These thoughts are gathered from the before, during, and after of a workshop I led as part of a data visualization conference. Big thanks to the Bar Chart Club Conference, led by Erin Waldron, for hosting the original workshop and helping to make it a success.

Introduction

In March I led a three-hour workshop called “Adding AI to Your Data Visualization Workflow.” A more accurate title might have been “Practical Tips for Thoughtful Engagement with AI and How to Resist the Creation of Meaningless Slop.”

We’re now on year four of large-scale public access to large language models (LLMs). The rosiest corporate-backed shine is definitely starting to wear off of generative AI (RIP tokenmaxxing, for example) as questions about financial cost and ROI become more urgent. While commonsense financial reservations are finally gaining more traction, generative AI is likely here to stay, at least in some capacity. 

Whether LLMs stick around or not, evaluating them through the lens of data visualization has changed the way I interact daily with these and other tech products.

Over the last four years, I’ve worked as a curriculum developer for Codecademy (now owned by Skillsoft), designing online interactive content in data visualization and analysis. Like many people in tech or tech-adjacent roles, I’ve been told multiple times to use AI whenever I can. Looking at AI through this data visualization lens is helpful and fairly comprehensive, because it requires attention to text, numbers, images, and code. Data visualization was my AI testing ground, and forced adoption by my employer kept me at it when on a personal level I would have said no again and again. 

The two major challenges that I ran into repeatedly are these:

• Generative language models will always have the potential to hallucinate. They regularly return untrue statements, and this problem is inherent to how they function.
• The LLMs most of us interact with are controlled by companies. The built-in sycophancy of chatbots to which you are, first and foremost, a customer is antithetical to transparency. We require transparency for data work.

If we choose to (or must) use the technology for data visualization work, how do we mitigate these harms?

There are a lot of things we can do. My solutions focus mainly on the ways that we think and talk about LLMs and how those translate into critical use. The solutions I propose here are questions for critical evaluation at multiple stages of projects (e.g., before prompting or afterward while evaluating a result). They fall into three major categories for thoughtful, effective use of AI:

• Accuracy: Is it true and verified?
• Security: Is my data safe and private?
• Sovereignty: Am I using the tool the way I want to?

While digital sovereignty can refer to laws and (inter)national-level regulations, in this case I’m talking about the choices we make about the tech we engage with. Where can we exercise control and self-determination and use our own judgments and values to guide our actions? While that’s often not part of the conversation because it’s the hardest one to quantify and probably takes the most introspection and critical thought, I think it’s maybe the most important as we consider what it will look like to exist alongside AI tools in the future. They’re part of the future, but they’re not the only part of it. 

So what are the questions?

Accuracy:

• Am I asking the AI to scrape web data and give it back to me in some capacity?
• Am I working within a defined universe (I’ll include the data and an example) or a more open universe? (I’m asking for something, but I’m not really sure what I want.)
• Have I set up boundaries against sycophancy to improve my ability to interpret the result?
• Can I verify the results? If so, did I verify the results?
• Do I know enough about this topic to be confident that the answers make sense? 
• One step further: Do I know enough about this topic to be confident that the answers are smart?

Security:

• Am I working with private data? (Mine or someone else’s.)
• Am I working with unpublished intellectual property? (Mine or someone else’s.)

Sovereignty:

• Am I working on a part of the process I actually love to do?
• In getting these answers via AI, will I lose an opportunity I value to find the answers on my own?
• Is my question or idea worth the resources I’ll be using to explore it this way? (This question deserves its own article. Environmental degradation in service of AI tools is reason enough on its own to be a conscientious objector.)
• Will I be comfortable disclosing that I used AI for this part?
• Does it add value to my project? (Especially with text.)
• Is it up to my standards?
• Am I erasing my own voice or opinions by using AI for this?

I arrived at these questions through grappling with those two challenges I mentioned earlier: hallucination and lack of transparency. Read on for lots more context.

Part 1 of the problem: Generative language models will always hallucinate

I’ll start with what is likely a familiar exchange, from a current Claude model in March 2026. I asked for compiled data from a Wikipedia table. This is a task I do fairly often for simple, public datasets that are regularly scrutinized for accuracy, so I wanted to see if Claude could do it instead. It worked for a bit and then returned an impressively well-formatted artifact, a spreadsheet with color coding and helpful filters applied.

