Coté

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.”

Logoff

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.

Why aren't all images super-secure, or hardned?

Tue, 02 Jun 2026 06:32:01 +0200

Here’s what I learned: container base images grew up as a developer convenience tool, not a security artifact. Installing extra packages from the command line is one of the first things any Docker tutorial teaches–Docker’s own Dockerfile guide includes apt-get install–and many of the most popular official images ship a full toolchain by default, with -slimand -alpine variants offered precisely because the defaults carry more than most workloads need, and changing them would have broken enough downstream workflows that it was never going to be a routine upstream decision.

There is also an incentive split. The upstream distribution maintainers and the developers using their images are different people with different priorities.

Users want to customize and add to images: they want new functionality that didn’t come out of the box.

🔗 Why Hardened Images are Suddenly Everywhere

Three reasons why a "batteries included" platform is urgently needed right now

Mon, 01 Jun 2026 07:06:19 +0200

Removing product as a bottleneck:

The conversation around PaaS is urgent again, and AI is why. Code generation can speed up your development cycles, building and pushing features faster, but production delays will persist if you’re still deploying at the same speed as before. To avoid eroding the benefits of code generation, you need to deploy applications nearly as fast as they can be coded with AI

Better, sustainable security:

Security works the same way. When your team makes the decision to pull from open-source components and stitch them together, you now own every security gap between them – data at rest, data in flight, and its running state. This seems like a worthwhile investment at first, but as recent research suggests, AI-assisted attacks are on the rise, and platform teams won’t be able to keep up with surging security vulnerabilities in the build-it-yourself model.

Getting fast access to new technologies you can use to build your competitive advantage:

The pace of AI innovation itself compounds the problem. Whether it’s shadow AI use, MCP servers, agentic harnesses, this week’s new foundation models, or whatever emerges next, the landscape is evolving fast enough that what’s bleeding-edge today may be table stakes in six months. When you build your own platform, you’re on the hook to evaluate each layer of these new technologies, determine how they fit into your stack, and then integrate them yourself, on top of everything else your platform team is already managing to keep the business going.

A proven, pre-integrated platform stack looks real nice when you need to deliver instead of tinker around with the same thing all your competitors have. Why not TryTanzu.ai?

Original 🔗: The DIY platform trap that’s burning out engineering teams

🤖 AI Productivity Gains Stall at Firm Level: The Three Stages of ROI

Mon, 01 Jun 2026 07:02:19 +0200

Original: Why AI isn’t showing up on your bottom line by Azeem Azhar. Summarized by AI on June 1, 2026.

AI tools have made individual workers faster and more productive, with engineers producing more code and teams feeling tangible time savings. Yet firms see little proportional ROI, echoing Robert Solow’s paradox of computers appearing everywhere except productivity stats. Only 27% of executives report AI meeting their expectations, reflecting a gap between personal efficiency gains and organizational outcomes.

This mismatch follows the classic productivity J-curve for general-purpose technologies. Initial adoption boosts individual output but requires complementary investments in processes and decision-making before firm-level gains appear. The historical analogy to electrification shows three phases: Stage 1 “lightbulb” improvements help individuals, Stage 2 “group drive” saves costs in existing workflows, and Stage 3 “unit drive” reorganizes the entire system for throughput.

Most organizations are stuck in Stage 1 or Stage 2. AI chatbots and agents accelerate specific tasks or workflows but remain bound to legacy organizational structures. Gains pile up at the edges, but congestion emerges when faster teams wait on traditional review cycles, managerial approvals, or decision pipelines that cannot match the new pace.

Reaching Stage 3 requires rebuilding around decision speed, not just task speed. Firms must reorient processes so that AI can interpret signals, make decisions, and execute actions without being bottlenecked by human intermediaries. At this level, AI becomes a cognitive layer that moves from incremental cost savings to transforming output and time-to-market.

Without this rearchitecture, additional AI workflows simply add more output to blocked pipelines. True ROI will appear only when companies move beyond using AI as a faster lightbulb or group drive and embrace the unit drive model of autonomous, end-to-end execution.

Links

🤖 Why AI isn’t showing up on your bottom line – Individual AI gains fail to translate into firm-level ROI without organizational redesign, echoing historical patterns of electrification and the productivity J-curve.

Summarized by ChatGPT on Jun 1, 2026 at 7:02 AM.

Enterprise AI ROI strategy: do both individual productivity and new revenue sources

Mon, 01 Jun 2026 06:48:58 +0200

“The most important question for CFOs is not how much can the organization spend on AI, but whether those investments are being deployed in ways that reinforce the business’s core growth and value drivers,” said Carlsen. “Moreover, the near-identical amount of use cases for efficiency and productivity use cases between efficient growth firms and control peers suggests that productivity-focused AI investments alone do not explain performance differences, and that automation by itself is increasingly becoming table stakes rather than a durable source of advantage.” … “For CFOs, the implication is to evaluate AI investments not only by the return of individual use cases, but also by how well those capabilities reinforce broader growth, product and decision processes across the enterprise,” said Carlsen.

I’m going to say this means: (1) use AI to improve how individual work, making them more “productive.” This get your bottom line improvements, but, (2) the way you’ll make money with AI is coming up with new things to sell in your business, features, products, markets you expand into, incremental improvements to your product that customer upgrade to, etc. that is where growth comes from.

The first is, or will, be commodity and required for all, the second likely more challenging and ignored by many.

On the finance department, there’s plenty of the first:

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.

