davideisinger.com

ludic-mataroa-blog-ss5swh.txt (39744B)

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 [1]Ludicity
 
 AI Mania Is Eviscerating Global Decision-Making
 
 Published on July 18, 2026
 
 Note: This has been cross-posted to my company's blog, in case you think there
 is some use in sharing with someone in a format that looks more authoritative.
 Link [2]here.
 
     I strongly believe there are entire companies right now under heavy AI
     psychosis and it’s impossible to have rational conversations with them
     about it. I can’t name any specific people because they include personal
     friends I deeply respect, but I worry about how this plays out.
 
     – [3]Mitchell Hashimoto, of HashiCorp and Ghostty fame
 
 Over the past year, I’ve run point on all of our company’s sales, led the
 technical components of all but two of our engagements, and over the lifetime
 of this blog have had something like 300 catchups with professionals from
 around the world. This has ranged from people on the ground in niche service
 industries to executives at Fortune 500 companies^[4]1. Because of this, I've
 had a front-row view to our collective institutions across both the private and
 public sector undergoing breath-taking mass psychosis. This essay is an attempt
 to describe the bizarre dynamics that are currently at play, as I am in the
 rare position where my wellbeing is not contingent on paying lip service to
 madness, and to reassure the people trying to survive amidst all of this that
 they are not crazy.
 
 The reality is thus: the people in charge either have no plan, or see no path
 forwards other than keeping their heads down. Not at banks, not at hospitals,
 not in our government institutions. The world’s organisations have been
 captured by people in the throes of frothing excitement, and saner people who
 now live in a state of constant commingled fear and frustration.
 
 I. AI Investments Are Generally Total Failures
 
     Reading this while working for a division that pivoted to provide
     interfaces for agentic workflows, only to discover that only ten users had
     ever touched the products we made for agents, only to pivot again to
     support for agentic workflows, which has a lot of competition because every
     company has to do something agentic now and there's only like four things
     you can do in that space, is bracing.
 
     – An editor of this essay
 
 Are companies actually seeing massive productivity gains from their AI
 adoption? Does any of this sordid affair make sense?
 
 This should be an easy question, but it is surprisingly hard to get a straight
 answer to it. Executives that tell the press that their company has gone insane
 will quickly find themselves removed from their positions. Employees who are
 honest will find themselves fired in short-order, or “randomly” selected for a
 round of layoffs. In fact, it is in the interests of almost every actor in the
 space – boards, executives, employees, vendors, consultants – to obfuscate and
 misrepresent the success rate of AI projects. Many publicly traded companies
 are putting out announcements about their AI productivity gains when I know for
 a fact that the businesses have done nothing other than purchase Copilot
 licenses and declare victory.
 
 Yet we need to know if these projects are panning out – if the total focus on
 AI as a core tenet of business strategy is succeeding at a reasonable rate,
 then a discussion about the relative risk and reward is warranted.
 
 Unfortunately, we live in a dark timeline. All of the AI projects we have
 observed as a team are failing. Every single one – we have seen 0% success in a
 year and a half, not only amongst projects we have been asked to participate in
 ^[5]2, but even within projects that we have observed in passing while doing
 totally unrelated work. Even if you grant that AI tooling accelerates specific
 workloads, the method and scale of the current investments is senseless.
 Frequently the failure is not related to AI itself, but rather that companies
 are terminally bad at running software projects effectively, and as [6]I have
 remarked previously, AI projects are subject to all the failure modes of normal
 projects plus you can get everything right and then still fail because of the
 method's novelty. Very few companies are so good at shipping software that they
 can afford the extra risk profile.
 
