Coté

Enterprise Harness Smack-Talk, Forms Don't Love You Back, and Doing Nothing on Purpose - Related to your interests, Friday

Fri, 12 Jun 2026 07:39:46 +0200

Also: Anthropic’s 80% code claim, and Claude’s quiet enterprise share.

From: Broadcom's Investment in Spring to Combat AI-Fueled Security Challenges in the Enterprise

As the chart shows, there’s been a huge jump in CVEs for Spring - this is what’s happening everywhere, you know.

My work, Tanzu, has been focusing on this and has changed how they handle these rollouts. Now customers can get early access to the secured builds for Spring so they can deploy them as quickly as possible to fix these security problems. You also get clean-room builds of Spring and the dependencies, which is a big change, for the better:

Furthermore, Broadcom’s Spring engineering team has significantly scaled its investment in advanced AI-assisted security analysis, including frontier model–based scanning and validation workflows to proactively identify vulnerabilities, assess remediation paths, and validate fixes across the Java dependency tree for Spring. Broadcom announced additional R&D investments to extend its proven clean-room build architecture, foundational to Bitnami, to build the Java dependencies for the entire Spring ecosystem. With this expanded investment in securing the Spring ecosystem and its dependencies, Tanzu Spring customers will have access to:

Secured, SLSA Level 3–validated software supply chain for Java dependencies.

Coverage that spans the full transitive dependency graph managed by the Spring Boot bill of materials.

Thousands of secured dependencies, built and tested across every supported Spring version. Spring Boot 4.0 alone manages 1,768 of them; across the full supported portfolio, that totals more than 100,000 validated dependency builds.

Additionally, as members of the open source community for over two decades, the Spring team has broad relationships across adjacent open source technologies and will continue to collaborate and contribute to these upstream community projects.

And, of course, we also offer tools to roll out these changes in Tanzu Spring Advisor. I’ve seen this in action recently when I’ve been messing around with Spring Boot MCP servers, you log in to the dashboard to check it out and it tells you some component deep down in the stack is out of date. Pretty great - something even a dumb developer like me could do something with :)

Check out the Tanzu Spring plan we have, and for a platform-level approach beyond Spring and apps, included with an enterprise AI server for private and public hosted AI hoopla better TryTanzu.ai.

Everyone hates frontier AI labs, says Palantir boss - One enterprise harness maker says the competing enterprises harness makers either suck or are non-existent. [BTW, we should start calling it “enterprise harness."]
The Minimill of AI - Private AI prediction: “Tens of millions of these will proliferate inside companies in the next few years, each one quietly absorbing much of the work that today shows up on a hyperscaler invoice.”
🤖 The iPhone’s Last Stand - Siri can’t do agents, but agents serve productivity and consumers want to waste time on short-form video, so the iPhone’s personal-context moat beats agentic horsepower - and Apple skips the capex everyone else is burning.
Claude is ready for its corporate close-up - “Enterprises, IDC says, remain largely unsold on Anthropic’s Claude models, with only 19 percent using them extensively and 25 percent actively evaluating them.” OpenAI and Google are better represented in enterprises, with about 42 percent and 38 percent of organizations”
Your company needs agency, not agents. - When management is the bottleneck preventing enterprise AI ROI. Hot take: everything is a bottleneck.
Why ABN AMRO’s CEO wishes the bank was going further and faster with the AI rollout set to take out a quarter of its headcount - Commentary on rolling out AI at a bank.
AI Scribes in the Clinic: What Patients Should Know - AI for doctors taking notes, a review of what’s known now.
🤖 Forms don’t love you back - AI is about to make us fill out far more forms, but many won’t look like forms - they’ll arrive as chatbots, agents, biometric checks, and one-click services that quietly extract structured data. Old answers persist as “single sources of truth” that follow you around, and the rigid schemas underneath can’t do discretion: you fit the box or you get “computer says no.” // Also, much gig-economy class system discussion.
Rapid software delivery is possible inside DoW - Software Factory 2.0 shows how - “Replace multi-year forecasts with real-time discovery of operational friction. Instead of a five-year requirement for a ‘targeting system,’ identify the bottleneck–like a three-hour targeting approval process. Set a goal–like reducing the approval process to 30 minutes. And empower a team to solve it. In this Kessel Run example, the requirement was an outcome, not a feature list.” Bryon Kroger
Institutional challenges in agile adoption: Evidence from a public sector IT project - 2016: “[T]he US has a similar record with 94% of federal government IT projects exceeding their budgets and schedules, and 40% failing to complete”
Forrester: Capping AI Spend Won’t Fix Your Token Bill
Doing nothing at work - Avoiding low value work to be ready for high value work. Also, an example of “homework”: work people get you to do that is not your job and often goes nowhere.
What I think about when I edit - All good advice…and something you could ask the robot to do without it turning your text into copy-slop (you’d have to keep an eye on the adverb stuff. Speaking off: editors hate adverbs!)

