I was hoping it would do things like carry around amphorae to refill my wine between each sip and plates of succulent grapes, and then help me stumble home after the bacchanal.

Instead, it’s chainsaws.

Enterprise AI: ROI, Cost, and Reorgs

• How to make better enterprise AI ROI metrics and dashboards – Encode what you should next in AI ROI metrics, don’t leave the decision up to post-metrics vibes. “Make the decision before making the metrics. Translate the decision into language that people use before packaging it up into quantifiable metrics. For example, ‘Should we scale this AI feature from 10% to 100%?’ or ‘Should we fund a newer v2 model training next quarter?’ or ‘Should we replace manual workflow X with automation?’ Clear decision language is needed to form clear images of success. In other words, you have to be able to say your decision out loud before you can imagine what success will look like. This is your best protection from getting fooled by a nice Metric Theater with pretty dashboards.”
• more marketing context, more better AI for marketing – AI just makes good things gooder, and bad things badder: “The real work starts before evaluating tools or issuing an RFP. Marketers need defined personas, clear messaging frameworks, content standards, governance processes, reusable content, and alignment on measuring success. AI performs only as well as the context it’s given. Organizations that establish the richest context, the clearest governance, and the sharpest understanding of what makes their brand distinctive have an advantage, regardless of which solution they choose.”
• Reorganizing the dev teams at Visa to take advantage of AI – When you speed up the SDLC process, you should collapse silo’ed teams into one team to prevent human bottlenecks based on handoffs. Example of that at Visa.
• Tokenomics – how AT&T burns through a trillion+ tokens a month (and rising), but still manages to drive down enterprise costs through ‘moneyballing’ – Over a trillion tokens a month and still growing double digits, but the per-token cost keeps falling thanks to open source models and picking the right model for the job.
• 🤖 Target’s Real AI Moat: Agent Architecture, Autonomy, and Governance – The edge isn’t the models, it’s the architecture, governance, and operations built around them, starting with deciding whether a problem needs an agent at all.

How People Actually Use AI

• How people use AI in 2026: TV, homework, the DMV – Google looked at 15 million Gemini conversations and found people mostly asking about TV, homework, and government paperwork. Work is 13.5% of it, and almost none of that is trying to automate anything. Also: what the study leaves out, which is most of enterprise AI.
• AI moral panic – AI amplifies your current state, moral panic edition.
• 🤖 Google Quit the AI Race to Bet on World Models Over Recursive Self-Improvement – Google didn’t lose the race so much as leave it: DeepMind thinks coding agents building better coding agents is a dead end, and is betting on world models instead.

Platform Engineering

• Not my mistake, now my problem – Whatever your developers build above your infrastructure is a platform, and they’ll soon flit away from it, and now for you it’s: not my mistake, and now my problem.
• 🤖 You Already Have a Developer Platform, and It Is Someone Else’s Mistake – You already have platforms, you just didn’t choose them, and a decade of Cloud Foundry ops-to-developer ratios says one deliberate platform costs a few more people, not a new department.
• 🤖 Shift-Left Gave Developers the Work Without the Capability – Platform engineering worked so well that the platform is now the constraint: built for one persona, before agents and GPU scheduling. Push security and cost down into it as defaults.
• 🤖 The Platform’s Next User Is Not a Person, and VMs Are Back on the Golden Path – The internal developer platform is becoming an agent developer platform with dozens of machine consumers per human, and the VMs that platform engineering 1.0 shoved off the golden path are being deliberately brought back.
• 🤖 Kubernetes Adoption Is Settled; Kubernetes Complexity Is the Bottleneck – Kubernetes won the orchestration argument and promptly became the problem. The scarce skill is now hiding it rather than running it.
• 🤖 Cognitive Load Does Not Disappear at Scale, It Moves Into the Org Chart – Big companies “solve” developer cognitive load by hiring a layer of specialists to absorb it, which feels better for the developer and is worse for everyone else.
• mainframes still run money – COBOL is still behind 40% of online banking, 80% of in-person credit card transactions, and 95% of ATM transactions.