 Often enough, though, it’s an actual failure in what LLMs can accomplish. The
 most common version of this, being rolled out across businesses around the
 world, is the internally-facing chatbot, or for the more daring company, the
 customer-facing chatbot. The story is always the same. For the former, I’ve
 never seen substantial internal uptake from inside a business. Employees don’t
 use internal chatbots because companies tend to have low-quality documentation
 and an LLM is not psychic – it can only know things that have been written down
 and made accessible. For the latter customer-facing applications, I have rarely
 had a pleasant experience as a consumer, with perhaps the exception of live
 transcription during medical appointments – hardly something worth pivoting an
 entire organisation around. In both cases, project leaders are very careful to
 avoid tracking basic metrics, such as whether the tools are being used at all,
 or they track metrics that are easily gamed.
 
 For example, my last consumer interaction was attempting to get help from
 Mitsubishi following an automotive failure, where a very polite robot asked me
 to describe the problem and that I’d receive a call back as soon as someone was
 available. This was the single most competent implementation of such a project
 I’ve seen in the wild, in that the voice was natural sounding, responded
 quickly, was clearly “live” in production, and promised a swift resolution.
 
 That was six months ago, and I did not, in fact, get a call back.
 
 When Mitsubishi did not call me back, what happened? Did that request just go
 into the void, showing one less incident for the year? Does it appear that the
 phone bot resolved my query without the need for human intervention? All we
 know is that it didn’t show up as an error, or I’d have received a call. I’m
 sure it looks great in all sorts of ways except the one that matters, which is
 that I was planning to buy a car and decided not to buy another one of theirs.
 
 For this reason, our team has quickly learned while on an engagement not to ask
 anything about ongoing AI projects in any context – by the time that project
 has started, it is too late for the management team, and intervention is not
 possible until a crisis point is inevitably reached. There is no conceivable
 positive outcome. The failure rate is so high that even basic inquiry leaves us
 in an untenable position. Any coherent question about how it’s going, what the
 goal is, who is using it, constitutes an inadvertent attack on the chain of
 command responsible for the work because there are no good answers to anything.
 Even in rare cases where my interlocutor has stated that things are going well
 (usually while the project is still mid-flight and failure has not had a chance
 to manifest), it is generally obvious that they are doomed, but at least in
 these cases I can simply agree and then go home to scream into a pillow for six
 hours straight^[7]3.
 
 All of this is to say that I am very confident that almost every report at a
 company about “massive AI productivity gains” is untrue as a matter of brute
 fact. Even if some companies are seeing clear gains, this is the exception, not
 the norm. With that assumption in place, we can talk about the dynamics at
 play, and how it has become impossible for many organisations to stay focused
 on things that actually matter to their long-term (or even short-term) health.
 
 II. Heretics Will Be Shot
 
 It has become outright dangerous to even raise the possibility that AI might
 not be the solution to a problem, let alone be the sole focus of a company’s
 entire strategy.
 
 In every sufficiently large business we have observed (say, with 500+
 employees), we have noted that continued advancement, and increasingly
 continued employment, has started to require repeated professions of belief in
 the transformative power of AI for said business. I am not talking about
 providing ideas about how to use AI in the business – I mean religious
 profession, declarations of faith. Overwhelmingly these statements are made by
 non-technicians, though it is not uncommon for technicians to emit deranged
 statements to curry favour.
 
 There have been several occasions where I have seen someone, apropos of
 nothing, blurt out almost word-for-word “AI is changing everything”, only to
 concede moments later that their organisation does not currently use LLMs for
 anything, and indeed, that they cannot name a single thing that has changed
 other than they get some use out of ChatGPT (frequently the free-tier). In one
 extreme case, I have seen an executive confess that they had never even used
 ChatGPT or any AI tool in their life, immediately after producing a technical
 strategy for an organisation with $2B+ in revenue which was entirely centered
 around AI.
 
 Initially these statements were so absurd on their face that I thought it was
 some cynical ploy to achieve thought leader status, and there are certainly
 some people doing this – I have had it admitted to me. But the broader reality
 is so much worse: people who have no background in the technology at all
 actually believe what they are saying. As a general rule you should avoid
 getting into business with a liar, but if you must, you can at least reason
 with them even if only in private. A true believer is much more threatening
 because they are impervious to even inducement by self-interest.
 