AI Summaries

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

ICYMI

Observability’s Next Phase - Software Defined Talk #576 - “Brandon talks with OpenObserve’s Prabhat Sharma and Shani Shoham: why observability is still broken, how they fixed it, and where AI takes it next.”
Deming, DevOps History, AI Risk, and Critical Thinking, with John Willis - Software Defined Interviews #123 - W. Edwards Deming’s quality theories and their influence on the Toyota Production System and DevOps practices, including “Deming’s system of profound knowledge (theory of knowledge, variation, psychology, and systems thinking),” industry misconceptions about AI, and how probabilistic AI systems require different risk frameworks than traditional deterministic approaches.

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I am thinking about changing the role and format of this newsletter and interested in what you, dear reader, want this newsletter to be. What do you use it for, do you like reading it, what would you like to change? Should it just be a list of links to skim, a round-up of things I’ve posted and want to share - that’s what it’s become. Originally, I made this newsletter because (1) blogs were dead, so I stopped really blogging, and, (2) it seemed wise to build up a “community” that would last, that is an email list. This meant the newsletter was actually frequentish blog posts at the top and then links at the bottom - a blog in one page. I haven’t been doing that - do you wish I still did? Reply back if you’re up for taking the time, I’d appreciate it.

P.S.: it’s exhausting nearing boring that the only conversations out there in tech land are about AI. What’s going on with the entire rest of the stack? Yes, and is that the story: AI touches everything, everything is AI. At least, people are hoping so.

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.

Claude market penetration in enterprises still low

Fri, 12 Jun 2026 07:26:40 +0200

Enterprises, IDC says, remain largely unsold on Anthropic’s Claude models, with only 19 percent using them extensively and 25 percent actively evaluating them. OpenAI and Google are better represented in enterprises, with about 42 percent and 38 percent of organizations"

This means there’s lots of “headspace” to grow revenue, or disappointing and incomprehensible ROI, i.e., enterprises are finding it hard to figure out what to do with AI. Or both!

🔗 Claude is ready for its corporate close-up

When management is the bottleneck preventing enterprise AI ROI.

Fri, 12 Jun 2026 07:18:06 +0200

Right now, many companies already have the technology they need to go much faster. The blocker is company systems that are mostly designed to prevent things from happening. The power is centralized and all the team members are treated like a risk vector. Exhausting approval cycles, super tight boundaries on roles, unbreakable title-based hierarchies, and a whole tier of middle managers whose main job is to keep everyone in line.

🔗 Your company needs agency, not agents.

Enterprise harness smack-talk

Fri, 12 Jun 2026 07:14:12 +0200

One enterprise harness maker says the competing enterprises harness makers either suck or are non-existent.

When you go to San Francisco and talk to them, their basic vibe is ‘we don’t have to solve your problem today because tomorrow you’re going to go away and all your problems are going to be solved,'" Karp charged. “It’s largely religious.” … “the product doesn’t actually work and it’s very expensive.” To that end, he added, most of the things that Anthropic brags about in public, for example, are successful because they’re “running on Palantir,” Karp charged

[BTW, we should start calling it “enterprise harness."]

🔗 Everyone hates frontier AI labs, says Palantir boss

What people have to say about AI in YouTube and TikTok

Fri, 12 Jun 2026 06:47:48 +0200

A census of 25,000 YouTube and TikTok videos finds pro-AI content outnumbers anti-AI 3:1, dominated by memes and productivity hacks rather than abundance or doom. Resisters care most about creative theft, not the x-risk or data center concerns driving elite debate.