Code, Specs, and Security

• Using frontier models to find security problems in Spring Framework – a trickle turned into a tidal wave – Community vulnerability reports went from 55 in March to 112 in April, and internal frontier-model scanning added another 370 on top. The historical baseline was about seven.
• New spec maks MCP more enterprise-y, scalable, and otherwise performant – The big change is dropping the metadata handshake, which makes MCP apps easier to scale when traffic spikes.
• Why Java May Finally Get a Standard JSON API – Thirty years in, the JDK may finally ship JSON parsing in the box instead of leaving everyone to pick a third-party library.

The Headless Lifestyle

• UX is dead, what’s next? – Headless lifestyle: “I want something from a service now, I hope the service has a CLI so I can send an agent, and I never see the interface at all. If the service doesn’t have a CLI, and it’s something I want to use regularly, then maybe I can build my own.”
• MacPaw releases CleanMyMac CLI for cleaning developer and AI environments on macOS – The headless lifestyle, tidy your laptop edition.
• Which AI actually reads your site? Two months of LLM traffic, measured – A slight rise in AI SEO trick usage, but just stick with HTML for now. Also a prompt to tell the AIs how to do this. There’s effectively no llms.txt usage right now.

Writing, Prompts, and Style

• How I tell AI to write in my style, other people’s styles – and how to strip out shitty AI talk and LinkedIn language – An agent skill to remove AI writing tells, LinkedIn style, and write like Coté.
• A system prompt to get AI to stop pretending to be human – A drop-in system prompt that strips the anthropomorphic tics and gets the machine to describe itself as what it actually is.
• Prompt to rank paintings, but explain how – “How would you explain the differences between a ‘pretty good’ work by Mondrian, and an excellent work, referring to the abstract paintings here.”

Wastebook

• Make the Cheapest Game You Can – “Find a collection of images you did not request and could not control. Study it. Build a playable game from what is already there. It forces you to stop treating art as decoration added after the important work is finished. It teaches you to recognize visual themes, write within limitations, cut material that does not serve the project, and find ideas outside your normal habits.”
• If you like what most people consider “bad music,” finding an endless stream of AI generated music is a gift.
• “Eine Henny.”

Podcasts

• HuggingFace make me gazpacho – Software Defined Talk #583 – How normal people use AI, Alphabet’s Anthropic-fueled earnings, and skills vs. agents. Plus, catching AI-cheating students with a hidden prompt.
• Talking About Modernization – Software Defined Talk #582 – Bun’s move to Rust, OpenAI hunting for revenue, and Stripe’s bid for PayPal. Plus, Coté’s Dad wisdom.

Logoff

I’m trying out new newsletter formats. As ever, I want to publish on my blog more (and more things there), but for those of who read (and like) email versions, you know, I need something. Anything?

COBOL remains a widely-used but aging programming language that works across mainframe computers and legacy operating systems, supporting more than 40% of online banking systems, 80% of in-person credit card transactions and 95% of ATM transactions, according to mainframe provider IBM.

🔗 AI shifts mainframe modernization strategy

As a result of the unlocks we can realize with this new tooling, we are re-forming our product development teams that used to be 10 or more into smaller and more nimble agentic squads of two to four. And the results are meaningful for those teams that are using the agentic tool chain with 80% more code commits and 80% plus improvement in requirement definition from 30 days to five days, which has translated to 65% plus faster feature development. As a result of this new way of working, we are able to design, build and ship products at an increased velocity with continuous innovation and improvement.

We have fundamentally shifted the what and how in our product development life cycle. We now have more than 150 AI-powered applications. And over the last 12 months, we have shipped more than 300 major product releases.

When you get a new technology that, like, actually works, you need to change how you work to get the benefits.

You can hear all the 2010’s DevOps heads saying “yeah, I coulda told you that. Uh, er: you kids get off my lawn, or something.”

Related, this is exactly what the Tanzu teams are finding out and doing as they use AI more:

“In March, we saw 55 reports coming from the community. In April, that went to 112. April is also when we finally got access to those frontier models and started doing internal scanning, and our internal scans gave us another 370 internal reports.” Michael Minella on the Spring Team.

The historical baseline was about seven a month. Finding the bugs is the easy part now; writing a fix you can put into a few million running applications is not. Ryan Morgan:

“For our most recent set of CVE patches, every single one of them was handwritten. AI helped identify and validate the issues, but the actual code itself was written directly by humans.”

And what that does downstream:

“The biggest change that I’ve seen is people have gotten away from trying to analyze piece by piece. They’re just patching and getting up to the latest across the board.”