 The turning point in my belief was watching someone with a spectacular amount
 of money on the line fire their highest performers because they were achieving
 that performance without LLMs. When an employer publicly talks about AI
 innovation, we have to ask ourselves if they’re simply trying to manipulate the
 market or customers. When they privately commit to strategies like this with
 their own money at stake, with no attempt to communicate that strategy to
 external clients, I can only assume they really mean what they’re saying.
 
 A while ago, I wrote [8]“Contra Ptacek’s Terrible Article On AI”, which was
 focused on the fact that many of Ptacek’s points in his own essay [9]“My AI
 Skeptic Friends Are All Nuts” were internally inconsistent^[10]4. But on the
 crux of the matter, we are actually in total agreement, because he opens his
 essay with this:
 
     Tech execs are mandating LLM adoption. That’s bad strategy.
 
 Which is to say that we can sidestep arguments about the precise utility of
 LLMs entirely and we’re left in a very simple place – it is entirely obvious to
 both myself and Ptacek, two people that are coming at this from fairly opposed
 views, that people are being really, really stupid about this, and that
 organisations are demanding bizarre workflow constraints from their specialist
 staff.^[11]5
 
 These mandates have led to extremely strange places. Several of my peers now
 “AI-wash” their work, meaning that even when they can perfectly competently
 execute on their jobs to the satisfaction of their management teams, said
 managers are unhappy if the engineers haven’t used AI in the work… so now
 they’re lying about using LLMs even in contexts where their professional
 judgement is that they aren’t the appropriate tool. They just do the work, the
 same way they have for decades, and say Claude did it. Others are being
 measured on their AI bills with “token leaderboards”, where higher is better
 because I have evidently fallen into the pocket of Hell where the demons
 torment me by doing elaborate impressions of absolute fucking morons, so the
 people hired for their freakish ability to perform system optimisation do the
 obvious thing. They set the LLMs prompting themselves in a semi-plausible loop
 in case someone inspects the token consumption and then they watch Netflix. Not
 a single one has been caught, even when their own assessment of the output is
 that it isn’t suitable for deployment.
 
     Checking out a parallel copy of our Go repository and telling the AI to
     rewrite the whole thing in Zig while I work on something else just so I can
     keep my job. I hate this shit so much. My job has usage tracking and
     quotas. I don’t use it for actual work, I just spin it up and disregard the
     output.
 
     – An actual software engineer
 
 In fact, the only people I know of to be fired over this whole thing are people
 that have expressed visible doubt about this organisational strategy, which
 again, even Ptacek thinks is transparently dumb. The net result is that
 everyone has learned very quickly to praise executives on their visionary AI
 prowess, or they will be gunned down in the proverbial streets.
 
 III. AI Demos Are The Mind-Killer
 
 Bless me, Father, for I have sinned. It has been ∞ days since my last
 confession. I accuse myself of the following sins:
 
 One of the main pieces of infrastructure we deploy at our clients is an
 analytics-focused database called Snowflake – for a typical business, the bill
 is tiny because it’s a pay-as-you-go situation and we can process all their
 data in one minute a day, you get a very hands-off deployment, and in short it
 has many characteristics that are very pleasant for our work. One of the
 features in Snowflake that we don’t use is called Cortex.
 
 Cortex is their AI chatbot layer, with the ability to plug into metadata (for
 non-nerds, descriptions of your data, like what a column in a spreadsheet
 means) and query a company’s database autonomously. In theory, you can ask a
 question like “What was our revenue for last week?” and it will spit out an
 answer.
 
 It is not really suitable for production usage. From memory, the last time I
 was given a presentation on it, by actual Snowflake staff, they reported that
 ideal configuration results in something like ~92% accuracy due to the
 complexity of data at a large business (see: probably best-in-class for these
 tools, but imagine your CFO having one in every ten of their numbers be
 outright wrong) and there were serious issues with managing deployments.
 Nonetheless, it can be used to produce some very flashy demonstrations.
 