Claude compiling a Wikipedia table of Maine power stations into a single spreadsheet, March 2026. Screenshot by Eva Sibinga.

I noticed that longitude and latitude had been stripped from the output, so I asked why they had been dropped. Claude replied that the web fetch had likely not rendered the data correctly, so it dropped the affected columns and didn’t flag the omission. Okay, not a huge deal. 

I dug a little deeper into the table and realized that the data was completely wrong. Names of power stations had been changed slightly, but most importantly, the key metric I wanted (power in megawatts) was incorrect more often than it was right. I asked Claude to give me some insight into what had happened.

Claude explaining why the compiled data was wrong, March 2026. Screenshot by Eva Sibinga.

It had failed to fetch my data and made up the results from its training data, then presented the table as a finished artifact rather than flagging that there was any issue. It also literally said “the web fetch gave me enough info” before making up the data.

Claude is also capable of doing this task correctly, which makes the failures much harder to catch. I’m not sure why the web fetch failed in this case, but I asked Claude to repeat the same task in the exact same language (in a different chat in the same account and on a different account) and it was able to give me a table with correct data. 

The issue of inconsistency means the tool is impossible to use efficiently. It appears to fail randomly, meaning it can never be 100% trusted. If we have to check every single output for something as basic as whether the data we already had is still correct when it comes back to us, then we’re wasting energy and time on mental babysitting instead of using that power for other tasks, including generating the artifact ourselves. (I’ll return to this idea later, in a discussion of prototyping.)

Despite nonstop hype over the last few years, despite C-suite missives to shoehorn AI in wherever it can fit, and despite AI models’ abilities to complete increasingly complex tasks, even current LLMs continue to fail at basic tasks, whether we notice or not.

What causes hallucinations? (LLM history and technology in five minutes)

To critically evaluate LLMs, we should have a basic understanding of how they work. And some history about chatbots is helpful as well. Standing on the shoulders of giants—that is, paraphrasing the work of my colleague Dr. Nitya Mandyam—I’ll tell you about ELIZA, the first chatbot, developed at MIT in the mid-1960s by Dr. Joseph Weizenbaum. 

ELIZA was a rudimentary chatbot built on simple pattern-matching rules, but even so, its conversation partners sometimes experienced an unexpectedly strong emotional connection to the bot. From “ELIZA” on Wikipedia:

Weizenbaum first implemented ELIZA in his own SLIP list-processing language, where, depending upon the initial entries by the user, the illusion of human intelligence could appear, or be dispelled through several interchanges. Some of ELIZA’s responses were so convincing that Weizenbaum and several others have anecdotes of users becoming emotionally attached to the program, occasionally forgetting that they were conversing with a computer. Weizenbaum’s own secretary reportedly asked Weizenbaum to leave the room so that she and ELIZA could have a real conversation. Weizenbaum was surprised by this, later writing: “I had not realized… that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.”

The key idea that ELIZA demonstrates is that humans are naturally drawn to language-based robots. It’s very easy for us to humanize them, even when they are rudimentary. 

Today’s chatbots have evolved from rule-based systems like ELIZA to count-based models, and then to semantic ones. The foundation of these language models is natural language processing (NLP), which allows us to quantify language into usefully sized bits of information and then do math with those bits. 

One of the breakthroughs in the math of semantic, and later contextual, models is that they use vectors in abstract space, which allows us to map semantic meaning to words. To a count-based language model, “lead” is a token that can co-occur with “pipe” or “president,” among other words. To a contextual model, “lead” as in “pipe” is a completely different vector from “lead” as in “president,” because the words are quantified based on their contexts, not their characters. 

A key aspect of this difference is that it moves us from deterministic models, where outputs can only be sequences that already existed in the training data (anything else has a probability of zero), to probabilistic models, where outputs that are absent from the training data can occur because they have a nonzero probability. 

We can generate text that wasn’t in the training data. This is why it’s called “generative AI.” While this breakthrough in semantic language modeling is extremely powerful, it’s also the Achilles’ heel of the model. We always have the potential to generate text that is not in the training data, which might also mean that it’s untrue or impossible. 

This helps to explain both how the models are so powerful and can write such durable, readable text outputs, and how they are so unreliable and continue to hallucinate. There’s way more to the technical side, but the core idea here is that an LLM’s generative capability is the inexorable partner of its hallucination problem.