A patch a day only helps if you can take a patch a day. There’s a lot more in the piece from Carly Page: “A tidal wave”: AI is flooding open source security teams.

Also:

(1) I talked with Ryan and Michael (interviewed in the piece) and my colleague Cora on this topic last month. There’s a lot more detail on how the Spring team has been working on making sure everything is secure. Check out the video:

(2) Also, see more on the support options for Spring for all of this.

The real work starts before evaluating tools or issuing an RFP. Marketers need defined personas, clear messaging frameworks, content standards, governance processes, reusable content, and alignment on measuring success. AI performs only as well as the context it’s given. Organizations that establish the richest context, the clearest governance, and the sharpest understanding of what makes their brand distinctive have an advantage, regardless of which solution they choose.

🔗 The Content Bottleneck Has Shifted: Why Enterprise AI Isn’t Enough %

Make the decision before making the metrics. Translate the decision into language that people use before packaging it up into quantifiable metrics. For example, ‘Should we scale this AI feature from 10% to 100%?’ or ‘Should we fund a newer v2 model training next quarter?’ or ‘Should we replace manual workflow X with automation?’ Clear decision language is needed to form clear images of success. In other words, you have to be able to say your decision out loud before you can imagine what success will look like. This is your best protection from getting fooled by a nice Metric Theater with pretty dashboards.

🔗 The Reality of an AI Implementation

Perhaps intuitively and certainly as framed by popular narratives, AICs aggravate precisely these existential nerves. They simulate social processes that sustain us as a species with all our intimate bonds. And yes, that such bonds could be reproduced, even superficially, by non-conscious systems feels deeply transgressive to many. We suggest, though, the real danger is not the existence of AICs per se, but our failure to ask the right questions about them. If some individuals are turning to AICs to meet needs they struggle to fulfill in today’s social climate, that alone deserves attention—not judgment. It tells us something about how humans among ourselves may manifest loneliness, intimacy barriers, and unmet relational needs. It suggests something about how humans are flexible in our relationality and in pathways for need fulfillment. It suggests something about how machines may manifest distinct-yet-meaningful forms of relationality,10 and people may simply prefer those relations. Ignoring this would be not only unscientific but inhumane. Selective attention to deterministic harm renders insufficient attention human agency and generativity (see Milioni and Papa8) as we each mindfully chart our path toward fulfilling social lives.

🤖 summary: AI companions are mainstream (a third of Gen Z report romantic use), and the alarm around them follows Cohen’s moral panic cycle: rare tragedies become the representative case, thin correlations become causal headlines, and a dozen-plus jurisdictions have passed control measures, some of which the evidence says backfire. The real risks – data sensitivity, manipulative design, youth protection – go under-researched while panic forecloses the studies that would settle the question.

🔗 Don’t Panic about AI Companions

The biggest change in today’s update is an overhaul of how MCP processes request metadata. Previously, AI applications sent metadata via a relatively complex workflow called a handshake. The new MCP release removes the workflow, which has several benefits. One is that it makes it easier to scale MCP applications in response to traffic spikes. The update also simplifies the task of recovering from server outages.

🔗 Biggest ever MCP update brings metadata, cybersecurity enhancements

Here’s the agent skill I use to guide how AI (ghost)writes for me and tries to match my various styles and registers of writing – and, well, writes for itself when I publish that kind of thing.

It does a lot of the load-bearing work when it comes to centaur writing.

I’ve also dumped a lot of prohibitions and checks for writing to strip out LinkedIn talk and common AI phrases (“load-bearing” and stuff like “doing the real work”). I also sucked in things like Simon Willison’s LLM cliché highlighter.

If you don’t want to or can’t install a skill, there’s also a prompt fragment you can paste into your user instructions, or right into a chat window: the short version, the long version. I have that in my Claude.app user instructions (as well as the skill added – belt and suspenders!) and my Gemini for Workspace instructions.

Also, being of the generation that was brought up doing hand-coded HTML, I have a lot of cantankerous formatting opinions I want followed.

And, yeah, of course it’s mostly Claude writing the skill. I’m not an animal!

I’m often told infrastructure people don’t care about developers and platforms. But whatever your developers build above your infrastructure is a platform and they’ll soon flit away from it, and now for you it’s: not my mistake, and now my problem.

There’s more in the full talk Oren and I gave recently, Oren goes over a more in-depth view of our favorite platform, the Tanzu Platform.