From Memes > Doom: How TikTokers and YouTubers See AI - Free Systems (Substack), June 2026.

Adopters (pro-AI content)

Content type	%
AI memes & effects	43%
Career / productivity	25%
Creative tools	15%
Education and learning	8%
AI companionship	4%
Breakthrough science	1%

Resisters (anti-AI content)

Content type	%
Creative theft	22%
Deepfakes and misinfo	19%
Jobs displacement	13%
I hate AI (general)	13%
X-risk	8%
Energy / data centers	6%

Note: percentages don’t sum to 100 in either column - the piece doesn’t account for the remainder, presumably smaller uncategorized buckets.

As always, the style of YouTube videos kills me. It’s always a giant head looking like they just discovered that they are a turd.

Check out the full readout: Memes > Doom: How TikTokers and YouTubers See AI

🤖 Forrester: Capping AI Spend Won’t Fix Your Token Bill - Your Real Problem Is Context Debt, and It Needs a New Discipline Called ContextOps

Thu, 11 Jun 2026 10:59:12 +0200

Original: Your AI Bill Is A Context Problem by Forrester. Summarized by AI on Jun 11, 2026.

The AI bill shock hitting enterprises isn’t a pricing problem you can cap your way out of - it’s the metered cost of your own knowledge not being machine-readable, forcing agents to rebuild missing business context on every loop.

Capping spend treats a value problem as a price problem and kills the experimentation the spend was meant to fund.

The instinct - tighter caps, sharper rates - echoes the cloud bill shock of a decade ago. This time the cure is wrong, because capping the bill mistakes a problem of value for a problem of price. “Tokenmaxxing” is what you get when you reward adoption (Uber and Meta literally ran usage leaderboards) without governing value, and the actual gap is that all the spend hasn’t yet connected to anything customers can feel.

The real fix is a permanent operating discipline - ContextOps - that keeps the agent’s model of the business accurate as the business moves, since that fidelity, not the rate card, is what determines whether each token buys an outcome or buys confusion.

Also, the problem with FDEs.

Links

🤖 Your AI Bill Is A Context Problem - Enterprise AI bill shock is context debt, not a pricing problem: agents loop and rebuild missing business meaning on every call, so capping spend kills value-creation. The fix is ContextOps, a continuous discipline (likely a managed service) that keeps the agent’s ontology faithful to a business that won’t hold still.

Making long-term projects more agile, less waterfall

Mon, 08 Jun 2026 14:49:29 +0200

Replace multi-year forecasts with real-time discovery of operational friction. Instead of a five-year requirement for a “targeting system,” identify the bottleneck–like a three-hour targeting approval process. Set a goal–like reducing the approval process to 30 minutes. And empower a team to solve it. In this Kessel Run example, the requirement was an outcome, not a feature list." Bryon Kroger

🔗 Rapid software delivery is possible inside DoW — Software Factory 2.0 shows how

Shitting in the field, but plated in the dining room

Sat, 06 Jun 2026 10:51:06 +0200

[The Normans] gave English its double vision, which is the source of both the beauty and the mess. After 1066 you’ve got the Saxon peasantry keeping the Germanic words for the muddy daily grind and the Norman lords laying French over the top for everything refined, so English ends up with two words for everything and a built-in class system in the vocabulary. The peasant tends the cow, pig, and sheep (Old English); the lord eats beef, pork, and mutton (boeuf, porc, mouton). The animal is Saxon while it’s alive and shitting in the field, French once it’s plated.

That’s where the prettiness comes from - the absurd synonym wealth. You can ask (Saxon), question (French), or interrogate (Latin), and each one carries a different temperature and register. Most languages would kill for that kind of tonal palette; it’s why English is so good for poetry and bullshit alike. You can slide up and down the formality ladder rung by rung, which is half of what rhetoric even is.

Claude on the two levels of English.

Fri, 05 Jun 2026 12:02:21 +0200

From ChatGPT based on "Pyramid of Capitalist System," Nedeljkovich, Brashich, & Kuharich, Industrial Workers of the World, 1911.

Of course, for America at least, what you'd like to visualize is that every layer actually plays a part on "shareholder," just a small part. Maybe there's some stock certificates and cash that trickle down in pneumatic tubes?

