Tag: Seconds

Identify the post must compare in the section of the readings recommended

  • The Scariest AI Risk Isn’t That it Will Take Control. 
It’s That We’ll Hand it Over.

    The Scariest AI Risk Isn’t That it Will Take Control. It’s That We’ll Hand it Over.

    We keep imagining the wrong AI apocalypse.

    Robots turn against humans.

    A superintelligence escapes from a lab.

    Machines decide we’re no longer useful.

    Great material for Netflix.

    But Sam Altman recently described a scenario that should worry professionals far more.

    Because it doesn’t require AI to become evil.

    It only requires us to become lazy.

    During a fireside chat at the Federal Reserve, Altman outlined three ways advanced AI could go badly wrong.

    The first is geopolitical.

    An adversary gets superintelligence first.

    It uses it to design biological weapons, attack critical infrastructure, penetrate financial systems or create capabilities that the rest of the world cannot defend against.

    Altman specifically warned that the biological and cybersecurity capabilities of current models are already becoming significant.

    Terrifying.

    But easy to understand.

    The second is the Hollywood scenario.

    We create something powerful enough that we can no longer control it.

    The AI doesn’t want to be switched off.

    Humans discover that the machine has objectives that no longer match ours.

    This is why AI laboratories spend enormous effort on alignment and control research.

    Altman described it as less likely than malicious human use, but extremely serious if it happened.

    Then comes the third scenario.

    And this is the one I can’t stop thinking about.

    AI never rebels.

    It never wakes up.

    It never announces that humanity is obsolete.

    It simply gets better.

    And better.

    And better.

    Until asking the machine becomes easier than thinking.

    Altman calls one early manifestation “emotional over-reliance.”

    He described young people saying they cannot make decisions without first telling ChatGPT what is happening in their lives.

    Then he pushed the scenario much further.

    Imagine an AI so capable that the President of the United States cannot make a better decision than the model recommends.

    The President follows the recommendation.

    Again.

    And again.

    Not because anyone forced him.

    Because statistically, it works.

    Imagine the CEO doing the same thing.

    The doctor.

    The engineer.

    The investor.

    The manager.

    You.

    Every individual decision may be perfectly rational.

    That’s what makes this scenario dangerous.

    There is no dramatic moment when humans lose control.

    We outsource it one sensible decision at a time.

    And I think this is where the AI conversation for professionals needs to change.

    We’ve spent years asking:

    Can AI do my job?

    Wrong question.

    The better question is:

    How much of my judgement am I willing to stop exercising because AI can exercise it faster?

    I’ve worked around technology for decades.

    Every major technology removes friction.

    That’s usually the point.

    Calculators removed arithmetic friction.

    Google removed information-retrieval friction.

    GPS removed navigation friction.

    AI is different.

    It can remove cognitive friction.

    The uncomfortable part before a decision.

    The doubt.

    The comparison.

    The argument with yourself.

    The search for alternatives.

    The moment when experience says:

    “The answer looks right, but something doesn’t feel right.”

    That’s precisely where professional judgement lives.

    And if you outsource that friction often enough, you may eventually outsource the capability that produced your value in the first place.

    This is why I don’t think the winning professionals of the AI era will be the people who use AI the most.

    They’ll be the people who know where to stop using it.

    Use AI to generate options.

    Use it to challenge assumptions.

    Use it to analyse more information than you ever could alone.

    Use it to accelerate the boring parts.

    But keep one thing stubbornly human:

    the final judgement.

    Because the most dangerous future may not be one where AI takes power from us.

    It may be one where AI becomes so useful, so accurate and so convenient that we happily give power away.

    No rebellion.

    No malevolence.

    No red flashing lights.

    Just billions of perfectly reasonable decisions.

    And one day we realise we’ve forgotten how to make them ourselves.

    AI dependency won’t arrive looking like a crisis.

    It will arrive looking like convenience.

  • I Put a Red Alert on My Own Website. Three Weeks Later, Nobody Saw It.

    I Put a Red Alert on My Own Website. Three Weeks Later, Nobody Saw It.