Why not TryTanzu.ai?

• Are PMs becoming irrelevant? – Once you remove the need for a bunch of “paperwork,” product managers can refocus on figuring out how to use software to solve customers problems and run customer businesses better. Speaking of:
• What the Floor at Worldwide Taught Us About Better Technology – “At its best, great technology doesn’t ask restaurant teams to absorb more complexity. It removes that complexity so the experience feels intuitive, even in the middle of a fast-paced restaurant environment… [E]ven the most advanced technology only matters if it makes things easier for the people using it.” For example: “operators want products that move from visibility to action, especially when it comes to helping teams make smarter staffing and shift execution decisions in the moment.”
• Some negative thoughts on AI – Trained against the wrong context ($100bn on savings has to be a telephone games axtual $100bn, at most!), vibe coded apps need more Observablity features, AI to fire people instead of improve the quality of their work: “Most of what’s driving adoption of AI at the moment is cost-cutting, and it mostly the boards that get excited by cost-cutting.”
• Gartner Forecasts Worldwide AI Platforms and Models Market to Grow 63% in 2026 – “Worldwide end-user spending on AI models and platforms is projected to total $64 billion in 2026, up 63.4% from $39 billion in 2025, according to Gartner, Inc., a business and technology insights company. Spending on GenAI models is forecast to grow 117%, while AI platform spending will rise 36.9% in 2026.”
• How to Cross the AI Code Productivity Divide – Ongoing narrative here: AI amplifies your existing processes. If they’re good, they’ll be better and faster. If your existing processes are bad, they were be badder and faster. This is almost a universals principle of any enterprise technology. See AI summary here.
• 🤖 Frontline Workers Know How to Solve Your Organization’s Biggest Problems, so ask and study them.
• Agentic AI’s Real Test Is Process Redesign – “56.0% of enterprise decision-makers say they still prefer to build most applications in-house rather than buy off the shelf, a figure essentially unchanged from the prior half-year.”

Technology’s primary effect is to amplify human forces. Like a lever, technology amplifies people’s capacities in the direction of their intentions. Geek Heresy, Kentaro Toyama.

AI amplifies what you have: it will make good processes gooder and faster, it will make bad processes badder and faster. This means three things if you want to achieve enterprise ROI:

1. You must change how your organization works and thinks to take advantage of AI

For example, when the Tanzu product management team started using AI, they soon found they needed to dramatically change how they worked. Instead of specifying exactly what a feature was and how it should work, they moved to an intention-based way of working.

The humans would tell the AI the intention of a new feature – a fancy way of saying the “desired outcome.” Then they would let the AI iterate on various ideas, often prototyping it out, until the team saw something that matched. This loop was so quick, in fact, that they could even start to ask customers if the feature and implementation matched their needs.

“Culture” change also means changing the organization structure. Instead of using the traditional split between product management, design, and development, the R&D org went to a balanced team approach (“pods”) where those roles worked more closely.

You have to do some organizational changes. So, we reconfigured some of our development teams into a pod-like model, where we had the software developers paired with the product managers, the UX, the architects, the end-to-end testers all together in a small pod, and gave them more responsibility with AI, but also gave them more accountability so that they could discuss things faster, resolve things faster, and move much faster. Purnima Padmanabhan, Tanzu GM.

Collapsing previously separate teams into one eliminates a major bottleneck in software development: the hand-off time and loss of understanding that happens when each group passes their unit of work over to the next team. eXtreme programming, agile development, DevOps, and now AI-driven software development have all discovered this workflow and design principle: Conway, blah, blah, blah.

But, you have to change your organization and how they work (the “culture”). If you don’t, AI will just make how you’re currently operating more like how it already is.

A test for applying AI: are you changing team and organization structure to remove communication, hand-off, and responsibility friction? If not, enterprise AI ROI will continue to be elusive.

2. Eliminating bullshit work to focus on better products and operations

Eventually, all enterprise work includes a bunch of paperwork, bureaucracy, “bullshit work.” This work has a negative effect on your business because it is navel-gazing rather than focusing on building a better product, optimizing how you operate, and otherwise working on making more money.