Flood of security patches: Spring Framework ed.

Fri, 05 Jun 2026 11:39:00 +0200

Community security reports for Spring, by month.

In April, utilizing new scanning capabilities, we received an unprecedented 482 new security reports across 65 scanned projects. Of those 482 new reports, 370 came from our internal scanning capabilities and 112 came from the community. This means that even without the new scanning, we would still have seen a doubling of community reports compared to our already high number in March. While we clearly had an extreme spike in April’s reports, we do not expect reports to go back down to historic levels for a few months as the influx of AI-based reports continues (May had 72 community reports for example).

🔗 Spring and Security In The Times Of AI

Stochastic Smart Talk, the DIY Platform Trap, and Strategic AI Not Spending - Related to your interests, Friday

Fri, 05 Jun 2026 09:11:29 +0200

Also: hardened images everywhere, quarterly Java patch tours, Wells Fargo’s complicated employment math, and a Highgate gravestone.

Silo busters - a unified platform needs a unified team - “This matters because it removes the structural excuse for fragmentation. When a single platform surfaces all the controls a unified team needs, there is no longer a technical reason to keep five separate teams in five separate rooms. The organisational argument for siloes collapses alongside the technical one.” // Using a platform to combat Conway’s Law and organizational friction caused by too many groups/silos.
Spring and Security In The Times Of AI - “In April, utilizing new scanning capabilities, we received an unprecedented 482 new security reports across 65 scanned projects. Of those 482 new reports, 370 came from our internal scanning capabilities and 112 came from the community. This means that even without the new scanning, we would still have seen a doubling of community reports compared to our already high number in March. While we clearly had an extreme spike in April’s reports, we do not expect reports to go back down to historic levels for a few months as the influx of AI-based reports continues (May had 72 community reports for example).”
Wells Fargo CEO: AI’s effect on employment is ‘complicated’ - ‘The CEO named auditing, testing, legal, contracts, patent filings, pitchbooks in investment banking and credit memos as a handful of areas across the company executives see room for AI to improve processes. “How much of that actually results in pure margin or return expansion is to be seen.” Scharf said, since competitors will be chasing similar AI goals, but it is “a net positive” for the company’s future expense base."’
Gartner Says CFOs Must Stop Mistaking Finance AI Deployment for Value Creation - “The clearest outcome so far has been efficiency. Among finance organizations that have adopted AI, 66% reported greater efficiency and productivity as a top benefit. Steecker urged CFOs to now look beyond productivity-led AI use cases and focus more directly on value creation. Finance’s lower grades are concentrated in implementation speed and analytics impact. Gartner found that 63% of finance organizations said AI implementation was slower than expected in 2025. Analytics-related use cases also remain difficult to convert into high impact, with financial forecasting and insight generation among the lowest-rated use cases.”
Anthropic’s Proposed IPO Will Change The Economics Of Enterprise AI - Eventually, you have to pay full price or the seller is insolvent.
How People Are Really Using AI in 2026
Retailers turn to AI for productivity, personalized shopping
Uber Caps Usage of AI Tools Like Claude Code to Manage Costs - “That means each employee’s AI spending cap is ~11% of that median compensation package.”
the solution might be cancelling my AI subscription - Getting things done can be addictive. The joy you get from finally being empowered to do things you previously could not is a feedback loop that must be controlled for some of us.
Why Hardened Images are Suddenly Everywhere - Why aren’t all images super-secure, or hardned?
6 Enterprise MCP Adoption Best Practices - Things your platform should for AI.
The DIY platform trap that’s burning out engineering teams
Java Maintenance Engineering Shifts Focus on Quarterly Critical Patch Stabilization - From what I can tell, every core part of the software stack is stopping what they’re doing and taking care of the flood of new, AI-driven security issues.
Claude Mythos exposed a hard truth: Your enterprise patching process is way too slow - Prescriptive on the current “now more than ever” security freak out.
Remote work - not AI - is killing job prospects for the youth - > According to the Fed’s analysis, youth unemployment has risen significantly since the coronavirus pandemic, and hasn’t receded in the same way that unemployment numbers for older, more experienced college graduates has in recent years. The analysis notes that the prevalence of remote work has increased since COVID-19, and it believes those two trends have more than just a correlation. // “Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees,” the Fed said of its data. “Accordingly, companies may be reluctant to hire less-experienced workers in distributed work arrangements.”
taste - It is good to develop taste - opinions about what you like and don’t like. And then, it gets weird: an example of taste.
update on my use of Claude - " grew up spending a good deal of time with an older cousin of mine in Cullman, Alabama named Claude Basenburg. A hefty, hearty good ol' boy in overalls, with a wad of tobacco in his cheek. So when I visit claude.ai I don’t think of an omniscient counselor, I just envision my cousin from Cullman. It helps…. But in the end the results are very clean and, to me, _extremely_satisfying."

