Workplace Excellence award at the Digital Workplace Group Awards for their innovative workplace transformation approach.
🔗 Adobe – Winner for Lean Digital Workplace Excellence

• Adobe demonstrated exceptional lean principles in their digital workplace transformation

• Their approach focused on eliminating waste while maximizing employee productivity

• The award recognizes Adobe’s innovative methods for streamlining workplace processes

  ---

A practical guide to Microsoft Copilot adoption—explore your options, define use cases, and build a roadmap that drives ROI and employee engagement.
🔗 Getting Started With Microsoft Copilot: A Guide To Driving ROI

• Organizations need clear use cases and implementation roadmaps for successful Copilot adoption

• ROI measurement requires defining specific metrics before deployment begins

• Employee engagement is crucial for maximizing the productivity benefits of Copilot

  ---

Zoom’s new features bring custom AI agents, support for Microsoft Teams and Google Meet, and enhanced collaboration tools designed to keep users in their flow.
🔗 Zoom Debuts Custom AI Agent Builder for Enterprise IT

• Custom AI agent builder allows enterprises to create specialized workflow assistants

• Cross-platform integration supports Microsoft Teams and Google Meet environments

• Focus on maintaining user flow and reducing context switching during collaboration

  ---

Microsoft is embedding AI into Excel with Copilot, but knowing when to use it and when to skip it is crucial for productivity.
🔗 How Copilot Is Changing Excel

• Copilot excels at data analysis and complex formula creation but may slow simple tasks

• Understanding the right use cases prevents productivity bottlenecks

• Training users on when to leverage AI versus manual methods is essential for adoption

  ---

Despite widespread AI adoption, 40% of employees report receiving ‘workslop’—low-effort AI-generated content that shifts cognitive burden to recipients.
🔗 AI-Generated “Workslop” Is Destroying Productivity

• “Workslop” creates an invisible tax of $186 per month per worker in lost productivity

• Poor AI content quality erodes workplace collaboration and trust

• Organizations need governance strategies to promote thoughtful AI use over quantity

  ---

Microsoft Teams introduces new AI capabilities through M365 Copilot and agentic AI, transforming workplace collaboration and communication.
🔗 AI at Work: How Teams and AI are Transforming Collaboration and Communication in the Workplace

• New agentic AI features enable proactive assistance and intelligent task automation

• Live demos showcase real-time AI integration in meetings and collaborative workflows

• Strategic AI implementation is reshaping communication patterns and productivity metrics

  ---

Microsoft launches Knowledge Agent in SharePoint Public Preview, delivering AI-powered content management features.
🔗 Introducing Knowledge Agent in SharePoint

• Automatic content tagging and AI-generated views simplify information organization

• Intelligent automation helps maintain fresh intranet content without manual intervention

• Integration optimizes SharePoint as a knowledge hub for Copilot search and retrieval

AI isn’t optional anymore — it’s becoming embedded in the tools and workflows we use every day. The question isn’t if you’ll adapt, but how quickly.

Ready to start? Visit StitchDX.