But people still have unrealistic expectations about quickly new technology and transformation can be rolled out:

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

Originals:

🔗 Gartner Says CFOs Must Stop Mistaking Finance AI Deployment for Value Creation

🔗 Gartner Says CFOs Gain a Competitive Advantages from Strategic AI Deployment, Not AI Spending Levels

Sat, 30 May 2026 08:09:20 +0200

From Run Lola Run.

Sat, 30 May 2026 08:07:25 +0200

Sucking Air Through Their Teeth, 50/50 success/failure, and Dell's $43B AI Server Quarter - Related to your interests, Friday

Fri, 29 May 2026 10:36:01 +0200

Also: the improbable life, four years working at AWS, an AI-SDLC panel debate.

Spotted by Dan Bettinger in Austin.

Most generative AI and custom model projects will be a bust: Gartner - ‘Analyst firm Gartner thinks at least half of all generative AI projects “will overrun their budgeted costs due to poor architectural choices and lack of operational know-how,” and most organizations that try to build custom models “will abandon their efforts due to costs, complexity and technical debt in their deployments."’ See also my commentary.
AI server demand drives staggering revenue growth for Dell and its stock soars - “The company delivered earnings before certain costs such as stock compensation of $4.86 per share on revenue of $43.84 billion, up by a staggering 88% from the same period one year ago. That’s a truly astonishing leap for such an established company, and it crushed Wall Street’s targets. Analysts were looking for earnings of just $2.94 per share on much lower sales of just $35.43 billion. // Dell delivered an even more impressive jump in its bottom line. It reported net income of $3.44 billion at the end of the quarter, up 256% from a year earlier, when it delivered a profit of just $965 million. // Since reentering the public markets in late 2018, five years after Dell had taken itself private, the server maker had never managed to exceed more than 39% growth in any quarter.”
I think Anthropic and OpenAI have found product-market fit - ‘I think both of these stories support my “product-market fit” hypothesis. The best advice I ever heard on pricing a product was that your customer should suck air through their teeth and then say yes. Uber’s budget overrun and Microsoft’s seat cancellations look like that effect playing out in practice.’
Amazon Web Services - Four Years and Out - By all accounts, working at Amazon is a tough gig.
Halide Mark III - I love a cool looking camera app.
Your Most Improbable Life - Yeah, sure. But it sounds like a lot of work. Can we strive for an equilibrium of not having to strive all the time?

From: Anthropic's run-rate revenue hits $47 billion

AI Summaries

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

Google SRE AI: Practices & Uses Extracted - how SREs are using AI, summarized down to the tools and practices.
🤖 The AI Race Is Failing Because It Ignores Half the World
🤖 Engineering Leaders Debate AI’s SDLC Impact: Fewer Tasks, Not Fewer Engineers, as Tech Debt and Code-Review Bottlenecks Get Reframed - representative of the “spend up coding is not the point” perspective. Plus, looking at the rest of the app delivery process and thinking how AI can help.

From: Is AI putting graduates out of work already?

ICYMI

Nobody Wants to Be a Measurer - SOFTWARE DEFINED TALK #574 - “This week, the hosts discuss the Cloudflare CEO’s op-ed, upcoming tech IPOs and GitHub getting breached. Plus, ranking their favorite manifestos.”
50%+ failure is normal
If speeding up coding was never the problem…then why are we spending money on speeding it up? - they say coding is only 15% to 20% of doing software…

Conferences

Conferences I’ll be at and some that I’m interested in.

J-Spring, June 4th, 2026 in Utrecht - Coté speaking.
WeAreDevelopers Europe, July 8-10, 2026 in Berlin, Coté speaking.
WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD26. Also, while supplies last, there are 25 free tickets available.
DevOpsDays Istanbul, October 24th, 2026 - Coté keynoting.
VMware User Groups (VMUGs) - Dallas (June 9-11, 2026), Orlando (October 20-22, 2026).

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Another week. Closer to full-on summer.

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50%+ failure is normal

Fri, 29 May 2026 10:16:00 +0200

Analyst firm Gartner thinks at least half of all generative AI projects "will overrun their budgeted costs due to poor architectural choices and lack of operational know-how," and most organizations that try to build custom models "will abandon their efforts due to costs, complexity and technical debt in their deployments."

Yes, and this matches decades software project ~~failure~~success studies from the Standish Group.

The success rate of projects has held steady forever:

Sources: 2009 study and 2015 study overviews.

That chart ends 11 years ago, but I haven't heard a lot of reports that the numbers have changed much over the years...case in point the opening quote!

In IT and software, very few projects are successful on the iron triangle of budget, schedule, and quality¹.

You can take this to mean that expectations were unrealistic, or that there is just genuine failure. I favor the first. I'm more of a "I'm not late to this meeting, it was just scheduled at the wrong time" kind of guy.

There's some kind of Jevons Paradox thing here. Each time we optimize how we make software, we then take on more challenging and difficult tasks, likely causing setbacks again. To me, this is what accounts for the low success rate.

If you don't want to do new things and try to do better, you could get those success numbers up probably by just continuing to do what works.

But, you add in a new technology, and while you're figuring it out, it feels like you're failing.

Back in the digital transformation days, we'd be clever and say: you're not failing, you're learning.

🔗 Most generative AI and custom model projects will be a bust: Gartner

I think of quality as more than "bug free." It also includes "does the software solve our problem, are the features done well," e.g., did we get something useful? ↩