    I built a red bar across the top of my site last month.

    Full width. White text. High contrast — I checked the numbers, 7.3 to 1, comfortably past the accessibility threshold. It was, by every measure I could apply, impossible to miss.

    My regular readers missed it.

    Not because they’re careless. Because it was still there. It had been there on their last four visits, saying the same thing, and their brains had done what brains do with anything that never changes.

    They filed it as furniture.

    And the moment I understood that, I realised I’d been watching the same failure for thirty years in rooms where the stakes were considerably higher than a newsletter signup.

    The version that costs money

    Walk into any network operations centre and look at the alarm board.

    Count the amber.

    Now ask how long each one has been amber.

    I have had that conversation more times than I can reconstruct, and the answer is almost never “since this morning.” It’s “that one’s been like that since we migrated.” It’s “oh, that’s just the link to the old site, ignore it.” It’s, occasionally and memorably, “I don’t actually know, it was like that when I got here.”

    The board is not showing the operator what’s wrong. It’s showing them what has been wrong long enough to become normal.

    Every one of those permanent ambers is doing something worse than nothing. It’s raising the floor. It’s teaching the room that colour doesn’t mean urgency, that the board is decorative, that judgement happens somewhere other than the screen everyone paid for.

    Post-incident reviews in process industries have been saying this for decades: when something goes genuinely wrong, the alarm that mattered arrived in a flood of alarms that didn’t, and the operator had already learned to discount all of them.

    The failure isn’t inattention.

    The failure is that we asked people to ignore things, they learned to, and then we were surprised.

    Why permanence kills signal

    Here’s the part that took me years to state properly.

    Information lives in change, not in presence.

    A warning that is always on carries no information. It’s a constant. You could delete it and the operator’s picture of the world would be identical, because they’d already deleted it themselves — just internally, where you can’t see them do it.

    This is not a discipline problem. You cannot train it away. Attention is a filter that adapts to its environment, and the environment you built taught it that red means nothing.

    Which produces the ugly conclusion:

    Your most urgent channel is your most fragile one.

    The louder the mechanism, the faster it burns out under permanence. A red banner is more susceptible to this than a plain sentence, because red is a promise. Break the promise a few times and the reader stops reading the colour, and now you’ve spent something you can’t easily buy back.

    I spent it on my own site in three weeks.

    The bit that connects to everything else I write

    Every permanent alert is a decision somebody didn’t make.

    That’s the whole thing.

    Nobody decided the link would stay degraded. Somebody deferred fixing it, then deferred deciding whether it mattered, and the amber became the record of that deferral — a small monument to a choice postponed.

    Multiply by the forty of them on the board, and the alarm system stops being a monitoring tool and becomes an archive of everything the organisation has quietly agreed not to resolve.

    That’s the complexity tax in its most visible form. Not an abstraction. A screen full of it, in colour, that everyone has stopped reading.

    And it arrives with a second bill: the next real incident costs more, because the mechanism that should have caught it early has been taught to be ignored.

    Three rules I now apply to anything that shouts

    Every permanent signal gets an expiry date.

    Not “we’ll review it.” A date. When the date arrives, either the condition is fixed or the alert is deliberately downgraded to something quieter — a log line, a report entry, a dashboard the right person actually opens. What is not allowed is for it to simply continue.

    My red bar now has a calendar entry. Six weeks, then it changes or it goes.

    Count what you’re asking people to ignore.

    This is the single most useful audit I know, and almost nobody runs it. Not “how many alerts do we have” — how many are we asking a human being to look past every day?

    If the number is above about five, you don’t have a monitoring system. You have a training programme in not looking.

    Test by removal.

    If you switch it off and nobody notices for a week, it was never doing its job. That’s not an argument for leaving it on. That’s the evidence you needed to take it out and put the attention somewhere it will be spent.

    The part I got wrong

    I want to be honest about what my instinct was when I saw the banner wasn’t working.

    Make it brighter.

    That’s the reflex — the alert isn’t landing, so increase the volume. Bigger type. Stronger red. Maybe animate it.

