Coté

Broadcom CEO: VMware still ringing the cash registers - ‘That translates into 32% quarter-on-quarter VMware bookings growth." And: ‘When it comes to revenue tailwinds, AI is driving “about two-thirds in compute and one-third in networking” said Broadcom CFO Spears.'

Executive translation. - “Many high-agency managers try to prevent executives from doing silly things, but it’s almost always more effective to translate their energy for a silly thing into energy for a useful thing. It also leaves the executive feeling supported by your work rather than viewing you as an obstacle to their progress."

Tanzu 10 Sets New Standard for Private Cloud-Native Platforms - The Futurum Group - ”According to a recent survey by Futurum Research, organizations that have adopted cloud-native technologies have seen an average of 20% faster time to market and a 15% reduction in IT costs.”

2025 IT Budgets See 5.5% Increase as CIOs Invest in AI and Cloud - “With a projected 5.5% increase in IT budgets, the year ahead is set to focus heavily on artificial intelligence (AI) and cloud computing."

Five Lessons Learned From a Lifetime of Platform-as-a-Product - Text version of a good talk.

Is AI a Silver Bullet? - “This is the trade off we have seen before: eliminating the accidental complexity of authoring code has less value than we think because code spends most of its time in maintenance, so the maintainability of the code is more important than the speed to author it."

How the buildings you occupy might be affecting your brain | Psyche Ideas - “Could bad architecture also be contributing to the development of neurodegenerative and psychiatric disorders?"

The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI | RAND - As ever, communicating the requirements and the problem to be solved to IT is difficult and often results in solving the wrong problem, at least in the right way: “In failed projects, either the business leadership does not make them- selves available to discuss whether the choices made by the technical team align with their intent, or they do not realize that the metrics measuring the success of the AI model do not truly represent the metrics of success for its intended purpose. For example, business leaders may say that they need an ML algorithm that tells them the price to set for a product–but what they actually need is the price that gives them the greatest profit margin instead of the price that sells the most items. The data science team lacks this business context and therefore might make the wrong assumptions. These kinds of errors often become obvious only after the data science team delivers a completed AI model and attempts to integrate it into day-to-day business operations.”

Why “AI” projects fail - “And we’ve been doing this for decades now, with every new technology we spend a lot of money to get a lot of bloody noses for way too little outcome. Because we keep not looking at actual, real problems in front of us – that the people affected by them probably can tell you at least a significant part of the solution to. No we want a magic tool to make the problem disappear. Which is a significantly different thing than solving it. Because actually solving a problem includes first fully understanding the reasons for the existence of the problem, reasons that often come back to an organizations’ structure, their – sometimes inconsistent – goals and their social structure in addition to actual technical issues.” And: “AI is so good at writing summaries of all the meetings I can then have summarized afterwards to understand what was going on” says that your organization has probably too many meetings that are not structured well and that you don’t think that understanding why people say a thing, how they present their case is important (which makes you a bad manager)."

Moonbound Revisited - “Several decades ago, the semiotician A. J. Greimas claimed that all stories are comprised of six actants, in three pairs: Subject/Object; Sender/Receiver; Helper/Opponent."

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