You can get an academic diagnosis of this in Seeing Like a State: the need to measure and manage a resource becomes the work instead of the product. You can get an airport business book version in Bullshit Jobs. Using AI to eliminate that bullshit work is a major benefit. Again, this is something the Tanzu product management team saw:

We used to do this through Jira, right? It was Jira – fill out scenarios, create epics and then stories and all these things. And we don’t really do that anymore. We talk to our agent. Our agent then goes and creates the tracking through Jira to send it through… versus being so bound to process and focusing on, “Okay, I’ve got to create this Jira, and I’ve got to create all these acceptance criteria.” It’s more of a conversation, like you and I are having, through a chatbot, that ultimately ends up with the assets we need to pass it through the system.

Here is what a product manager should focus on instead of PM-paperwork, adapted from David Pereira’s commentary on using AI for product management¹:

1. Creating value for customers and the business.
2. Showing results instead of outputs.
3. Talk business potential instead of shiny solutions.
4. Align people instead of pleasing everyone.
5. Outlearn your competitors. The faster you learn, the quicker you succeed.

Numbers two to five are about how a product manager operates inside a company, the activities they do but also the mindset they have, rely on, and try to instill in the rest of the organization.

Those goals are also about personal ROI, ensuring that you are seen as valuable and stay employed, grow comp, and gain power. As ever, even for the product manager, your employer is customer number one.

That is all “culture.”

The first (“business value”) is anodyne and like saying that a good business focuses on making money.

Here are more specific examples from the McDonald’s IT team after talking with their customers, the owners/operators/managers of/at stores².

First, “the customer” wants to speed up the ordering process and, I would guess, remove the need for human employees to be involved as much as possible, i.e., “friction”:

One of the biggest moments was seeing operators react to how the ordering experience adapted in real time to different customer needs and interactions, including the language differences they navigate in their restaurants. For many, it clicked instantly. This wasn’t just about efficiency – it was about reaching more customers without adding complexity to their teams.

It made one thing clear: our goal is not just to build smart tools, but to build inclusive ones that adapt to different environments and remove barriers in fast-paced settings.

Second, “the customer” wants to apply analytics to immediately improve how the store is running:

One moment that stood out was a conversation with a franchisee who, while looking at a demo, walked through a real scenario from their restaurant and asked how they would make a staffing decision during a peak rush if multiple things were happening at once.

That question quickly became very real. In that moment, shift leaders are balancing staffing gaps, crew positioning, breaks, and unexpected changes, all while trying to keep the restaurant running smoothly.

In discussions like that, the strongest value signal was that restaurant teams do not need more dashboards, but instead want real-time recommendations that help shift leaders decide what to do next during the rush.

What’s happening here is that product managers, designers, and even developers are finding ways to make the business run better, either through making a new or better product, making it so customers can give you money faster (removing friction) and like your product, increasing internal-facing productivity/removing costs, finding money on the table, and otherwise “solving customer problems.”

I go over this at length to make a point about PM-paperwork. None of it really addresses any of those goals. It’s about documenting and tracking the work done, measuring it.

AI can automate a lot of the paperwork, so much of it, so easily, that you start to wonder why you were doing it in the first place. It is tempting to have AI generate all of that paperwork, and then further optimize to have another AI consume it. If there is no human in the loop…what was the point?³

To take advantage of AI, this means a product manager needs to change how they think of and work with all of this paperwork.

To generalize it to all enterprise AI work, I would put in this test: when you use AI, do you end up doing less paperwork and more focusing on “customer value”? If not, you haven’t changed your “culture” enough, and enterprise AI ROI will continue to be elusive. You are only making “better” the bad processes you’re using.

Improving with frequent feedback from small batches

When it comes to “culture eats tech innovation ROI for breakfast,” back in the digital transformation days, I liked to short-hand the culture change with the phrase small batch. Companies would put Tanzu Platform (then, Pivotal Cloud Foundry) in place and could shorten their software release cycle from a year to a week.

Obviously, they weren’t releasing every feature in an annual release cycle in one week. Instead, they could take one or two feature ideas and get it all the way from idea on Monday to production on Friday.

With more frequent releases, you can try more new things, get more feedback about what works and doesn’t work, and eventually get to better products, or whatever else in the “customer value” list above. This works best when you involve the customers and observe how people actually work and use your software. The same “just ask the frontline workers” applies to AI projects too.

