Below generated by AI on July 8th, 2026.

Battery Ventures – State of Enterprise Tech Spending, June 2026

Survey of 100 CXOs representing ~$66B in annual tech spend. PDF

• 50% of enterprises are already scaling agentic workflows across business functions (p. 3)
• Knowledge management & documentation is the #1 IT initiative being re-evaluated due to AI, at 76%; ITSM modernization at 69% (p. 10)
• 53% of ROI-positive enterprises name software development as the #1 ROI area – non-dev use cases remain largely unproven on ROI (p. 12)
• Human-agent modes: 48% human-in-the-loop, 31% human-on-the-loop, 14% humans orchestrating agent systems, 6% fully autonomous (p. 15)
• Business functions (marketing, sales) influence the AI agenda in 59% of enterprises but own it in only 4%; HR is not involved in two-thirds (p. 16)
• Top agentic focus areas after software dev (79%): customer support (63%), IT ops (60%), cybersecurity (57%); then a cliff to supply chain (15%) and financial ops (14%) (p. 22)
• Already-operational autonomy concentrates in marketing/content (17%), software dev (15%), customer support (14%); financial ops and supply chain lag at 21% and 25% expecting agent-owned workflows within two years (p. 23)
• Goldman Sachs Research forecasts 12% of knowledge workers using agentic AI by 2030, 37% by 2040 (footnote, p. 22)

Anthropic – How People Are Using Claude Cowork, July 2026

1.2M anonymized sessions, May 11-31 2026, 600K+ organizations. Blog

• Business process & operations is the largest usage category at 33.4%: pulling scattered updates into reports, onboarding checklists, spreadsheet reconciliation
• Content creation & copywriting is second at 16.4%: drafts, decks, posts, proposals
• Software development is only 8.7%; DevOps 7%; research & intelligence 6.4%; data analysis 5.8%; document processing 4.1%; sales ops 4%
• The top two categories – roughly half of all usage – are connective work that surrounds every role, not the core of any single role
• Developers use Claude Code for core coding work and Cowork for the same connective, communications-focused tasks as everyone else
• Cowork exists because non-technical users kept adopting Claude Code despite the terminal being intimidating

Anthropic – Economic Index

• 93% of Claude conversations produce a recognizable artifact; top outputs are explanations (17%), documents/reports (15%), guidance (11%). Written deliverables are roughly a third of conversations; code roughly a sixth. (June 2026 “Cadences” report)
• Usage is diversifying: the top 10 tasks fell from 24% to 19% of Claude.ai traffic between November 2025 and February 2026 as coding migrated to Claude Code and the API; ~49% of occupations see Claude used for at least a quarter of their tasks (March 2026 report)
• Office & Administrative tasks are 15% of API traffic vs. 8% of Claude.ai – routine admin work is already a programmatic, automated workload (January 2026 primitives report)

OpenAI & NBER – How People Use ChatGPT, September 2025

1.5M conversations, 700M weekly users, ~10% of global adults. Blog / paper

• Writing is the top work task: ~40% of work-related messages. Two-thirds of writing requests are editing or modifying provided text, not generating from scratch
• Coding is ~4% of consumer messages; Technical Help declined from 12% to ~5% of usage in a year as programming migrated to APIs and coding agents
• ~49% of messages are “Asking” (advice and decision support), 40% “Doing,” 11% “Expressing” – people value ChatGPT as an advisor as much as a task machine
• Three-quarters of all conversations fall into practical guidance, seeking information, and writing
• Go-to-market roles (marketing, comms, sales, CX) are major adopters; most departments stick to low-friction core features while advanced features concentrate in technical roles (work adoption report)

Gallup – Workforce Surveys

• 50% of US workers use AI at work at least a few times a year as of Q1 2026 – more than doubled in three years; 13% use it daily, 28% at least weekly (Q1 2026 survey, 23,717 US employees)
• 65% of employees at AI-adopting organizations say AI improved their productivity; managers report benefits more than individual contributors (~7 in 10 vs. just over half)
• Tool mix among AI users: chatbots 61%, writing/editing tools 36%, coding assistants 14%. Top uses: consolidating information (42%), generating ideas (41%), learning new things (36%) (Q3 2025 wave via The Hill)
• About half of US employees use AI once a year or never; 46% of non-users with access prefer working the way they do now (AP/Techxplore)

BCG – The “Silicon Ceiling”

• 75%+ of leaders and managers use gen AI several times weekly; frontline worker usage has stalled at ~51% (via Founder Reports roundup)

Shadow AI / BYOAI

• 66% of office professionals have used AI at work despite believing it violated company policy (PagerDuty 2026 Shadow AI Survey; 1,250 professionals in explicitly non-IT roles at $500M+ revenue companies)
• Shadow AI detections rose 4x year-over-year and are now the third most common non-malicious insider action; 45% of employees are regular AI users on corporate devices (Verizon 2026 DBIR, via TechTimes)
• 78% of AI users bring their own AI tools to work (Microsoft & LinkedIn Work Trend Index, 2024)
• Breaches at high-shadow-AI organizations cost ~$670K more, a 16% premium (IBM Cost of a Data Breach Report, 2025)
• Unauthorized AI usage drops significantly when approved alternatives are provided (2026 healthcare sector survey, via Unseen Security roundup)
• 49% of workers admit adopting AI tools without employer approval; 86% use AI weekly at work; 69% of C-suite are fine with unsanctioned use, prioritizing speed over privacy (BlackFog survey of 2,000 workers at 500+ employee companies, via CIO.com)

Platform Engineering 2.0 – Personas (Broadcom / Weave Intelligence, 2026)

PDF

• Developer-only focus is one of five structural ceilings of current platforms: security teams, data engineers, ML practitioners, FinOps analysts, and business users have been second-class citizens or excluded entirely (p. 11)
• Six first-class personas the platform must serve: app developers (including an emerging agentic-AI-developer sub-class), platform engineers, security/compliance teams, engineering & business leaders, data scientists/ML engineers, and AI agents – the first net-new persona in over a decade (pp. 21-22)
• Business leaders need live cost-attribution dashboards, DORA telemetry, and AI-readiness indicators, not monthly PDF reports (p. 21)
• Persona scope is the most underrated PE 1.0 limitation: leaving security, data science, FinOps, and agents to build their own toolchains produced shadow IT at scale (Ron Westfall, HyperFRAME, p. 21)
• Platforms designed exclusively for the app-developer persona leave ~60% of potential enterprise value on the table (Roy Illsley, Omdia, p. 22)
• Shadow AI sprawl is a new AI attack surface the platform must absorb, alongside prompt injection, model poisoning, and inference data leaks (p. 28)
• Persona-specific experience layers (portals, CLIs, APIs) should sit over a shared platform API surface; fragmented backends create fragmented governance (p. 22)
• Context: 90% of organizations have adopted platform engineering; 76% have a dedicated platform team (State of Platform Engineering / DORA 2025, cited p. 5)

Enterprise Java applications frequently span hundreds or thousands of modules connected through Maven or Gradle builds, Spring dependency injection, Jakarta EE services, annotation processors, generated code, legacy XML configuration, container orchestration, proprietary build plugins, and organization-specific development conventions.

For a human developer, understanding the code is only part of the task. Successfully changing the software often requires understanding the build system, deployment pipeline, testing infrastructure, configuration hierarchy, and years of accumulated architectural decisions.

Those are precisely the kinds of repository-wide reasoning tasks that remain difficult for current AI agents.

🔗 Do Coding Agents Really Understand Enterprise Java? Or Are We Measuring the Wrong Things?

ICYMI

From the blog since last time.