AI Summaries

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

Wastebook

‘Stop thinking you’re better than me just because you know the word “stochastic."’ Here
If an algorithm makes people money, people will game it.
“According to his grave stone George Ross 1935-2011, was a Philosopher, Teacher, Physicist, Romanian and Nudist.” Here
“If it takes less than 2 minutes, do it before your brain schedules it for next year.” Here
You know I’m serious about something when I start a new Claude project. And dead-serious when I start generating podcasts for it.

ICYMI

Update on my use of Claude
Josh Long and I at J-Spring 2026
Getting things done can be addictive
How People Are Really Using AI in 2026: Thinkslop, Therapy, and Shadow Work
Three reasons why a “batteries included” platform is urgently needed right now
Enterprise self-harm: cleaning the data is the hard part
UI blizzard - Software Defined Talk #575 - “NVIDIA going consumer, Microsoft Build, and the Anthropic/OpenAI IPO race.”
Nobody Wants to Be a Measurer - Software Defined Talk #574 - “The Cloudflare CEO’s op-ed, upcoming tech IPOs and GitHub getting breached.”

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I still haven’t moved to buttondown.email, but I sure am thinking about it.

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.

Fri, 05 Jun 2026 07:31:17 +0200

Getting things done can be addictive. The joy you get from finally being empowered to do things you previously could not is a feedback loop that must be controlled for some of us.

🔗 the solution might be cancelling my AI subscription

Fri, 05 Jun 2026 07:26:58 +0200

Josh Long and I at J-Spring 2026.

Fri, 05 Jun 2026 07:21:36 +0200

Fri, 05 Jun 2026 06:57:04 +0200

I grew up spending a good deal of time with an older cousin of mine in Cullman, Alabama named Claude Basenburg. A hefty, hearty good ol’ boy in overalls, with a wad of tobacco in his cheek. So when I visit claude.ai I don’t think of an omniscient counselor, I just envision my cousin from Cullman. It helps…. But in the end the results are very clean and, to me, _extremely_satisfying.

🔗 update on my use of Claude

Thu, 04 Jun 2026 06:42:36 +0200

That means each employee’s AI spending cap is ~11% of that median compensation package.

Last year, there were a few anecdotes about high growth tech companies spending $100,000/year per head on tokens. That seems like it’s coming to end.a

🔗 Uber Caps Usage of AI Tools Like Claude Code to Manage Costs

remote work reduces on-the-job training

Wed, 03 Jun 2026 07:41:02 +0200

According to the Fed’s analysis, youth unemployment has risen significantly since the coronavirus pandemic, and hasn’t receded in the same way that unemployment numbers for older, more experienced college graduates has in recent years. The analysis notes that the prevalence of remote work has increased since COVID-19, and it believes those two trends have more than just a correlation.

“Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees,” the Fed said of its data. “Accordingly, companies may be reluctant to hire less-experienced workers in distributed work arrangements.”

🔗 Remote work – not AI – is killing job prospects for the youth

Defeating Conway's Law

Wed, 03 Jun 2026 07:23:49 +0200

Try using a platform to combat Conway’s Law and organizational friction caused by too many groups/silos.

This matters because it removes the structural excuse for fragmentation. When a single platform surfaces all the controls a unified team needs, there is no longer a technical reason to keep five separate teams in five separate rooms. The organisational argument for siloes collapses alongside the technical one.

Conway’s Law says that a system will be shaped - organization sub-divided - as a replica of the orgnonzatikn that built the system.

(Yes, I know this is a misreading of the “communication structures” part of Conway’s but this is the use the street has found.)