 On several occasions, we’ve been exposed to folks that have been sort of
 lukewarm on our main offerings, but they really, really wanted to use AI to
 perform a natural language query on their data. And we thought “Okay, if you
 really want to see it, maybe we can caveat this appropriately and show you what
 it might look like.”
 
 This was a terrible mistake. It backfired in the most predictable way
 imaginable – every lukewarm client that saw the chatbot in action, even with us
 telling them that it was not going to accomplish what they wanted, wanted to
 buy it immediately. Every other consideration, including millions of dollars
 that we could plausibly help them achieve by non-AI means, was swept aside. It
 was like a dark and terrible force seized control of their limbs, plunged their
 hands into their own chests, and presented their still-beating credit cards to
 us in grim supplication. We were so mortified by the inexplicable shift in
 energy that we (wisely) declined to take the money and ended the sales process,
 and soon thereafter removed Cortex from our list of demonstrations. It would
 have been too irresponsible to exploit this gap in their reasoning, and
 frankly, it was already irresponsible to have even run the demonstration –
 doctors don’t walk around showing off cool pills that they’d never prescribe.
 
 Watching the total 180°, that shift from ice-cold to red-hot buying frenzy, was
 a deeply unsettling experience. It was personally uncomfortable to see people
 that clearly didn’t gel with us interpersonally suddenly dying to enter an
 ongoing relationship, but more broadly uncomfortable because for a brief moment
 I began to understand what is happening in sales meetings around the world.
 There was no warning I could have given that would have made them refuse to buy
 the damn thing – their appetite was as large as their budget could stretch, and
 some part of me wonders if this is because they knew that their ravenous hunger
 would be present in their own customers. They’d just buy it from us, then pivot
 right to a larger company and mind control their leadership team until the buck
 finally stops with the loser that needs to justify the expense. The main
 protection against this seems to be that the median vendor is [12]so bad at
 their jobs that we had presented the first even somewhat-working products these
 people had seen, and this included an ASX-listed company that was already
 bragging about their AI usage. It took our team two hours to produce something
 that was frankly not that good – basically just typing text descriptions of
 data into a web browser – and it was still better than anything the leads had
 seen because they had nothing to show for all the investment.
 
 In fact, we have been forced to opt out of every sale where the lead has
 expressed anything beyond the most fleeting curiosity in the use of AI in their
 business. I don’t mean that we’ve heard that they’re interested in AI and
 elected to drop the contract on moral grounds. I mean that, over the course of
 the engagement, these people have exhibited a pattern of behavior that has made
 it near-impossible to sell to them without incurring reputational and legal
 risk, and are furthermore crafting management environments that I can only
 describe as cultish, ineffective, and “please dear God, do not let it be on
 earth as it is on LinkedIn”.
 
 IV. Executives, Game Theory, and The Emperor’s Clothes
 
     The good news is, CISOs are used to having to protect the business from
     their hare-brained initiatives, and this one isn’t really that different,
     except that there’s a cult-like atmosphere to it that you didn’t see with,
     say, the cloud. It almost doesn’t matter whether you embrace the initiative
     or not; there’s work to be done to manage the risk, so that’s what you do.
     From talking to CISOs everywhere, I would say most of them are quietly
     skeptical but afraid to speak up.
 
     – Career CISO and well-known speaker that asked to remain anonymous
 
 Despite the substantial prevalence of true believers, many of the people
 running large AI initiatives, or making public statements about them, do not
 believe what they are saying. There are “heads of AI” who read this blog, at
 companies with $1B+ in annually recurring revenue, who have written in to say
 they believe their job is totally fraudulent but it was the only promotion
 pathway remaining at the organisation.
 
 On a trip overseas, I had the privilege of a meeting with one of the Fortune
 500 executives mentioned at the beginning of the post, who will remain
 anonymous so that they are not executed by firing squad by their board. As we
 were chatting, it became clear that they were very switched-on and technically
 competent, and they also happened to be at a company that had committed to the
 usual battery of exorbitant claims about their recent innovations – we’ve
 100x’d our productivity, AI is the future of everything, I am but a vessel for
 OpenAI to make love to my wife. You know, normal things. But since I had them
 there without any microphones around, I asked why this was being repeated
 without opposition. Was it just sales fluff?
 