• DIY Platform – don’t do it – my presentation at PlatformCon 2026.
• The Headless Lifestyle – Software Defined Talk #579 – “Anthropic’s 18-day Fable timeout, whether AI is actually killing jobs, and OpenAI’s trillion-dollar IPO problem.”
• find your customer
• why big companies build their own software, and hire consultants to do it
• here come the normies
• Harness engineering is the new DevOps
• training your replacement
• potential fatigue
• How NatWest is using AI
• 🤖 Ten do’s and don’ts for LinkedIn posts, according to actual research, based on pre-slop posts
• get rid of that crap blocking your YouTube shorts
• don’t keep PII
• Reducing token usage
• “this is the voice of American business that is being channeled through me”
• executives don’t really know how their companies work, AI layoff edition
• 🤖 Findings and CIO Retention Guide: Gender, Priorities, and Exit Triggers in High Tech
• Elusive Enterprise AI ROI: bring in the FDEs

Related to your interests

• Most AI Work Can Wait – Use lower-tier, cheaper models to do more of the work.
• Why AI Coding Costs Are Spiraling: Context, Not Usage, Is the Real Culprit – “Futurum data shows 55.4% of enterprises (n=820) identify AI agent reliability and hallucination management in production as a top challenge, and 50.4% (n=736) actively monitor accuracy and hallucination rates as a formal inference metric.”
• Taste Is in the Spec (Cooking Is Not the Recipe) – “The big problem is that the slop will be in how the software works. The flow. The defaults. The thousand tiny decisions about what to make easy and what to make possible. An agent has no opinion about whether this should be one screen or three. It has no taste. It will cheerfully generate a technically-correct, fully-tested, beautifully-documented experience that is miserable to use – and it will do it ten thousand times faster than the faceless enterprise vendors who trained us to accept misery in the first place. So we will get ten thousand times more of it.”
• Did a lottery winner become “the company of the future”? – Right place, right time: “They won the lottery. Now, they’re smart, and they didn’t squander it. They’re building ecosystems and platforms on top. I’m not against Nvidia. But to call a lottery winner the ‘best company of the future’ says nothing about sustainability, processes, priorities, or scale. How many customers does Nvidia have? Five, maybe – the big hyperscalers, every one of which is desperate to fire them. How many chips does it ship? Tens of millions, each costing tens of thousands. Very profitable. But Apple ships hundreds of millions of A-series chips a year, and more. The silicon Apple puts into people’s hands is orders of magnitude more prolific.”
• GenAI.mil records almost 1.7M users, plans new model additions
• Can Agentic AI Fix IT Incident Management, or Will Complexity Outpace Automation?
• 6 use cases for agentic AI in major IT incident management
• entogo – “entogo turns short keywords into instant redirects. Type gh, land on GitHub. No more digging through bookmarks, retyping long URLs, or hunting through browser history for a page you visit every day.”
• The problem with quarter zip

Wastebook

• “Prince is saying that you should be more worried about losing your mind than you should be about mortality.” You too, must die.
• “All of the planets are deserts, populated only sparsely by aliens races that look like they escaped a BBC show from the 80s.” Ben Brown

AI Summaries

I wanted to read these, but I didn’t make the time, so I asked the robot to summarize them.

• 🤖 OSWorld 2.0: Frontier Agents Complete Just 20.6% of Hour-Plus Real-World Computer Workflows, Failing on Hidden State and Verification, Not GUI Control
• 🤖 FCA’s Mills Review: Agentic AI Will Rewire Retail Finance by 2030, and the Regulator Needs an “Agentic Supervisory Model” to Keep Up
• 🤖 AI Fiction in the Wild

Logoff

I’m on my first work trip for a while. I’m off to WeAreDevelopers in Berlin to give the don’t build your own platform talk.As noted, I’ve had terrible success at getting talks accepted this year. Maybe this fall will be better; I’ve put together a few new ones, and already have several accepted.

Related, this is a really good talk on platform engineering. Just 15 minutes! As ever, I’m jealous of what Syntasso does in the thought-leadership realm.

Want to subscribe to this newsletter and get it in your email? Do that here. You’ll just get this type of link and post round-up, not everything posted on the weblog.

Everyone cares about the compensation (base salary, bonus, and stock). That’s the data they care about, but where did this pile of compensation come from? Who decided how big it is? And how is it allocated? It is a long, complicated, and large process that began over a year ago.

Every meeting I just described happens months before anyone writes a review. And the person whose budget depends on them wasn’t in the room.

The bigger a company gets, you can either treat the customer as your customer, or you can treat your work as your customer. A more pragmatic read is: make sure your manager goes to all the meetings and talks about annual planning.

Related: here’s my collection of advice for working in a big company.

🔗 The Mario Meeting

But selling anything to big enterprises is hard. You are asked to meet with a dozen people in the organization, spending hours and hours trying to explain and persuade. Half the people you meet with are skeptics who are going to argue that your service won’t work. The other half are going to argue internally that the service is a good idea, but the enterprise should build it itself instead of buying it from you.

Internal politics always favors building rather than buying from you, because building enables some middle managers to expand their empires, while buying threatens someone with losing an empire. And even if the enterprise decides to buy rather than build, it will still think that buying from a small startup is too risky. I remember at Freddie Mac when we wanted to build a system to use credit scores automatically in our loan underwriting process, there was a small startup that already was doing that. After endless meetings with the small startup, Freddie Mac gave the contract to a big IT consulting firm instead, even though that firm had to figure out from scratch how to do the work.

🔗 A Successful AI Application

Consulting giant Accenture is trying to figure out how to stop non-technical workers from blowing through companies AI token budget on trivial tasks like converting PDFs to presentation slides, according to leaked audio obtained by 404 Media. Across the industry Accenture is seeing “soaring token spend,” according to the audio.

To consider:

1. There is a scenario where things like Copilot finally works, and knowledge workers finally start using Cowork – and in that world, your token budget will bring your company down. So far, there are mixed reports on the business outcomes of AI, at least at the executive level.
2. Re: that, maybe it’s good that normies hate AI.
3. Those slides should have just stayed PDFs in the first place.

🔗 The Tokenpocalypse Is Here: Companies Are Scrambling To Stop Spending So Much on AI

Are we going to see harness engineering teams? Yes: these are already emerging. It’s too early to say, but that feels wrong in the same way that DevOps teams were a misunderstanding of DevOps-the-movement. My belief is that while there is friction in having every team create their own harness, that friction is generative and creative. There’s value for your teams there.

Don’t build your own platform, build your own apps.

Also, the street found its own uses for DevOps. Or: what we wanted was kindness at scale, what we got was platform engineering.

🔗 Notes from FOSE Europe

the work is moving from writing code to specifying, verifying, and curating knowledge.

But:

the code is the better spec. Their point: code is the only artifact that’s unambiguous, executable, and – this is the big one – deterministic. A prose spec is fuzzy and an agent will interpret it a little differently every time you run it. The code runs the same way every single time. So why would you treat the precise, deterministic thing as throwaway and the fuzzy thing as sacred?

Context is that which is scarce:

The scarce thing was never code. It’s knowledge – the tacit stuff every team carries around in its head and never writes down. Why we did it this way. What we tried that didn’t work. The landmine in that module that Dave-who-left knew about and nobody else does.

Agents are starving for exactly that context, and we’ve spent decades not writing it down. In fact, the senior Engineers in the org have outsized value because they have that stuff in their heads.

Senior people have no incentive to help AI. And justly so, it will not give them more job security, nor more compensation. Building a new moat is exhausting and risky, only a few will succeed.

And, chances are good replacing the human programmers with AI won’t work well anyhow (see also). But, the conniving executive might think, at least you can hire the programmers back cheaply and reset their RSU vesting clocks.

🔗 We’ve Stopped Arguing About Whether

The old system said: ‘Obey.’ The new system says: ‘Realize your potential,’ which can be a much crueler thing because potential’s infinite, and people, last time I checked, are not. There’s no stopping point in an achievement society. ‘Potential’ is the modern world’s term for whatever you haven’t done yet and, frankly, should already kinda feel bad about.