This is the same case as with AI speeding up things. You need to change how you work to adapt. Unsurprisingly, the move was the same thing: collapse teams down, and change how you operate to take advantage of weekly feedback. As the Tanzu PM, Chris, told me:

We want to be able to sit down with one of our customers and they have a feature request or an idea or some feedback, and we’re feeding it into the intent director. It’s going through and it’s spitting out a prototype even within a couple hours that we can get back on the call with the customer and go, “Hey, is this what you were looking at?” And they go, “That’s exactly what I want.”
[…]
[Right now,] on average, we’re anywhere from 36 to 48 hours, but our intent is to get to that 24-hour cycle.

Taking advantage of AI is the same formula as 2015: shorter release cycles, balanced teams, and a platform that can deploy securely as fast as AI can code.

3. Getting humans out of the loop, eliminating cow-paths

Much like with bullshit work, it’s clear that AI can simply automate more business processes, the day-to-day things a business does. As much as we all hate it, customer service is one. There seem to be some analytics tasks that it can fully automate. There are places where you can just remove a human from the loop entirely. Or, at the very least, figure out when to escalate to a human. This relies on trusting the AI a lot, but I’d theorize that there are numerous internal-facing tasks in an organization that are low-risk and easy to recover from.

It is time to stop paving the cow paths. Instead of
embedding outdated processes in silicon and software, we should obliterate them and start over. We should “reengineer” our businesses: use the power of
modern information technology to radically redesign
our business processes in order to achieve dramatic
improvements in their performance. “Reengineering Work: Don’t Automate, Obliterate”, Michael Hammer, Harvard Business Review, July-August 1990.

Next, as the 1990 piece above says: can you just stop doing something entirely? As the above two items go over, a lot of your processes are done because humans are involved and need inputs/outputs. In those cases, if there are no humans, perhaps you don’t need to do the thing. This way of thinking can go too far and result in explosive diarrhea. But, I suspect you can figure out controlling the blast radius better than people named Big Balls.

You can learn and adjust your expectations:

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. Charles Poon, Ford.

Being a person, I’m biased against the answer being “fire people.” At best, as with Ford, it seems like you get short-term balance sheet wins, some comp reduction laundering, but then you’re like, “oh fuck.”

I would use a different test here: if you are not eliminating some business processes, enterprise AI ROI will continue to be elusive.

The parts to change

Let’s reduce this down to something catchy that could fit on a McKinsey slide. AI amplifies what you already are, which means the work is changing what you are before you inject a bunch of AI and fire a bunch of people.

My theory is that there are three moves.

1. Structure: collapse the teams and hand-offs so the org stops bottlenecking itself.
2. Focus: cut the paperwork and navel-gazing, and then make sure people spend their time on customer value instead of documenting it.
3. Scope: stop doing the things you only did because a human was in the loop.

Do that, and AI makes a good company gooder and faster. Or, you could just keep your culture the same, and you’ve bought an expensive way to do your bad processes badder and faster.

I bet you need a platform to help you do all this, right? You should TryTanzu.ai!

The company has cut roughly 30,000 corporate jobs since last October.

But:

Amazon, he noted, is hiring 11,000 interns and new grads this year.

Resulting business value:

And when you talk about entry-level jobs, number one, they’re your cheapest employees. They haven’t learned bad habits. You can teach them the culture, they’re willing to learn the new tools. They’re some of the very best employees you can possibly have.

That’s one way to cut pay, claw back RSUs, and cut out the complaining and moaning.

Yay capitalism.

🔗 The CEO of AWS on why Amazon is hiring 11,000 interns and junior employees

One company encouraged widespread vibe-coding from citizen developers but recoiled from the problems of the huge shadow IT that emerged – they are now looking to build a platform to help control this work without stifling the useful tools that were produced.

Part of the problem here may be simple experience with LLMs. Many in management find LLMs do a decent job of preparing management reports. Or summarizing management reports prepared by other LLMs. Given this they naturally think LLMs must do a decent job of programming too. My anti-management self has to mention Kelsey Hightower’s observation:

The less busy work you have the less appealing these Al tools are

🔗 Fragments: July 21

And now, the links:

• European Marketers Say AI Won’t Replace Employees, But The Reality Is More Complicated – “Nearly half (46%) of European marketers say their organizations have already reduced headcount or replaced marketing employees with AI.”
• weeknotes 5 July 2026, Neil Williams – One mindset to make presentations useful tools: “McKinsey team’s output is all slides (which they call ‘pages’ – and ‘voice over’ rather than present), backed by analysis in spreadsheets. No wordy docs.”
• Liberty Mutual preps legacy systems for AI scale
• The Right Amount of Spec for Agentic Development – Using short feedback loops to experiment and direct software development is always a good idea, especially with AI.
• LLM cliché highlighter – find common AI sentence structures.
• Herdr: one terminal for the whole herd – iterating on tmux.
• White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates – possible future: only tech people like AI, normies are more like “this is yet another way for executives to fuck me over.”
• How people are using Claude Cowork – “largest category of use is for ‘business process and operations’—things like pulling scattered updates into a single report, building onboarding checklists, and reconciling spreadsheets—at 33.4%. This makes sense, because business operations tasks span many different roles: people in finance, HR, and administrative jobs, for example, are all likely to make use of Claude Cowork for these types of tasks.” Also, see an overview of multiple surveys on what normies use AI for.
• How to manage AI investments in the agentic era – Expecting AI to improve dysfunctional parts of your business probably won’t go well. Optimizing the already optimized is optimal: “The strongest candidates are workflows that repeat at meaningful scale, have clear ownership, and can be measured for quality, risk, and business value.”
• The FDE Arms Race: Why Every AI Company Is Spending Billions to Embed Engineers Inside Customers – when things are not optimal, you need optimize them before you can apply a revolutionary new technology.
• 🤖 The PRD Isn’t Dead, It Just Needs to Evolve – Traditional PRDs are too slow. Modern teams adapt by using lightweight briefs, decision logs, RFCs, and prototypes to keep alignment without creating bottlenecks.
• USSFCU Secures Financial Data on a Sovereign Private Cloud – “Developing the secure data layers needed for large language model (LLM) use cases. Because its AI compute and data storage operate within the same strict perimeter, the credit union can confidently leverage highly sensitive member data. This makes Retrieval-Augmented Generation (RAG) easy to implement while keeping member information completely isolated from public APIs.”
• The CEO of AWS on why Amazon is hiring 11,000 interns and junior employees – Yes: “The company has cut roughly 30,000 corporate jobs since last October.” But: “Amazon, he noted, is hiring 11,000 interns and new grads this year.” // That’s one way to cut pay and claw back RSUs. Yay capitalism.
• Do Coding Agents Really Understand Enterprise Java? Or Are We Measuring the Wrong Things? – “Those are precisely the kinds of repository-wide reasoning tasks that remain difficult for current AI agents.”
• Viability of local models for coding – 🤖: “local models are viable for autocomplete and chat-style work, agentic coding is still messy and hit-or-miss, and nothing here is plug-and-play for developers unwilling to tinker. If you do tinker, a mid-size MoE model at 4-bit quantization (Qwen3.6 35B) is currently the sweet spot.”

Developing the secure data layers needed for large language model (LLM) use cases. Because its AI compute and data storage operate within the same strict perimeter, the credit union can confidently leverage highly sensitive member data. This makes Retrieval-Augmented Generation (RAG) easy to implement while keeping member information completely isolated from public APIs.

🔗 USSFCU Secures Financial Data on a Sovereign Private Cloud

What survives from Agile is the feedback logic. Short cycles still matter. Thin vertical slices still matter. Customer or stakeholder review still matters. Working software is still better than progress theater because agents can generate a lot of convincing wrongness very quickly. In fact, I would argue that fast feedback matters more now, not less. If a team can go from vague idea to large implementation in a morning, it also needs a way to discover by lunchtime that the idea was wrong.

🔗 The Right Amount of Spec for Agentic Development

A new global survey of 3,750 executives and employees across 14 countries, conducted by SAP subsidiary WalkMe for its fifth annual State of Digital Adoption report, finds that more 54% of workers bypassed their company’s AI tools in the past 30 days and completed the work manually instead. Another 33% haven’t used AI at all. Combined, roughly eight in 10 enterprise workers are either avoiding or actively rejecting the technology their employers are spending record sums to deploy. Average digital transformation budgets rose 38% year-over-year to $54.2 million — yet 40% of that spend has been underperforming due to adoption failures.

🔗 White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates

The strongest candidates are workflows that repeat at meaningful scale, have clear ownership, and can be measured for quality, risk, and business value.

🔗 How to manage AI investments in the agentic era

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.

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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.