 The answer was a lot more interesting. It was partially ridiculous sales
 material being delivered to an easily excitable audience, but this was not the
 dominant factor constraining honesty. Executives at their customers were saying
 absurd things about achieving 100x productivity, and this meant that if any
 executive at the vendor said that these gains were not plausible, it would
 undermine the credibility of the customer’s executive, be perceived as an
 attack (or heresy), and possibly result in an enterprise contract cancellation.
 And getting enterprise contracts cancelled because you wanted to opine on
 something that doesn’t really matter to your organisation’s mission is a great
 way to get fired.
 
 But this company was also a major player, of the kind that signs enormous
 enterprise contracts with other companies. So presumably there is another
 vendor that has sold to them, and their CEO is worried that saying something
 sane will contradict this executive, and very quickly we can see how we can
 have executives around the world nervously pointing guns at each other, not
 wanting to be shot first but also watching everything gradually spiral out of
 control^[13]6. This is to say that we’re facing a [14]coordination problem
 around executives being honest around the AI gains they’ve witnessed – if they
 co-operate, they keep their jobs. If they defect, they will possibly be fired
 by their embarrassed peers (who have now been implicitly called liars, cowards,
 or incompetents) and then replaced with someone that will toe the line anyway.
 If they could all admit the truth at once there might be some hope, but there
 is no way to coordinate that event.
 
 This sounds deeply concerning, but it is worth noting that it means that some
 executives who are emitting nonsensical statements are not as dull as they
 might seem at first – they’re in a fraught political environment, where they
 are surrounded by many people that are gunning for their roles, and subject to
 the whims of a board that is undergoing similar pressure. Against all the
 dictates of reason, I have presented on navigating AI hype to people on S&P 500
 boards^[15]7 and they are in exactly the same situation – the main comments I
 remember from the session were board members admitting they were skeptical, but
 expressing anxiety that their positions were contingent on demanding AI
 investment. One of them commented “investing this early seems like risk without
 much upside”. About two years later, I can see now that their decade-old
 multi-billion dollar organisation is now branded as “AI-native”, whatever the
 hell that means.
 
 V. You Must Be This AI-Native To Ride
 
 All of the above converges on the state that we find ourselves in now, where
 effective decisionmaking has ground to a halt. Collectively, what started as a
 few people undergoing either destabilising psychological events or being caught
 up in hype has now resulted in an environment where leaders cannot speak
 honestly about their beliefs on how best to guide organisations, for fear of
 being removed, creating a sort of distributed government by assassination. This
 means that the least sensible recommendations are going totally unchallenged,
 resulting in employees being evaluated on totally gameable metrics such as
 “money spent on AI”, and those employees must play along to avoid being
 terminated. This has also created an insatiable appetite for purchasing “AI”
 solutions, which target both true believers that will believe implausible
 claims, and also non-believers that cannot decline the purchases without having
 their commitment to the cause coming into question.
 
 This means that all offers that are subject to internal politics at an
 ideologically captured organisation must include AI alignment, even if the
 value proposition is patently ambiguous. My assessment of the market so far is
 that a substantial component of the outburst of AI projects are actually non-AI
 projects with an AI element slapped on after the fact to pass the purity test.
 
 For example, I recently witnessed an organisation handling a database migration
 from an Oracle database to Snowflake – instead of handling the migration
 directly, the vendor bolted on a preliminary phase which involved trying to get
 an LLM to automate the translation of the Oracle-flavored SQL to
 Snowflake-flavored SQL. When the project failed (due to issues getting enough
 permissions to automate the work, not because an LLM can’t do something that
 easy), the vendor simply started handling the translation by hand but the
 company billed it as an AI-driven success because some inconsequential portion
 of the SQL had been translated by AI before being pasted over.
 