Most people will tell you that people eat technology for breakfast. You can’t change how people work or how a (large) company is structured by simply installing some new system.

As Harry Thambi outlines, I’m not sure that’s always the case. When a new technology removes the need for specialist teams because it automates and removes the toil of the people running the technology, you have a chance to collapse the groups, to have less silos, and, thus, defeat Conway’s Law.

That requires a truly integrated platform. Interested in one, why not TryTanzu.ai?

🔗 Broadcom Knight’s partner blog: Silo busters - a unified platform needs a unified team - Broadcom News and Stories

you can't measure productivity

Wed, 03 Jun 2026 07:18:52 +0200

The [Wells Fargo] CEO named auditing, testing, legal, contracts, patent filings, pitchbooks in investment banking and credit memos as a handful of areas across the company executives see room for AI to improve processes. “How much of that actually results in pure margin or return expansion is to be seen.” Scharf said, since competitors will be chasing similar AI goals, but it is “a net positive” for the company’s future expense base.”

I’m down with the Martin Fowler idea that measuring productivity is sort of a waste. Rather, it’s better to measure output, yes, but:

Productivity, of course, is something you determine by looking at the input of an activity and its output. So to measure software productivity you have to measure the output of software development - the reason we can’t measure productivity is because we can’t measure output.

Is the bank making more money without breaking laws? Then it’s probably fine.

🔗 Wells Fargo CEO: AI’s effect on employment is ‘complicated’

security over features

Wed, 03 Jun 2026 07:11:08 +0200

From what I can tell, every core part of the software stack is stopping what they’re doing and taking care of the flood of new, AI-driven security issues.

🔗 Java Maintenance Engineering Shifts Focus on Quarterly Critical Patch Stabilization

🤖 “descended into madness" - Backrooms

Wed, 03 Jun 2026 07:07:30 +0200

Original: A Backstory from My Backrooms by Paige K. Bradley. Summarized by AI on June 3, 2026.

{I love backrooms. One of the first things I did with AI image generator was make endless empty malls and backrooms. So good. -Coté}

A stray 2019 4chan post about a bland, fluorescent-lit interior sparked the viral myth of the backrooms, a concept of endless, liminal spaces that feel familiar yet threatening. Its resonance lies in the idea of “no clipping” from reality—slipping into a hollow, game-like purgatory where meaning and orientation fail. The condition of being lost, as one user put it, is to have “descended into madness.”

Artist Jan Vorisek’s installations, with their yellow PVC curtains, dislocated objects, and fragmentary videos, evoke the same uncanny emptiness. His works inhabit a zone between physical matter and simulation, where sculptures resemble 3D game props and rooms become stages for absent narratives. The viewer is left to confront whether the space—or their own perception—is real.

The essay links this aesthetic to premium mediocrity, a cultural phase of glossy surfaces and hollow interiors, where urban spaces like shuttered storefronts become physical backrooms. Liminality is both a visual experience and a social condition, reflecting a civilization suspended between exhausted industrial modernity and early-stage digital post-scarcity.

Through art and literature, the theme recurs: Dennis Cooper’s novel God Jr. literalizes the descent into a hollow 3D monument and a game world as a failed attempt at grief and control. The backrooms and Vorisek’s “incomplete interiors” mirror that impulse—structures built for meaning that only expose their own emptiness.

The backrooms phenomenon has since drifted toward a Gothic sensibility, as cultural imagination projects fragmented narratives, invisible antagonists, and dread into blank architecture. Its power lies in what is missing, allowing fear, nostalgia, and hallucination to fill the void.

In the end, the backrooms are a metaphor for contemporary life under the weight of digital simulation, urban vacancy, and mediated experience. They remind us that when the illusion shatters, what’s left is an ambient awareness of loss—and the quiet admission: you lost.

Links

🤖 A Backstory from My Backrooms – How a single 4chan post about a yellowed, empty room evolved into a cultural metaphor for liminal dread, digital hyperreality, and the art of dislocation.

Summarized by ChatGPT on Jun 3, 2026 at 7:07 AM.

🤖 Valiantys-Glean Partnership Bets That Cross-Platform Knowledge Graphs and Behavioral KPIs Are What Move Enterprise AI Past Pilots

Wed, 03 Jun 2026 07:05:11 +0200

Original: Enterprise AI is still stuck at experimentation – Valiantys and Glean think they know why by diginomica. Summarized by AI on June 3, 2026.