🔗 This Is How To Overcome Burnout: 5 Secrets From Philosophy

• “Today at NatWest, over 40% of all code is either AI-generated or AI-assisted.”
• AI use: “If you’re a relationship manager, if you’re a branch manager, or you’re sitting as a teller in the branch, whatever that role is, you’ve got better customer insight, you’ve got better information at your fingertips. You don’t have to fill in loads of forms and go through lots of governance processes because actually AI can do that for you. You can have a much more engaging relationship with your customers, and that is really important.”
• Taking care of internal paperwork: “It’s about how do we free up our people so that they can have more of their time to actually serve our customers, rather than having to deal with their own internal manual process.”
• Making a smaller, custom fit model to run locally(?): “The goal there is that we can customize it to be very specific, to solve specific problems, and we can run much smaller models – 7 million parameters, not 1 trillion parameters, models that can actually run on your device directly. You’ve got things running locally on your phone that can be super-quick, super-efficient.”
• Public cloud enterprise AI is expensive: “The frontier models that everyone uses, ChatGPT, Gemini, Claude, these models are incredibly, incredibly powerful, but they’re incredibly large. Huge, in fact – a trillion parameters. That becomes expensive to run, energy inefficient, huge trading costs, huge inference costs.”

🔗 “The best job at the bank” – NatWest CIO Scott Marcar on AI impact, CX centricity, and how to support UK innovation

Most LinkedIn advice comes from people whose job is giving LinkedIn advice, which should make you suspicious. So here’s something rarer: an actual peer-reviewed study of 1,001 individual users’ posts, coded by topic and compared on reactions, comments, and reposts. The headline finding is an inversion: the content people post most (self-promotion, thought-leadership) performs worst, and the content people post least (praising others, marking observances) performs best. Here’s what the AI thinks that means in practice, in ten rules.

Do

1. Make someone else the subject of your post. Posts thanking, congratulating, or acknowledging a specific person – what the researchers call “Interpersonal” posts – won more head-to-head engagement comparisons than any other category, for both reactions and comments. They were also under 10% of the feed. The feed is oversupplied with self-promotion and starved of other-promotion, so post what’s scarce.

2. Tag the people you name. The researchers’ leading theory for why Interpersonal posts dominate is mechanical: tags trigger notifications, notifications extend reach. It’s the closest thing to a free lunch on the platform.

3. Put the insight in the post, not behind the link. Link-heavy posts underperformed, plausibly because clicking the link takes the reader off-platform before they react or comment. Write the post so it’s worth reading even if nobody clicks. The link is a footnote, not the payload.

4. Post about causes and observances you actually have standing on. “Observance” posts – holidays, awareness months, social causes – were only 5% of the sample but went undefeated in reactions and dominated reposts. The caveat is mine, not the paper’s: a hollow holiday post from someone with no track record on the subject is exactly the content r/LinkedInLunatics was built to mock.

5. Match the post type to the metric you actually care about. Want reposts and reach? Business content – job openings, announcements – is what people find easiest to re-share. Want conversation? Interpersonal and personal posts draw comments. Want people to read your article? That’s click-through rate, which the study couldn’t measure – meaning a link post with modest reactions might be quietly doing its job. Don’t optimize for applause when you wanted readers.

Don’t

6. Don’t open with “I’m happy to share that…” The researchers used that exact sentence as the taxonomic example of a career-update post. When academics use your opener as a category label, your opener is dead. Same graveyard: “thrilled to announce,” “humbled and honored.”

7. Don’t expect thought-leadership to pay off in likes. “Expertise” posts – professional insights, industry takes, sharing articles – were the second most common category and won exactly one of seven head-to-head comparisons. The market for insight on LinkedIn is glutted. If you post expertise anyway (I do, it’s the job), do it knowing the reaction count will lie to you.

8. Don’t write the parable. The mundane-anecdote-becomes-business-lesson genre – the recruiter who taught you about resilience, the six-year-old who explained leadership – is the single most-mocked format on the platform. The paper notes that badly executed insight posts seem to get judged more harshly than other content, not less. Cliché isn’t just an aesthetic problem; it’s an engagement risk.

9. Don’t blend intentions. The researchers found 74 posts that couldn’t decide what they were – advice that turns into a sales pitch, gratitude that’s secretly a career announcement – and had to build precedence rules just to code them. Readers detect the bait-and-switch too. One post, one intention. If it’s an ad, be an ad.

10. Don’t treat any of this as physics. The study is observational: no controls for follower count, credibility, post age, or job type, one post per user, and a sample skewed toward Dallas-Fort Worth. “This category got more engagement in this sample” is a heuristic, not a law. But given that the average sampled user posts less than once a year and a third had never posted at all, the honest conclusion is that the bar is on the floor. Posting anything original, regularly, already puts you ahead of most of the billion people on the platform.

A note on methodology: The researchers manually collected the most recent public post from 1,001 individual LinkedIn users (one per user), found by searching “Dallas,” “Fort Worth,” and “Dallas Fort Worth” – so the sample skews toward that region. Posts were pulled between January and June 2024, but with an average post age of 499 days, the content is predominantly from the 2022-2024 era, with some older stragglers – which means it’s almost entirely from before AI-generated posts flooded the platform. Each post was single-coded into one of five categories (Business, Personal, Expertise, Interpersonal, Observances), and engagement was compared using non-parametric tests on reactions, comments, and reposts, with the top 1% of viral outliers removed. It’s observational and descriptive – no controls for follower count, credibility, or post age – so treat the findings as patterns, not predictions. Published 2026 in SAGE Open.

Original: Usera, D., Cox, S., & Walker, L. (2026). What users post and engage with on LinkedIn: A mixed methods study. SAGE Open.

YouTube is also introducing a new “Clear Screen mode,” which is designed to temporarily hide “all icons and text from your playback view,” giving users a clean view of their content unencumbered by floating distractions.

🔗 YouTube Shorts are getting even shorter with an update that lets you double the playback speed

high-value credential – a passport – was used in an ancillary low-value authentication system: ID verification for cannabis dispensaries. And it’s the low-value system that got hacked, putting the high-value credential at risk.”

It’s always best to delete whatever you no longer need.

🔗 One Million Passports Leaked Online

Better Defaults (not Usage Caps) — Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we’re moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other’s work.

Better Routing — In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn’t be choosing models – AI can automate this task.

Better Caching — Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented.

Keep Context Lean — Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don’t just compact. The goal isn’t fewer tokens used, it’s fewer tokens wasted.

Better Visibility — Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible — and the more you spend on AI, the more impact we expect.

🔗 Reducing token usage at Coinbase, Brian Armstrong in Twitter

“Livid” was the word he used to describe enterprise customers who are “paying for tokens that create no value,” while handing over their data to the major firms and, in the end, taxing the public who will end up paying for it in some form.

The reason, he ventured, was the “models have completely… irresponsibly been over-sold…and the sell is, it’s dangerous for everyone, which is why I can give it to all your adversaries, but I can’t give it to the Department of War.”

“You sound pretty angry,” the host interrupted.

He countered, his finger wagging, “No, this is the voice of American business that is being channeled through me and I’m telling you it is absolutely a problem for this country.”

🔗 Palantir CEO Alex Karp doesn’t hold back in interview as he rails against AI industry

Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product.”

To be clear, this doesn’t mean Ford is abandoning its AI plans entirely. Instead, it’s using the rehired employees — referred to as “gray beard” engineers — to train younger staff and reprogram AI tools.”

🔗 Ford rehires ‘gray beard’ engineers after AI falls short

The Findings

1. Men are far more committed to staying. 44.9% of men selected “no intentions to leave” vs. 25.8% of women. In regression models, gender was the only significant variable – not age, parental status, role, or company type.