 What was actually purchased? A totally standard database migration to help an
 executive meet the strategic deliverable of decommissioning a system prior to
 license renewal. What was sold to their superiors? “I allocated a substantial
 percentage of my budget to AI and it helped me accomplish my mandate.” True AI
 projects, of the kind that is driven by an LLM as the sole mechanism underlying
 it, where the project can clearly fail to deliver specific numbers, are
 actually very rare. We mostly see them in the context of startups, and frankly
 we have stopped engaging with them because we kept getting to the end of the
 sales conversation and finding out they wanted us to build the product that
 they were marketing as completed.
 
 However, some projects simply do not have an easy way to tack on the AI label,
 or the person advocating for them either does not want to lie or has not
 understood that lying has become necessary. In all cases, this either kills the
 request for funding outright, or adds a pervasive and intractable drag on all
 communications, as every request must be worked and re-worked until it is “AI
 enough”. Failure to comply will either result in denial or, in many cases, a
 demand from a true believer to know why the extra work “can’t be done with AI”.
 Many companies have actively publicized that this is their new hiring policy –
 when a member of staff requests additional headcount, they must demonstrate
 that they have tried to use AI first. The part that’s being left out is that if
 you say you used AI and still need the help, you will be labelled “bad at AI”
 and potentially laid off.
 
 The net result of this is that almost every large organisation that I am aware
 of is no longer able to focus on anything important, unless they are one of the
 (very) few organisations where AI happens to address their highest priorities.
 They cannot buy sensible software, hire competent talent, communicate honestly
 with executives about the state of projects, or undertake any sort of sensible
 initiative.
 
 VI. Navigating AI Mania
 
     An emptiness falls through you
     As you realize what this means
     You're starting to feel what I feel
     Now you've seen what I've seen
 
     – So Sick, Domesticated Incels
 
 This is an unfortunate situation to be in, but it will pass eventually. I’ve
 learned a lot about the latent insanity that we have inculcated in our
 leadership strata, and unfortunately those traits will persist long past the
 current bubble, merely awaiting another similar reactivation trigger – and some
 organisations will stay captured until they have totally collapsed, in the way
 that not everyone has successfully moved away from the dreadful blockchain
 affair. That’s something to write about for another time.
 
 What I wanted to get to were some thoughts on surviving the immediate crisis,
 either by directly making systemic improvements or by holding onto your sanity.
 I’ll start with the “making improvements” part, because that’s the situation I
 find myself in the most frequently.
 
 When You Have Another Objective
 
 We’re going to do a lot of sucking it up and smiling here. This section assumes
 that you are trying to achieve some goal that isn't repairing the
 organisation's manic stance, but either trying to course-correct a specific
 project (and possibly risk getting fired as either a leader or consultant) or
 achieve some totally unrelated goal.
 