Part 2 of the problem: lack of transparency

Remember that Claude example? The issue of how Claude presented its work is another core problem. It shows us that the perception of a completed task is a more important output than an accurately completed task. 

LLMs are not just technologies. They are also products. LLM products such as ChatGPT or Claude are owned by companies—OpenAI and Anthropic, respectively. Their first goal is to keep you using their platform and convert you to a paid customer. Any other stated goal will always be secondary.

We know, from studying ELIZA, that humans are naturally drawn to become attached and form emotional connections with language-based robots. We find them to be really compelling, and we are very willing to believe them, identify with them, and trust them. This, in addition to their actual use cases, makes them a very sellable product. 

And the companies need to sell it. AI spending has far outpaced AI revenue in four successive years, and the gap between the two is growing. I asked Claude to make this graph comparing AI spending vs. revenue for OpenAI and Anthropic from 2022 to 2025, and felt just a bit of schadenfreude:

AI spending vs. revenue at OpenAI and Anthropic, 2022–2025. Source: reporting by CNBC, The New York Times, The Wall Street Journal, and Bloomberg. Charts generated by Claude, prompted by Eva Sibinga.

The companies are highly incentivized to sell and push their product. They need to capture market share and convert users to paid customers during this period of growth and relative investor confidence. This matters because the models are designed to create a feeling of convenience. Admitting that they have failed or cannot accomplish a task is not good for their bottom line and runs counter to their pie-in-the-sky marketing, so they don’t do it. 

LLMs are made by companies that value your perception of task completion more highly than they value the accuracy of task completion. The number-one goal is “task appears to be completed,” not “task is completed correctly.” This is why Claude uses the exact same cheerful tone when it returns garbage as when it returns something actually super useful.

That’s not even to mention its baseline complimentary, ingratiating tone. This sycophancy really, truly makes it harder to interpret LLM results correctly. We are not evolutionarily prepared for this type of social interaction, because if a human did this to us consistently, there would be social consequences. You wouldn’t keep going back to a colleague for help if they did the work wrong, cheerfully lied about it, and then gaslit you.

This lack of transparency is antithetical to how we do good data work.

Actual generative intelligence: collective knowledge created in the workshop

I won’t include a lot from the interactive part of the workshop, which was a group ideation session in which we spent about an hour answering questions individually and discussing them together. The goal was for us to reflect on the experiences we had already had with AI, think about aspects of our jobs that we did and did not want automated, ask technical and other questions, and explore the emotions and assumptions that we bring to AI conversations. 

When I opened up a space to be critical or skeptical, there was so much more of this energy than I anticipated. In this room, most of it came from people who had resisted the technology (some continued to; others had recently tried it out), but skepticism or negative feelings also came from people who used the technology daily and relied on it to get through a heavy workload. Some problems were reframed as issues with the workplace (devaluing of labor and expertise, understaffing and underfunding), not inherent issues with how AI technology works.

We recognized that there are successful AI use cases, especially in code-based tasks where the LLM output is code that can be better tested for accuracy and efficiency, and where the developer knows how to think programmatically and evaluate code output. (Although this, too, is rife with problems.) However, the reduction in nuance and idea building is a consistent blocker to implementations that meet our standards for non-AI work in data visualization.

So many new-to-me ideas came up—about the gender pay gap; about wondering if feeling proud of work an AI tool did was “allowed”; about the depths of automation fears from within a group of exceedingly competent business owners, analysts, and consultants. The workshop group happened to be a room full of women, and a feminist thread emerged in the conversation: questioning some of the ways we had seen people on LinkedIn (often but not always men) speak about AI, and talking about which ways did or did not resonate. I mean feminist in the Donna Haraway sense: that seeing everything from nowhere is a f—ed-up god trick, that all knowledge is situated, that disembodied “facts” can lead us further from useful truths (“Situated Knowledges,” 1988).

The common AI stance “get on board or get left behind” sat poorly with the group as a whole. It often lacks nuance and is blind to the skills that create exceptional work in data analysis and visualization. It’s not to say that AI tools can’t be helpful in data visualization. It’s that chasing them from a place of fear and inadequacy feels not only terrible but also incorrect.

We were collectively buoyed by this “AI therapy session,” as one participant called it. Space to openly acknowledge the significant failures of AI tools not only validated lots of experiences in the room, but it also made the genuine use cases for AI tools feel more rewarding to explore.