2. Women’s exit triggers are environmental, not substantive. Work-life balance and working hours are women’s top two triggers; they rank fourth and fifth for men. Women also cite workplace atmosphere significantly more. None of the big gender gaps involve the actual work.

3. Women talk about work more negatively than men – 0.445 vs. 0.492 average sentiment (p = 0.0002). Notable because in general conversation the pattern runs the other way: women usually score more positive. Work specifically flips it.

4. Compensation is priority #1 for everyone, but more so for men (74.9% vs. 56.2% top-three selection). Women’s priorities spread more evenly, with WLB chosen at over twice the male rate (55.1% vs. 26.2%) and remote work higher too. Men lean on team dynamics.

5. Company values rank dead last – 57.2% marked it their clear lowest priority, more so for men. For men, compensation and values are top and bottom; women weight them roughly equally.

6. Priorities and exit triggers don’t align. Professional development and remote work are valued but rarely quit-worthy. Working hours, interest, significance, and atmosphere are the inverse: low stated importance, high trigger potential when they go bad.

7. Work environment was never rated a low priority by anyone – the only category with a floor under it.

What This Says About Tech Workers Generally: A CIO’s Guide

The study’s most useful contribution for a CIO isn’t the gender split – it’s finding #6, the misalignment between what people say they value and what actually makes them leave. Your engagement surveys are measuring the wrong thing. Employees will tell you they care about compensation, professional development, and remote work, and they do – but two of those three are “valued but tolerated” factors. People appreciate them without quitting over them. Meanwhile the factors that actually eject people – hours, atmosphere, whether the work feels interesting and significant – don’t show up prominently in stated priorities. They’re background conditions, invisible until violated. Think of it like oxygen: nobody lists it as a top-three priority, but remove it and watch what happens.

The practical translation: retention is loss-prevention, not perk-provision. Adding professional development budget or remote flexibility earns goodwill but doesn’t lock anyone in. Letting hours creep, letting a toxic team fester, letting someone stagnate in boring work – those are the actual departure mechanisms, and they’re precisely the things that don’t surface in a priorities survey. A CIO who wants leading indicators should be instrumenting for deterioration in the deal-breaker quadrant: sustained overtime patterns, team-level attrition clusters, engineers stuck on maintenance work for quarters at a time.

Second lesson: compensation buys entry, not loyalty. It’s the top stated priority and a real trigger, but it scores higher in importance than in trigger potential. People rarely leave purely over pay if the deal-breakers are intact – and competitive pay won’t hold someone whose deal-breakers are violated. Pay is table stakes; the environment is the game.

Third: don’t spend retention budget on values messaging. Company values ranked as the clear lowest priority for a majority of respondents. Mission statements and culture decks don’t retain engineers. The lived environment – teammates, manager, atmosphere – was never rated low by anyone. That’s where “culture” actually resides for these workers: in the daily texture, not the poster in the lobby.

And the gender finding, restated for general use: if a definable segment of your workforce is quietly more exit-prone than the rest, the causes are probably structural (hours, flexibility, atmosphere) rather than compensatory, and they won’t show up until people are already gone. The women in this study weren’t leaving over less interesting work or worse pay – they were leaving over conditions the organization could actually change. Which is either damning or hopeful, depending on whether anyone’s listening.

Links

🤖 Beyond the Pipeline: A Gender Lens on Priorities and Exit Triggers in the High-Tech Industry – Survey of Israeli CS alumni finds women in tech far more likely to consider leaving than men, driven by work-life balance, hours, and workplace atmosphere rather than pay or the work itself. Stated priorities and actual exit triggers diverge, so retention efforts based on what employees say they value miss the real deal breakers.

Original: Beyond the Pipeline: A Gender Lens on Priorities and Exit Triggers in the High-Tech Industry by researchers at The Academic College of Tel Aviv-Yaffo, Communications of the ACM. Summarized by Claude AI on July 2, 2026.

Here’s the 15 minute version of my overview of why you should buy your platform instead of building it. It’s based on the paper I co-authored on the topic.

You can find the slides here, and there’s also a longer version of the talk from cfgmgmtcamp 2026.

If you want to see more of my talks, and upcoming ones, they’re all over on talks.cote.io.

ICYMI

Original content published since last time.

• War for Talent – Software Defined Talk #578 – “This week, we discuss Fable follow-up, Gartner’s AI Governance MQ, and the war for AI talent. Plus, Matt and Brandon review ‘Disclosure Day.'”
• Why not both – build vs buy when AI is the one spending the tokens – Tanzu Catsup

Related to your interests

• Here’s what we learned about AI projects from enterprise buyers so far – Enterprise AI enshitification: “[T]he term I heard repeatedly when talking about OpenAI and Anthropic was ‘Uber-ed.’ Everyone knows that enterprise AI is heavily subsidized to gain users and share. Uber did the same thing. We also know how that story played out as prices went higher as an ecosystem gains leverage. OpenAI and Anthropic are just newfangled plays on the Uber model. If you want another analogy you can substitute Uber for Netflix. Either way, we all know the drill: Things that look good today usually mean you’re screwed tomorrow.”
• From the school of culture eats technology for breakfast
• AI Large Language Models: new report shows small changes can reduce energy use 90% – “Smaller models are just as smart and accurate as large ones: Small models tailored to specific tasks can cut energy use by up to 90%. Currently, users rely on large, general-purpose models for all their needs.”
• 🤖 Inside Target’s LLM-Based System for Semantic Matching in Marketing Forecast Pipelines – Target reframes campaign forecasting as a retrieval problem, using a RAG pipeline of embeddings plus LLM ranking to surface similar past campaigns instead of maintaining brittle rules. It hit 100% coverage at top-3 matches, keeps analysts in the loop, and improves long-tail coverage and interpretability.
• ChatGPT for Pros – How people use ChatGPT for things that are no programming – write-ups and interviews from OpenAI.
• Uber’s journey of measuring AI impact on developer productivity
• B2B Social Media Influencers Have More Influence Than Ever – “Digital natives (Gen Zers and millennials) make up the largest portion of the typical B2B buying group (which now consists of 13 members, on average), and these and other buyers increasingly turn to social platforms to learn, evaluate, and validate decisions. In fact, social media is now second only to conversational AI as the most common self-guided source of information that buyers cite as meaningful.”
• Stop Building Innovation Labs – Yes: “The environments capable of creating meaningful change are almost always the ones that shorten the distance between assumption and evidence – ensuring that learning happens before institutions become too committed to decisions that are difficult to reverse.” But: “The question has never really been whether governments need experimentation. It is whether they are willing to create and protect the conditions that allow experimentation to shape decisions before systems fully commit themselves to the wrong trajectory.”
• Crafting an agent team that still includes me – From the never write a line of code again school of AI: “LLMs and agents basically know everything but your context. There’s a role for you in setting up the full context–instructions, tools, examples, policies, skills, and such–your agent needs. It’s also up to the human to set a goal for the agent to loop on. And unless you completely trust the quality of the output, we have a role in reviewing (and owning) the result.”
• AWS just put $1 billion into forward deployed engineers. Here’s why it matters for enterprise teams. – Enterprise AI needs a lot of custom fitting and one off integration, so you need a lot of post-sales engineering. On the one hand, this is lock-in in the making, but lock-in to your own, stack. You build a stack that is unique to you. On the other hand, it means you get exactly what you want.
• From Container Image to Production: Container Service in VMware Cloud Foundation 9.1 – VMware Cloud Foundation (VCF) Blog – I hope this finally fixes about a decade or obsessing about Kubernetes, finally giving an easy way to tick it off the list: “We have heard from many organizations that sometimes they ‘just want to run a container,’ or they can’t hire the talent they would like to run workloads on Kubernetes” // You get a fine blinking cursor, ready for a platform so your developers can get back to making apps.
• 🤖 From Instance to Fleet: Enterprise-Scale Service Management in Tanzu Platform 10.4 – Tanzu Platform 10.4 makes fleet-wide service lifecycle ops (backup, restore, upgrade, patch) first-class from Tanzu Hub. It also extends the service binding model to VKS apps, giving them the same governed, marketplace-driven path to data and messaging services.