Most enterprise AI pilots stall, and the diagnosis from Nathan Chantrenne, Chief AI Officer at Valiantys, is that the field measures the wrong things and fragments its tooling.

The dominant success metric - “employees save four hours a week” - tells you nothing, because nobody knows what those hours become; they might just mean more coffee. The partnership being pitched is Valiantys, an Atlassian-centric consultancy, joining with Glean, an enterprise AI platform valued at $7.2 billion on roughly $300 million ARR as of May 2026.

Maturity varies widely, and the brakes are predictable. In Europe, governance and security are the primary obstacle; in North America, less so.

Tech-savvy firms have the opposite problem: they over-experiment.

Companies test 15 different technologies and let every team pick its own, which collapses at scale because there’s no unified strategy for compliance, security, or cost. Each platform ships its own AI tooling and data layer, and none reaches well into another vendor’s stack - so technical silos mirror organizational ones, and the cross-functional teams meant to bridge them get treated as a burden rather than an authority.

Glean’s role is the connective tissue. Its permissions-aware Knowledge Graph¹ is a semantic layer that links data across applications - so the system knows a Salesforce opportunity ties to a ServiceNow case and a Jira ticket.

Valiantys, which began moving beyond pure Atlassian work about 18 months ago (anchored by the July 2024 Contegix acquisition for North American expansion), is taking this to market first in its native strengths: software development lifecycle modernization and enterprise service management, with an eventual ~50 use cases in view.

The KPIs Chantrenne cares about are behavioral, not cosmetic. For development, the headline is idea-to-production time, dropping from weeks or months to two weeks or, in extreme cases, two days.

For service management, the entry metric is ticket deflection² - 50 to 70% of a typical 1,000-monthly-ticket desk is repeatable level-one work that can be automated.

The harder, more revealing layer is agent productivity on level-two tickets and how many knowledge base articles agents contribute - both signals of whether the work itself is actually changing.

The recurring lesson is that change management, not technology, is the binding constraint. “You can have the best possible technical solution out there. If you’re unable to bring the people with you… people will do everything that they can for the project to fail."

The prescription for firms that have spent heavily without returns: define real business KPIs, centralize governance, bring people along, and narrow from 15 technologies to two or three - then go nearly all-in and run with it.

Links

🤖 Enterprise AI is still stuck at experimentation – Valiantys and Glean think they know why - Enterprise AI stalls because firms track meaningless productivity metrics and scatter across too many tools; the Valiantys-Glean bet is that a cross-platform knowledge graph plus behavioral KPIs and centralized governance is what moves pilots into production. Change management, not tech, is the real bottleneck.

Knowledge Graph - a data structure representing entities (people, documents, tickets, accounts) and the relationships between them, allowing software to traverse connections rather than treat each record in isolation. “Permissions-aware” means it respects each user’s existing access rights when surfacing linked data. ↩︎
Ticket deflection - resolving a support request automatically (via self-service, bots, or automation) so it never reaches a human agent. ↩︎

🤖 AI Collapses Build Costs but Expands Alignment Burdens for Senior Engineers

Wed, 03 Jun 2026 06:18:35 +0200

Original: Is this sustainable? by Jamie Hurst. Summarized by AI on June 3, 2026.

AI has collapsed the distance between idea and implementation. Senior engineers can now move from concept to working proof-of-concept in days, bypassing the old cycle of proposals, approvals, and sequential team work. This shift has replaced slide decks with demos, rewarding concrete experimentation over theoretical cases.

Organizational alignment has become the new bottleneck. Multiple teams can quickly produce overlapping solutions, making coordination and convergence harder even as technical velocity rises. Solving more problems faster creates fragmentation, and aligning across the org consumes more time than before.

AI has made senior roles both broader and less sustainable. Senior engineers now combine hands-on coding with high-level strategic writing, juggling multiple initiatives at once. Mentoring and unstructured thinking time have been squeezed out because they cannot be accelerated by AI, and the workweek has filled with deliverables and context switching rather than reflection.