  1. Where possible, when raising issues, do not have conversations about the
     state of AI projects in group settings, as this creates a dynamic where
     each individual member of the group is worried about outing themselves in
     front of their peers. Arrange for one-on-one settings. Make it clear that
     you are willing to countenance that the current AI environment is frothy,
     and that you will keep opinions unidentifiable when raising them elsewhere.
     Be extremely aware that the most outspoken people can be identified by
     their peers, so take care to avoid exposing your sources by, e.g. direct
     quotes. In the event that only a small minority (say, one person in a group
     of six people) is willing to speak out, it might be worth giving up and
     moving on to a patient that has better chances.
  2. For ongoing projects, an effective trick that I believe I picked up from
     Secrets of Consulting is the anonymous poll, where you can ask individuals
     to rate their opinion of an AI project’s success chances on a scale of 1 to
     10. The typical split I have observed is half of those involved rating the
     project at a 3/10 and others at around an 8/10 – a clear bimodal split on a
     project that was already three years late. Bringing this data to a CEO can
     be an effective method of pointing out that some information is clearly
     being hidden from them on the state of the project.
  3. Always involve people on the ground. The only source of data on whether
     projects are succeeding or the investment is going anywhere are the people
     that use it for their day-to-day activity. Care must be taken to bring them
     into the environment where they are treated with respect (all sufficiently
     large companies have people that view subordinates as
     not-quite-real-people). It is not uncommon to uncover worldview-shaking
     information in short order – with one client, we uncovered that staff were
     totally unaware they had been given licenses for AI tooling, which cast
     into doubt all productivity claims.
  4. Do not question the broadest claims about AI. I cannot emphasize this
     enough. If someone says “AI is changing everything”, just let it pass if
     your goal is to fix an object-level problem rather than challenge the
     reality at the institution. The challenge can only come after you have
     gained the trust of the most senior person involved. Trust is gained over a
     meal in private where you assuage their anxieties, not by embarrassing them
     in front of peers.
  5. Remember that you do not know what statements have been emitted prior to
     entering a room. There will sometimes be people that have publicly
     committed to statements like “I am 100x more productive than I was last
     year”, and some may even wish they hadn’t said that but are too embarrassed
     to walk it back. In an untested room, common sense like “LLMs should not be
     allowed to deploy code without human review” can kill your chances to make
     an impact before you’ve even started.
  6. My practice requires me to maintain an honest relationship with my clients
     or the whole thing falls apart, so I can’t do this – but honestly, if you
     work in the fire service and need money to stop a puppy from catching fire,
     just lie. It’s fine. History will forgive you. Add a $10,000 AI chatbot to
     your project, exclusively discuss that part in meetings, whatever. Save
     that puppy.
 
 When You're Just Trying To Survive
 
 This is for people that are just waiting for the bubble to burst and trying not
 to go nuts.
 
  1. I have bad news – accept that you are probably not going to meaningfully
     push back on any of this. This is not a feature of AI, it’s a feature of
     dysfunctional companies.
  2. If you feel like you’re going absolutely nuts, consider switching over to
     contracting. I’ve advocated for contracting many times over full-time
     employment, but you’ll get paid a lot more and be left out of most internal
     politics. Also when you run into a really intolerable situation, you’ll
     know that you’ve got a fixed end-date.
  3. I do my best to limit my uptake of AI-related news, as it is pretty
     crazy-making and unproductive to consume. I no longer visit Hackernews,
     Reddit, or really anywhere where I am going to be drip-fed nonsense, though
     I allow myself exceptions for very funny things like [16]Apple suing OpenAI
     over alleged corporate espionage. Consume exactly the amount you need to
     feel like you aren’t going insane, then stop. Ditto for complaining with
     friends – and tell them that’s why you’re talking about it, which buys a
     lot of tolerance.
  4. When someone tells me they are using AI for something when they really
     shouldn’t be, I smile and nod as long as they are unlikely to get
     themselves killed. Even family. Especially family.
  5. When someone asks me for my opinion of AI as a programmer, I recommend
     saying “Oh, that stuff is pretty overblown” and then changing the topic,
     unless they are in a position where their opinion might influence something
     important. Non-programmers need this guidance the most.
  6. If you’re being asked to review huge volumes of terrible AI code, just
     assume that the organisation is going to burn you out and fire you. You
     will not convince the person drowning you in 2000 line PRs to stop. Start
     looking for a new job as if you have already been fired. I have seen this
     happen many times now, and it always plays out the same way – do the job
     search while you have energy. Don’t worry if your speed drops or management
     gets annoyed at you. There is no way to avoid that, you can simply choose
     whether it happens now because of your job search, or later because you are
     too depressed to work anymore.
  7. If your manager is responding to you with clearly AI-generated text, use AI
     to respond to save your sanity and then look for a new job. Many people
     assume they will get in trouble for being that obviously rude. You will
     not, this particular behavior is exhibited only by true believers, and they
     actually like that you’ve clearly not bothered to engage with them. I know,
     it’s fucking wild.
  8. If you’re being asked to max out on token usage, look for a new j – okay
     look, you get it, right? Go find a job that isn’t going to wrench reality
     from your tenuous grasp. They do exist, largely at companies so small that
     they don’t turn up on job platforms. It might take months to find one, so
     start now.
 