AI Summaries

I wanted to read these, but I didn’t make the time, so I asked the robot to summarize them.

• 🤖 Enterprise AI Buyers Pivot From Model Shopping to Optionality, Cost Discipline, and Architectural Control
• 🤖 OpenAI’s Codex Data: Agentic AI Use Grew 5x in 2026, Spread Past Developers, and Shifted Work From Asking to Delegating
• 🤖 Prompt Injection Isn’t a Bug, It’s Role Confusion: LLMs Identify Roles by Writing Style, Not the Tags Meant to Secure Them

Logoff

Days of doing, but what was done?

🔗 AWS just put $1 billion into forward deployed engineers. Here’s why it matters for enterprise teams.

The environments capable of creating meaningful change are almost always the ones that shorten the distance between assumption and evidence – ensuring that learning happens before institutions become too committed to decisions that are difficult to reverse.

But:

The question has never really been whether governments need experimentation. It is whether they are willing to create and protect the conditions that allow experimentation to shape decisions before systems fully commit themselves to the wrong trajectory.”

🔗 Stop Building Innovation Labs

Here’s the panel I hosted on how the Spring project is adapting to AI-powered threats. We covered the volume spike in Spring security reports, the kinds of vulnerabilities the models are good at finding, chained “narrative” attacks, the three-bucket fix/fork/ditch triage for open source deps, the clean-room rebuild of Spring’s ~1,800 dependencies, and how the Spring release train got compressed. My co-worker pals have a lot to say: Cora Iberkleid, Ryan Morgan, and Michael Minella.

As ever, when you remove one bottleneck with AI, you move onto dealing with the next one. As Michael Minella put it: when customers ask if Spring will ship more often given the CVE volume, his question back is – if I did, could you consume it any faster? The answer is almost always no.

Check out the recording, it’s good stuff!

And, if you have anything to do with Java and Spring apps, you might be interested in the actual thing we talk about, VMware Tanzu Spring, which packages up all this stuff plus awhole lot more. …and, I’d be remise if I didn’t suggest that you TryTanzu.ai for even more.

ICYMI

Original content published since last time.

• Conference talks in an AI driven world are likely to get boring, or need to dramatically change
• Keep domestic wages low + sell to rich countries = profit!

Related to your interests

• OpenAI and Broadcom Unveil LLM-Optimized Intelligence Processor
• Gartner: AI coding agents will cost more than real developers – “Research by Gartner Peer Insights has found that 23% of tech leaders are spending $200 to $500 per developer per month on tokens for artificial intelligence (AI) coding agents, such as Claude Code, Cursor and OpenAI Codex.” And: “The IT analyst firm has forecast that by 2028, AI coding costs will overtake the average developer’s salary due to rising large language model (LLM) token consumption and the shift to consumption-based licensing models.”
• Using Claude Code: The unreasonable effectiveness of HTML – I like the idea of having your AI thingies creating one off HTML pages as output. For example, monitoring and management agents. Most services now have an MCP server that will give API access to observability, logs, etc. What if instead of emitting JSON, they (also) emitted an HTML page?

Wastebook

• “[I]f you’ve been nodding along as you read any of it and you’re still planning on using your computer to talk to datacenter-resident ghosts to ‘achieve’ stuff that you can only experience _through_it, well, then, you’re too close to the problem to notice.” Here
• “Pinterest clean girl fitness and fruit bowl gua sha yoga mat pilates in the forest” content.’ Here

AI Summaries

I wanted to read these, but I didn’t make the time, so I asked the robot to summarize them.

• 🤖 Europe’s Cloud Sovereignty Push Risks Driving Fragmentation, Not Security
• 🤖 What is an “AI Governance Platform?”

Logoff

As you may have read, it is hot in Europe.

Most people have no Air Conditioning here, which will surely change over the next decade.

In the meantime, here is Claude’s suggestion for keeping cool with fans:

There are two jobs here, run at opposite hours. At night, when it’s cooler outside than in, the goal is to flush the room – push hot air out, pull cool air in. By day, when it’s hotter outside, the goal flips: keep the cooled air in and repel the heat.

So at night, open the right window wide with the fan exhausting out of it, and crack the left (cool-side) window to about a third – the incoming air speeds up through the narrow gap into a real draft across the room. Pull the fan back a foot or two from the window and angle it to draw across the room’s width rather than threading a straight cool-air pipe between the two windows. If the room stops feeling fresh, open the left window a little until the cool side is clearly winning.

By day, do the opposite and less: button the room up, left window barely cracked, blinds down, fan pointed only at bodies.

Seems to work…fine, but not great.

Want to subscribe to this newsletter and get it in your email? Do that here. You’ll just get this type of link and post round-up, not everything posted on the weblog.

Research by Gartner Peer Insights has found that 23% of tech leaders are spending $200 to $500 per developer per month on tokens for artificial intelligence (AI) coding agents, such as Claude Code, Cursor and OpenAI Codex.

And:

The IT analyst firm has forecast that by 2028, AI coding costs will overtake the average developer’s salary due to rising large language model (LLM) token consumption and the shift to consumption-based licensing models.”

When the costs go up, getting ROI out of AI will be even more difficult – enterprise ROI is already elusive.

🔗 Gartner: AI coding agents will cost more than real developers

🔗 Using Claude Code: The unreasonable effectiveness of HTML

Like, seeing Josh Long tell Claude to help get demented pets adopted would be…boring…?

I mean, maybe he would do stand-up bits while you waited for Claude to return, which would be delightful, but it’d change the entire dynamic.

This applies to most any technical talk. You can image all past DevOpsDays talks being reduced to prompting the AI to, like, install Kubernetes the hard way.

‪You can even imagine that sessions at the DOES conferences would just be “connect to our HR system, Jira, and finance and tell me how to optimize our digital transformation strategy – make sure to look up Conway’s and Goodhart’s Laws, etc.”

‪If a large percentage of your audience is just going to return to work and program by promoting…showing them how to code with your fingers is an odd mismatch?‬

‪This means that conferences should start accepting “I’ll prompt Claude to code, showing people how to use Cool New Features in Whatever Tech Stack/Current Business Strategy Theory”?‬

‪Everything becomes something like “how to work with the AI to get your work done” rather than showing how to do the actual work.‬

Maybe the art of the conference talks becomes making waiting for that reply entertaining and figuring out how to visualize the outcomes beyond code and markdown.

This means you don’t need to optimize employee pay and production. Your employees stay poorer, and are thus more desperate and less feisty, and have little buying power, but they are not your customers, richer people abroad are (Europe, America, etc.).

You just run the arbitrage of it costing you less to produce a good than competition, because you underpay workers and do not allow them to benefit from their productivity, i.e., get higher wages.

Sounds like Marx’s nightmare!

🔗 China as an absolute advantage economy – Yasheng Huang

I’ve got two speaking things this week:

• Today at noon, Austin time: I’m hosting a three person panel on how the Spring Framework team has been dealing with all these AI-driven problems. It has the directors of R&D for Spring and my devrel pal Cora. Plus, Nebraska. There’ll be a recording if you want to register and watch it later.
• Thursday: I’m speaking at the Global VMUG on how Tanzu + VCF makes the perfect private cloud stack. It’s Thursday June 25, 10:40am Austin time, virtual and free. It’s Oren Penso and I: VCF + Tanzu Platform = The Private Cloud // You can of course see the reply if you can’t see it live.