    It’s the same reflex that adds a fifth severity level to an alarm system that already has four nobody respects. It escalates the format instead of fixing the reason, and it always buys a few days before the new level gets absorbed into the furniture too.

    The answer was never more contrast.

    The answer was fewer permanent things, each of which changes when something actually changes.

    Go and look at your board

    Whatever your board is.

    The alarm console. The risk register nobody has reordered since the audit. The compliance dashboard that has shown the same two ambers since the NIS2 assessment. The banner on your own website.

    Count the things that have been in the same state for more than a month.

    That number is not a measure of your monitoring.

    It’s a measure of how much your organisation has agreed to stop seeing.

    And the thing about what you’ve stopped seeing is that it’s still there, still costing, still perfectly visible to whoever eventually writes the incident report.

  • I Didn’t Choose Digital Transformation. It Arrived Where I Was Already Standing.

    I Didn’t Choose Digital Transformation. It Arrived Where I Was Already Standing.

    People ask me why I work in digital transformation, and the honest answer is uncomfortable.

    I didn’t pick it.

    When I started, the phrase didn’t exist. There was no category, no conference track, no consultancy practice with that name on the door. There were networks that had to stay up, and there was me, twenty-something, with a degree in electronic engineering from La Sapienza and no idea what I’d walked into.

    The field arrived later. It arrived exactly where I was already standing.

    What the word actually named

    For about fifteen years, I did work nobody called “transformation”. Migrations. Refreshes. Integrations between systems that were never designed to speak to each other. Contract transitions where one vendor left and another arrived and the network in between had to carry trains, calls, or water regardless.

    Then, sometime in the 2010s, all of it got a name.

    And the name attracted a crowd — strategy decks, maturity models, four-quadrant frameworks, people who could describe the destination beautifully and had never once been in a room at 3 a.m. deciding whether to roll back.

    That’s when I understood what I actually had. Not a specialism. A vantage point.

    I had watched the thing get named. I knew what the word was covering up.

    The gap that became the work

    Here’s what I repeatedly saw from inside.

    The strategy was almost never wrong. The transformation programmes I watched fail didn’t fail because someone chose the wrong architecture. They failed in the space between a decision and its consequences — the eighteen months where nobody owns the outcome, the handover fortnight nobody scheduled, the end-of-support hardware everyone agreed not to raise.

    Consultants who arrive at the start don’t see that space. They present, they leave, the deck gets approved.

    Operators who live in that space can’t name what they’re seeing. They experience it as bad luck, or bad management, or Tuesday.

    I ended up in the narrow band between the two. Long enough in operations to have felt it, close enough to procurement and vendor negotiation to see how the decision was made in the first place.

    That’s not a career I designed. It’s a position I noticed I was occupying, and then took seriously.

    Why I stayed when the exits were open

    There were easier options. Sales, and the compensation that comes with it. Pure management, further from the systems. Or the version of this field where you never touch anything real and never get blamed when it breaks.

    I stayed for one reason, and it isn’t nobility.

    The problems here are unsolved.

    Thirty years in, organisations still cannot answer the simplest question I can ask them: what did the delay actually cost? They can price the project. They cannot price the postponement. That gap has cost the clients I’ve worked with more than any technology decision I’ve ever watched them make.

    Nobody has solved that. Not the vendors, not the big four, not the frameworks.

    A field where the central problem is still open is a good place to spend a career. A field where everything works is a field that no longer needs you.

    What the answer really is

    So — why digital transformation?

    Because I was already in critical infrastructure when the word was invented, and I stayed close enough to the systems to watch what the word left out.

    Because the interesting part was never the technology. It was the eighteen months of silence between a decision and its bill.

    And because I’d rather work on the part nobody has figured out than on the part that already has a framework.

    That’s not a mission statement. It’s just where the work was.


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  • The Tax Nobody Votes For

    The Tax Nobody Votes For

    Where complexity actually enters an organisation, and why every decision that created it looked correct at the time.

    Coming Soon