 Fight the good fight, and don’t let the bastards grind you down. Godspeed.
 
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 
  1. Also, and this is 100% true, Matt Mullenweg once asked me for coffee
     because he read the [17]AI piledrive essay, and in context probably enjoyed
     it, but had to cancel because he hadn’t realized he had a flight later the
     same day. I am willing to pay a competent witch to hex him for this
     slight. [18]↩
 
  2. We have rejected all AI implementation work. It is absolutely a gigantic
     bubble and we have minimized our exposure to it – every single one of our
     current contracts would be totally unaffected by OpenAI collapsing, save
     for perhaps some second-order effects such a recession causing a client to
     become unable to pay us. And there’s nothing we can do to insulate
     ourselves from that anyway. [19]↩
 
  3. One of the most valuable rules I’ve heard, from Gerry Weinberg, is that
     consulting is influencing people at their request. Unless someone has
     indicated that they want us to stick my nose in, usually by explicitly
     saying they want guidance on general data strategy, we just let the
     projects fail in peace. You can barely recognize me, I’m so calm these
     days. [20]↩
 
  4. We have since kissed and made up in private, though I don’t think we’ve
     budged at all on the core points of our viewpoints. I maintain that Thomas
     is [21]a very talented writer with a lot of good advice who just happened
     to blow it massively that one time because he takes Hackernews commenters
     too seriously. We all have our weaknesses. Mine is people telling me that
     “Scrum is good if you do it right”. [22]↩
 
  5. This is always baffling to me as a matter of being a responsible adult. If
     I was somehow CEO at a hospital or civil engineering firm, I would not for
     a second think it’s my place to start mandating specific procedures or
     building techniques without explicit agreement from the professionals on
     staff – how fucking clueless are the non-technicians who have attended a
     few talks and are now making mandates about how their extremely expensive
     professionals are doing their jobs? [23]↩
 
  6. If you’re an executive, board member, or anyone in charge of an “AI
     project” that feels trapped, I would love to hear from you. I will file the
     serial numbers off any stories very carefully, as I’ve done here and in
     every other article. [24]↩
 
  7. This sounds very fancy, but I think it was secretly one of those compulsory
     professional development things and half the audience were just like,
     making dinner. Truly, HR and professional bodies make victims of us all. 
     [25]↩
 
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 References:
 
 [1] https://ludic.mataroa.blog/
 [2] https://hermit-tech.com/blog/ai-mania-is-eviscerating-global-decisionmaking
 [3] https://x.com/mitchellh/status/2055380239711457578?lang=en
 [4] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fn:1
 [5] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fn:2
 [6] https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/
 [7] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fn:3
 [8] https://ludic.mataroa.blog/blog/contra-ptaceks-terrible-article-on-ai/
 [9] https://fly.io/blog/youre-all-nuts/
 [10] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fn:4
 [11] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fn:5
 [12] https://ludic.mataroa.blog/blog/the-worlds-left-to-conquer/
 [13] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fn:6
 [14] https://en.wikipedia.org/wiki/Prisoner%27s_dilemma#Real-life_examples
 [15] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fn:7
 [16] https://www.theguardian.com/technology/2026/jul/10/apple-sues-openai-trade-secrets
 [17] https://ludic.mataroa.blog/blog/i-will-fucking-piledrive-you-if-you-mention-ai-again/
 [18] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fnref:1
 [19] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fnref:2
 [20] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fnref:3
 [21] https://sockpuppet.org/blog/2025/02/09/fixing-illinois-foia/
 [22] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fnref:4
 [23] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fnref:5
 [24] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fnref:6
 [25] https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/#fnref:7
 [26] https://akols.com/previous?id=ludic
 [27] https://akols.com/
 [28] https://akols.com/next?id=ludic
 [29] https://ludic.mataroa.blog/rss/
 [30] https://ludic.mataroa.blog/newsletter/
 [31] https://mataroa.blog/