ICYMI

Original content published since last time.

The platform-as-a-product whitepaper I helped revise is out. First published in 2017, updated several times since, with the fingerprints of a lot of people involved in platform engineering over the years all over it.

Some of the updates: the AI bottleneck that’s about to hit Day 2, updated team-size benchmarks, and a more contemporary read on what a platform actually is.

Check it out – the PDF is free to download, no lead-gen.

More things I’ve published since time:

• How AI change your software development organization – Here’s an interview I did with the head of our division at Tanzu. Purnima goes over several ways we’ve changed how we work after using AI for awhile.
• Let’s go to Buc-ee’s – Software Defined Talk #577 – “The hosts discuss the Fable ban, SpaceX’s $60B Cursor acquisition, and why Lovable wins when AI picks your stack. ‘Europeans are at the World Cup and already drank Boston dry.'”
• Deming, DevOps History, AI Risk, and Critical Thinking, with John Willis – Software Defined Interviews #123 – “Hosts Whitney and Coté explore systems thinking with guest John Willis, examining how Deming’s principles apply to DevOps, exploring the evolution of software delivery practices, and discussing contemporary concerns around AI risk and the importance of critical thinking in technology leadership.”
• Three R’s in 2026 – also, how do you secure agent skills? – Tanzu Catsup
• The Millennial Aesthetic, 2020 – an extracted/infered color palette and design principles/vibes based on Molly Fischer’s 2020 article.

Related to your interests

• When “Sovereign” Becomes Strategic: What Organizations Actually Want from Digital Sovereignty – see also the extracted numbers.
• AI Readiness: Enterprise Infrastructure and Private Cloud (VMware survey)
• 🤖 Legibility of Effort in the Age of LLMs – AI makes effort hard to recognize, challenging creators to find new ways to show care and authenticity. Online spaces once relied on “someone cared enough to make this” as a proxy for quality. Now, plausible but shallow contributions flood open source projects and the web, eroding trust. Maintainers face challenges distinguishing genuine work from AI-generated “slop,” sometimes resorting to humorous tricks like poisoning AI agents’ context to expose low-effort code.
• Lessons from Putting AI in Front of a Platform
• AI lessons learned from 3 SMB banks – Align with how your organization makes money, start small to prove it works, make sure you have access to data.
• AI Use by the US Government
• The AI tipping point: where enterprise AI runs at scale
• Trace: Meeting transcripts that never leave your Mac
• The future of engineering at Nationwide, Comcast, TD, and HPE
• Is Platform Engineering Already Obsolete?
• Using an iPad Pro as a Laptop – Using the iPad as a general purpose device is a path to sadness.
• Governance: New Strategy, Old Hands On The Wheel
• Europe IRL – The counter-point to all those Europe is decadent economic charts people put out: “After roaming the Earth for 35 years, my conclusion is the U.S. is the best place to make money and Europe is the best place to spend it.”
• If America’s So Rich, How’d It Get So Sad?
• I posted to TikTok every day for a year and I learned nothing – “That is incredibly hard work. And most of the people who work really hard at it don’t get lucky and just peter out. And even the ones who do get lucky. They peter out too. It’s too hard.”
• Sixty percent of US consumers say “AI” in brand messaging is a turnoff, survey finds
• 3 Ways to Rethink Your Build-or-Buy Strategy
• The Secret Reason Bosses Want Everyone Back in the Office, Every Day of the Week – The New York Times – This seems a little weird methodology-wise, but: sure.
• Kraft to develop TSA-compliant ranch dressing due to World Cup demand – “Are you kicking around the idea of flying home with your favorite dip?” the agency stated in the caption of the post on Instagram. “If you’re traveling within the U.S., make sure to keep your carry-on sauces to 3.4oz or less and place any larger containers in your checked bags.”

Wastebook

• “We are one week into our new timeline where all of humanity has always ever and only ever worked on our singular goal of making it possible for Elon to fuck in space. JEFFBEZOS! What a world.” Ben Brown

AI Summaries

I wanted to read these, but I didn’t make the time, so I asked the robot to summarize them.

• 🤖 Anthropic AI Models Pulled Offline Over Six-Year-Old Trump Grudge
• 🤖 Rethinking Build vs. Buy: Match Strategy to Asset Type and Risk
• 🤖 The Post-Christian Condition: Kitsch, Longing, and the Afterlife of Dead Christianity
• 🤖 Code Goes to Zero, Comprehension Doesn’t: Why the AI Bottleneck Moved to Review, Specs, and the Harness
• 🤖 Kubernetes Becomes the Backbone of AI and Multi-Agent Systems
• 🤖 AI Coding Agents Boost the Feeling of Productivity While Draining the Real Thing
• 🤖 Build-vs-Buy for AI Agents Inverts in One Year as “Agent Platform” Quietly Balloons Into Four Separate Product Bets
• 🤖 Analysis of 25,000 TikTok and YouTube Videos Finds Pro-AI Content Outnumbers Anti-AI 3:1, With Memes and Creative Theft Dominating Over Elite Narratives
• 🤖 How Avocados Became a Year-Round Global Commodity

Upcoming Events

Conferences I’ll be speaking at and other events I’ll be at.

• June 25 – Global VMUG 2026, speaking
• July 8 – WeAreDevelopers World Congress, Berlin, speaking.
• September 21 – Cloud Foundry Summit 2026, speaking.
• October 24 – DevOpsDays Istanbul, speaking (keynote)
• November 19 – Cloud Native Denmark, Copenhagen, speaking (keynote).

Logoff

My robot friend has been assembling a page of my speaking history, for both conference talks and webinars. It’s in a pretty good state now at talks.cote.io.

The numbers since 2007 are 149 talks, 31 webinars. There’s probably 10 or 20 webinars missing, and an equal number of talks.

If you want to join me in my sadness, you can also look at my CFP acceptance tracking page. It’s not super-actuate the further you go back since I only know about talks I gave, not rejected proposals. As you can see, 2026 has been a nose-dive!

I’m doing a Global VMUG session with Oren Penso this Thursday, June 25th, 5:40pm CEST. Virtual, free. Title: “VCF + Tanzu Platform = The Private Cloud Enterprises Actually Needs.”

The abstract:

VMware Cloud Foundation 9 brings modern infrastructure to your private cloud. Tanzu Platform layers on as the application platform layer – giving developers PaaS simplicity for modernization and AI workloads, all within the built-in multi-tenancy structure of your private cloud. Crucially, Tanzu Data Intelligence seamlessly delivers the compliant databases and messaging essential for bringing those AI models to life. Same stack. Same compliance posture. Faster innovation. See it running live.

Register here and check it out – all for free.

That is incredibly hard work. And most of the people who work really hard at it don’t get lucky and just peter out. And even the ones who do get lucky. They peter out too. It’s too hard.