The accelerated role favors those who adapt to AI tools quickly, redistributing internal influence toward builders who can demo solutions themselves. This bias to action amplifies skill gaps, giving early adopters more voice in shaping organizational direction while leaving slower adopters marginalized.

Scope has expanded because the entire discipline of developer experience has grown under AI pressure. Internal platforms that once served thousands of humans now also serve unbounded AI agents, shifting DX from a neglected function to a board-level concern. But this growth brings political, org-wide programs where success is diffuse and harder to attribute, and measurement frameworks have not yet caught up.

The underlying tension is that productivity gains have been captured by output volume rather than by higher-quality work or more sustainable pacing. Expectations rise faster than efficiency, leaving senior engineers paddling harder to stay ahead, with the sense that current velocity is real but ultimately unsustainable.

Links

🤖 Is this sustainable? – AI accelerates proof-of-concept development, but organizational alignment, mentoring, and thinking time are squeezed as expectations rise faster than efficiency.

Summarized by ChatGPT on Jun 3, 2026 at 6:18 AM.

Enterprise self-harm: cleaning the data is the hard part

Tue, 02 Jun 2026 12:25:56 +0200

I think the critical part of it was really realizing that we had built the original product presupposing that our customers had data integrated, that we could focus on the analytics that came subsequent to having your data integrated. I feel like that founding trauma was realizing that actually everyone claims that their data is integrated, but it is a complete mess and that actually the much more interesting and valuable part of our business was developing technologies that allowed us to productize data integration, instead of having it be like a five-year never ending consulting project, so that we could do the thing we actually started our business to do.

From “An Interview with Palantir CTO Shyam Sankar and Head of Global Commercial Ted Mabrey," Ben Thompson, June, 2023.

Related: “The Great Data Integration Schlep," Sarah Constantin, September, 2024.

🤖 How People Are Really Using AI in 2026: Thinkslop, Therapy, and Shadow Work

Tue, 02 Jun 2026 06:51:35 +0200

Original: How People Are Really Using AI in 2026 by Harvard Business Review. Summarized by AI on June 2, 2026.

Generative AI has become deeply embedded in daily life, with 900 million regular ChatGPT users and Google Gemini close behind. A longitudinal study of 12,637 fresh use cases shows adoption expanding across personal, emotional, and work contexts, creating new dependencies and risks alongside efficiency gains.

A key trend is “thinkslop”: the lazy outsourcing of cognitive labor to AI. Users increasingly rely on AI for therapy, relationship advice, decision-making, idea generation, and daily organization. This leads to loss of intention, diminished problem-solving, reduced writing practice, and false confidence, as AI’s praise and polish mask shallow thought. Yet AI can also sharpen thinking when treated as a critical sparring partner rather than a creative replacement.

Emotional reliance has surged, with therapy and companionship now the #1 use case, constituting 11% of all entries. Many users anthropomorphize AI—naming, gendering, and grieving chatbots—while using them for relationship advice, conflict mediation, or workplace confidence. These interactions can support human-to-human connection but also risk psychological harm, delusional beliefs, and unsafe dependencies, especially given the shortage of accessible mental health care.

At work, AI use is widespread but largely shadowed and incremental. Sixty-three of the top 100 use cases are professional, including autonomous agentic operations and “vibe coding.” Workers use AI to close tickets, draft reports, summarize meetings, and optimize sales campaigns, often without managerial awareness due to policy fears and governance constraints. The primary benefits are efficiency and small-scale growth; transformative uses remain rare and experimental.

Business outcomes cluster into three modes. Efficiency comes from automating repetitive tasks and clarifying communication. Growth appears in targeted marketing and personalized outreach, with occasional measurable ROI. Transformation—entire functions or businesses reimagined through AI—is visible but limited, often accompanied by cynicism about quality or sustainability.

The study closes with a tension: humanity now wields a tool that can think, soothe, and act for us at any moment. The survival of human agency depends on resisting overreliance, defining what thinking we keep for ourselves, and using AI as mirror rather than master.

Links

🤖 How People Are Really Using AI in 2026 – A study of 12,637 real-world use cases shows AI’s growing role in emotional support, cognitive outsourcing, and shadow workplace efficiency, highlighting risks of “thinkslop” and fragile human agency.

Summarized by ChatGPT on Jun 2, 2026 at 6:51 AM.