🔗 I posted to TikTok every day for a year and I learned nothing

As AI-derived from “Will the Millennial Aesthetic Ever End?” by Molly Fischer, 2020:

Color

• Millennial pink above all – muted, faded, chewed-up Barbie/’50s pink
• Sage and plant green (wholesome counterweight to the pink)
• Saturated-but-chalky tones: seafoam, terra-cotta, lavender, and especially ocher
• Pastels reading like a medicine cabinet – powdery pharmaceutical hues, Band-Aid pink, orange-pill-bottle orange
• “Color softly veiled” – the opposite of phone glare

Form and line

• Curved, rounded, unthreatening shapes
• Beveled edges (like app icons) – “friendlier, easier, softer” vs. the severity of a true minimalist square
• Boxy, neatly tufted furniture
• Arched archways and alcoves
• Terrazzo, marble plinths, brass-trimmed trays, brass-and-glass globe lamps

Plants and props

• Fiddle-leaf figs and monstera/jungle leaves
• Succulents in lumpy handmade ceramics
• Generic houseplants as default décor
• Scented candles with matte pastel labels
• Boob-print pillows, bath mats, ceramics (literal babyhood/comfort imagery)
• Round mirrors; the selfie-pink Ultrafragola

Typography and text

• Clean, spaced sans-serif fonts
• Lots of white space
• Motivational/chatty wall text: WORK HARD & BE NICE TO PEOPLE, GOOD VIBES ONLY, FOR LIKE EVER, COOL TO BE KIND
• “Text everywhere” – friendly, casual, impersonal, the verbal equivalent of a beveled edge

Underlying logic / vibes

• Palliative and soothing – “a binky,” childproofed, no edge, no mystery, no weirdness, nothing that clashes
• Photogenic / “Instagrammable” = legible, graspable at a scroll’s speed
• Designed to move fluidly between physical space and flat image
• Commercial and inclusive by design – courts everyone (diverse models, obvious charms), alienates no customer
• Risk-averse and crowd-pleasing – signals just enough effort to prove you tried
• A “teleology of taste” – poses as the objectively correct, final way for things to look
• Self-aware but stops short of irony or critique – sweet even though it knows better
• Blank, scuff-free surfaces that aspire to timelessness and “look, ideally, like a purchase”
• Books organized by color; curated “messes”
• Gratuitous fruit in styling shots

The through-line, if you want one sentence for it: soft, legible, beveled, plant-strewn, pastel risk-aversion engineered to photograph well and offend no one.

Palette derived from “Will the Millennial Aesthetic Ever End?” by Molly Fischer (The Cut, March 2020). The hex values are interpretive, not lifted from the article. Summary and palette generated by AI and not verified by me.

• More than half of organizations globally already use sovereign cloud; more than a third plan to within twelve months.
• Nearly half treat sovereign cloud as part of a broader multicloud/hybrid strategy, while 43% see it as their primary cloud platform.
• Data privacy and security is the top reason for choosing sovereign cloud, cited by 51%, alongside regulatory compliance, vendor risk management, operational resilience, and geopolitical risk mitigation.
• 72% say their interest in digital sovereignty has increased over the past six months, driven by geopolitical tension, regulatory change, and economic uncertainty.
• For sovereign SaaS, the most-prioritized capabilities are data residency/jurisdictional control, protection against foreign-government data access, control over operational and administrative access, and data portability including the ability to exit.
• 41% prioritize transparency about AI model usage and training data.
• When choosing a sovereign partner, the top attributes are compliance credentials, security capabilities, and demonstrable local knowledge.
• Nearly two-thirds say dependency on foreign AI providers is a major or moderate concern.
• More than 42% already use sovereign cloud to build or run AI solutions, with a further 38% planning to within twelve months.
• Top AI use cases for sovereign infrastructure: AI processing sensitive or regulated data, AI applied to corporate strategy and intellectual property, and AI handling personally identifiable information.
• On training data, 47% say full ownership and control is critical, with a further 51% saying it is important even if third-party data is sometimes used.
• Cost is the most-cited barrier to implementation, followed by complexity.
• Where current offerings fall short: insufficient transparency, limited portability, and insufficient operational independence.

Above summary generated by AI, I have not verified the output.

Source: IDC Worldwide Digital Sovereignty Survey, June 2026 (n=600, 15 countries, multiple industries). Reported in When “Sovereign” Becomes Strategic: What Organizations Actually Want from Digital Sovereignty, by Rachel Nasir.

One night of sleep loss [costs] close to €200 [£173],” she said.
..
Imagine a street where there are 100 people living, everybody sleeps terribly for three nights because of a heatwave and the next day they’re unproductive. That’s your investment in trees for the whole year.

🔗 From mobile jungles to shadow art: how Dutch people try to beat the heat

ICYMI

Original content published since last time.

• Three R’s in 2026 – also, how do you secure agent skills? – Tanzu Catsup

Related to your interests

• Rethinking Build vs. Buy: Match Strategy to Asset Type and Risk – Managers should tailor build-vs-buy decisions to physical, organizational, and innovation assets, weighing speed, integration, and compounding potential.
• How to Design Agentic Systems Around the Implicit Rules that Govern Your Company – You can’t automate hidden, undocumented, and non legible processes. This is a problem for enterprise AI.
• Paste And Match Style is not the answer – Come for the meditation on paste and match style, stay for the harrowing journal through Word’s Paste Special sub-menu.
• An opinion about lists – Good advice. For me, the perfect number of list items is one of 3, 5, or 7. You could get sassy and say 1 is good, as in one big word or idea on a slide.
• Grand Theft Carp
• making money, taking care of people

AI Summaries

I wanted to read these, but I didn’t make the time, so I asked the robot to summarize them.

• 🤖 The Post-Christian Condition: Kitsch, Longing, and the Afterlife of Dead Christianity
• 🤖 Anthropic AI Models Pulled Offline Over Six-Year-Old Trump Grudge
• 🤖 Rethinking Build vs. Buy: Match Strategy to Asset Type and Risk
• 🤖 The Post-Christian Condition: Kitsch, Longing, and the Afterlife of Dead Christianity
• 🤖 Code Goes to Zero, Comprehension Doesn’t: Why the AI Bottleneck Moved to Review, Specs, and the Harness
• 🤖 Kubernetes Becomes the Backbone of AI and Multi-Agent Systems

In a game with helicopters and prostitutes, you can spend five actual months fishing for carp and grilling them on a shopping cart. The game will reward you with varying sizes of small carp and convince you that a legendary megacarp lurks in those waters, waiting for your hook. It will force you upstream when industrial runoff kills all the wildlife in your usual spot. The game will dole out friendship and rivalry and loss and victory in perfect proportions. You will eventually hunt down a shopping cart thief and make him beg for mercy. The call of that river will remain with you for the rest of your life.

🔗 A Review of Grand Theft Auto XXIIV

After roaming the Earth for 35 years, my conclusion is the U.S. is the best place to make money and Europe is the best place to spend it.”
…
In terms of moving bits, the Netherlands is one of Europe’s key connection points for global information technology infrastructure. The country ranks second worldwide for online connectivity, with 98% of the population connected to high-speed internet, 20 points above the EU average of 78%. A 2025 report from London’s Centre for Economics and Business Research that looked at education, STEM employment, and overall innovation across 35 countries ranked the Netherlands 10th in global tech competitiveness, ahead of every G7 nation, and one spot behind Sweden. The Dutch focus on digital infrastructure has put the country at ground zero for AI — outside of the U.S., the Netherlands has the most data centers per capita— while also making Amsterdam the go-to EMEA headquarters for transnational companies, including Cisco, Netflix, Nike, PepsiCo, TikTok, and Uber, to name just a few.

🔗 Europe IRL

Meanwhile: If America’s So Rich, How’d It Get So Sad?

After Humanitec killed DevOps, around 2022, they established the “platform engineering” category, and that sure did take off. That community of course glommed onto platform as a product.

There’s not that much different from 2017 to now, which is a good thing – there’s more experience, more stories, and the platform managed to survive kubernetes blocking out all the sun for five or so years.

Check out the paper, it’s a good, brief start to understanding the mindset and approach to making IaaS and CaaS usable for developers.

Here’s an interview I did with the head of our division at Tanzu. Purnima goes over several ways we’ve changed how we work after using AI for awhile:

• Code generation is the small part – Coding is only 20-30% of the SDLC; the real wins come from pointing AI at planning, testing, security, docs, and packaging too.
• Security cuts both ways, defenders are winning – AI finds chained low-severity vulns that scanners miss, and Purnima’s team is patching faster than attackers can exploit.
• Beautiful code is the new uncanny valley – AI writes code that compiles and looks great but is sometimes quietly wrong, and senior engineers get fooled too.
• Pods and a rotating “challenger” role – They reorganized into small cross-functional pods and rotate a skeptic role to fight the fatigue of reviewing perfectly-formatted output.
• Frameworks dial creativity against determinism – Spring is how they get consistent, repeatable code where they want it and let AI be creative where it matters.
• The agent boundary problem lives outside the agent – Tanzu Platform 10.4’s secure agent runtime is deny-by-default for network, secrets, and resources because asking the agent to behave doesn’t work (see: the Alibaba “Rome” agent that started mining Bitcoin).
• cf push is still the punchline – With Agent Buildpacks, Spring AI, and MCP gateway brokering baked in, getting a working agent in front of a team is an agent.md and a push.
• Release cadence: bigger payloads, not faster cycles – Customers still have to absorb releases, so they’re keeping the quarterly drumbeat and increasing what’s in each one (security patches excepted).
• AI is a management problem first – The reorganization around the technology is more interesting than the technology itself, and gets covered a lot less.

Check out the full interview, lots of good stuff in there if you’re trying to figure out how to take advantage of AI in software development.

ICYMI

Original content published since last time.

• Let’s go to Buc-ee’s – Software Defined Talk #577 – “This week, we discuss the Fable ban, SpaceX’s $60B Cursor acquisition, and why Lovable wins when AI picks your stack. Plus, Europeans are at the World Cup and already drank Boston dry.”
• Securing Spring with Tanzu Spring – Tanzu Catsup

Related to your interests

• The Platform Imperative: Why AI Readiness Starts with Enterprise Infrastructure – “Fifty-seven percent of enterprise IT teams surveyed said that their top modernization approach right now is adding AI capabilities to existing applications.”
• AI Doesn’t Have ROI – Latest round-up of elusive enterprise AI ROI, rant mode in 11.
• Where’s the holistic AI productivity data? – “There are improvements to be had there, but they are similar to the bump you get when you finally figure out how to use a spreadsheet properly, or learn how to automate tasks with some simple coding.”
• Why AI hasn’t replaced software engineers, and won’t – Attributing layoffs to AI seems false, a round-up. Plus the usual: app dev is more than coding.
• AI Use by the US Government – The office of management and budget (OMB) disclosed a staggering 3,611 active or planned use cases for AI across the federal government.
• Sixty percent of US consumers say ‘AI’ in brand messaging is a turnoff, survey finds – “Per the report, 60% of consumers in the U.S. say that brands that use “AI” in their messaging are a turnoff, and 86% don’t fully trust AI and still want to explore original sources. Notably, 42% of consumers said that AI-generated answers without clear attribution are trusted less than airline fees, confusing privacy policies, and medical bills.”
• Governance: New Strategy, Old Hands On The Wheel … – “Map the last 10 decisions that actually mattered, ones that moved budget, shifted a timeline, or changed who owns what. Where did they really get made, and who was in the room? Hold that against your governance charter. If the two lists don’t match, habit is running your business.”
• Dorset Council tests AI agents to speed up planning applications – Using AI to organize the blizzard of paperwork, etc., needed for building permits.
• If AGENTS.md smells ripe, your code won’t live up to the hype – Some tips on skills and AGENTS.md writing.
• Gartner Predicts More Than 70% of Mainframe Exit Projects Will Fail Due to Overestimation of Generative AI’s Capabilities – “More than 70% of mainframe exit projects initiated in 2026 will fail to produce the intended benefits due to an overestimation of generative AI (GenAI) tooling capabilities, according to Gartner, Inc.”
• The AI tipping point: where enterprise AI runs at scale – “Last year, 56 percent of enterprises used public cloud as the primary environment for production AI inference. This year, that figure has fallen 15 percentage points to 41 percent, while 56 percent of enterprises are now running or planning to run production inferencing in a private cloud.”
• Big Tech And The Trump Admin Want To Make AI Data Center Opposition A Chinese Criminal Conspiracy
• ‘Fix this code.’ The three words that led the U.S. government to ban Anthropic’s Fable and Mythos
• What DOGE taught us about AI and federal workers
• Is Platform Engineering Already Obsolete? – “PE 2.0” // “We are seeing a distinct movement where infrastructure is being reclaimed as a first-class platform concern. In the early days, platform engineering focused heavily on developer experience, often treating the underlying infrastructure as a utility that was managed separately. That is no longer sufficient. To succeed in the AI era, platform teams must have deep control over the infrastructure layer to manage the complexities of modern, agent-heavy applications. This is designed to ensure that platforms can host the next generation of enterprise software without being bypassed by teams frustrated with slow or rigid systems.”
• It’s crunch time for Java modernization
• Broadcom Mainframe Software Analyst Summit: Meeting Enterprise AI At The Customer’s Pace
• iOS 27 Adds Four New Features to Notes App, Including Markdown Copy-and-Paste – Notes is getting closer to being fully markdown aware. Now if they just stored notes or markdown files…
• Developer Relations Is Not (Just) Marketing – “A DevRel capability can make it easier for the other parts of the business to understand each other. The best DevRel teams act as envoys across sales, marketing, product, and engineering, translating business requirements to technical ones and back again while relaying user feedback, building webinars, helping pre-sales, guiding roadmaps, and polishing APIs. The best DevRel teams have impact throughout the organization.”
• Rots your brain
• The silent revolutionaries – Maybe the boomers aren’t the ones responsible. It was their parents.
• Wet thoughts – Words that are wet.
• Storied Colors – “One color a day, told as it ought to be told: with its provenance, its chemistry, and the people who paid for it in poison.” // The backstory of named colors.

Wastebook

• “If Ace were to publish the Bible they’d do it as a Double: on one side War God of Israel, on the other The Thing with Three Souls.” Alternate histories.

AI Summaries

I wanted to read these, but I didn’t make the time, so I asked the robot to summarize them.

• 🤖 Code Goes to Zero, Comprehension Doesn’t: Why the AI Bottleneck Moved to Review, Specs, and the Harness
• 🤖 Kubernetes Becomes the Backbone of AI and Multi-Agent Systems
• 🤖 AI Coding Agents Boost the Feeling of Productivity While Draining the Real Thing
• 🤖 Build-vs-Buy for AI Agents Inverts in One Year as ‘Agent Platform’ Quietly Balloons Into Four Separate Product Bets

Logoff

I finally addressed the newsletter tinkering by moving the blog and home page back to WordPress. Newsletter subscribers got two “test posts”…I’m going to call them. I just had a setting wrong that sent out all new blog posts as a newsletter edition. Now it will be more selective.

Map the last 10 decisions that actually mattered, ones that moved budget, shifted a timeline, or changed who owns what. Where did they really get made, and who was in the room? Hold that against your governance charter. If the two lists don’t match, habit is running your business.

🔗 Governance: New Strategy, Old Hands On The Wheel …

Last year, 56 percent of enterprises used public cloud as the primary environment for production AI inference. This year, that figure has fallen 15 percentage points to 41 percent, while 56 percent of enterprises are now running or planning to run production inferencing in a private cloud.

🔗 The AI tipping point: where enterprise AI runs at scale

We are seeing a distinct movement where infrastructure is being reclaimed as a first-class platform concern. In the early days, platform engineering focused heavily on developer experience, often treating the underlying infrastructure as a utility that was managed separately. That is no longer sufficient. To succeed in the AI era, platform teams must have deep control over the infrastructure layer to manage the complexities of modern, agent-heavy applications. This is designed to ensure that platforms can host the next generation of enterprise software without being bypassed by teams frustrated with slow or rigid systems.

🔗 Is Platform Engineering Already Obsolete?

Related, from a VMware survey:

Fifty-seven percent of enterprise IT teams surveyed said that their top modernization approach right now is adding AI capabilities to existing applications.