One more thought on what to do about it

So now we arrive at the challenges—hallucination, intentional lack of transparency—armed with more information. 

If we choose to (or must) use the technology for data visualization work, we can mitigate some of this harm with pointed questions. On accuracy: Is it true and verified? On security: Is my data safe and private? On sovereignty: Am I using the tool the way I want to?

I’ll leave you with one more solution and something else to read. Another solution we talked about in this workshop was the importance of prototyping. I cited Frank Elavsky’s excellent blog post from earlier this year, “On genAI: Was prototyping really a bottleneck?” in which he explores “what if the slow parts about prototyping are actually what make it worth doing?”

The key point here is that prototyping is where we test the intellectual rigor of an idea. I loved this graphic he included about the intellectual refinement and technical refinement of ideas. It makes it clear how incorporating AI early on in the idea stage can make shoddy ideas look sleek while really generating slop, and how the better opportunity for incorporating AI is the “zone of missing skills + resources” for ideas that have already proved themselves in a prototyping stage.

“The prototype slop-zone,” from “On genAI: Was prototyping really a bottleneck?” Illustration by Frank Elavsky, used with permission.

It’s hard to refine ideas with an AI tool. You have to be in the driver’s seat, because the tool is a reflection of what you ask for paired with a repackaged, statistically likely output of what others have already said on the topic. Elavsky puts it so well: “[P]eople tend to assume that the ideas they have in their head are really good, if they aren’t used to rigorously iterating on ideas.”

A partner who is incentivized to keep us on their platform isn’t one who will say “I just don’t like this direction” to a scrappy drawing in a notebook. Or one who will say “Oh wow, I’ve never thought about it this way before, but you’ve hit on something that’s pretty key.” But both of those kinds of feedback are actually helpful. The LLM partner will be whatever we tell it to be, but always in service of its corporate overlords. 

As Elavsky helps us to see, being thoughtful about when in the process we use AI helps us to develop robust ideas that are worthy of robust technical ends. 

And as this article attempts to lay out, questioning how we engage AI helps us introduce it critically into data visualization work, which demands high standards from visual, numeric, textual, and coding perspectives. 

The best data visualizations prove time and time again that good data analysis is human-centered, asks lots of questions, and makes genuine meaning out of numbers by transforming them into something we can see and contextualize. This part might be sped up or expanded by AI, but it cannot be automated. 

And hooray for that! This is the good part! This is the part that has drawn so many creative, critical, analytical thinkers to data visualization in the first place. Let’s use that same rigorous, flexible thinking when we engage with AI tools. Because if and how we choose to engage with AI in our data visualization workflows has implications for the tools themselves and for our outputs, certainly; but more importantly, if and how we engage with AI reflects how we honor our craft, our own brains, and our lives.

Researchers in China have created a textile from living mycelium that is self-cleaning, near self-repairing and can be coloured or made UV protective through "plug-and-play" add-ons of different fungi or yeast.

The breakthrough from the researchers at the Shenzhen Institutes of Advanced Technology is a type of engineered living material (ELM) – a material built off living organisms that stay active even after they're fabricated into their final form.

This distinguishes it from most contemporary uses of mycelium, which involve drying and effectively killing the fungus to produce a stable, non-growing material that has become a popular emerging alternative to plastic packaging and vinyl.

By working with living but dormant cordyceps militaris fungus instead, the researchers have been able to take advantage of its biological functions. The result is a material that is self-renewing and responsive to its environment, in ways that could one day transform architecture and clothing – as seen in a prototype dress created together with material innovation company Peelshere.

It can also be adapted by mixing in other fungi or yeast, lead researcher Ke Li and her team detail in a paper in the peer-reviewed journal Science Advances.

In it, they describe a "programmable fungal platform" where mycelium is treated like a modular system, with the sheet material forming a base structure and extra biological abilities, such as colour and UV resistance, becoming "plug-and-play" add-ons via other organisms.

This gets their textile closer to the self-repair, environmental responsiveness and controllable functionality that is the promise of engineered living materials, they argue.

"While synthetic biology has greatly expanded the functional capabilities of ELMs, a persistent challenge lies in integrating autonomous structural assembly with sustained biological activity at macroscopic scales," the scientists write.

"Achieving such integration is essential for practical applications, from adaptive textiles to architectural biomaterials, where mechanical robustness, spatial uniformity and scalable fabrication must converge with engineered biological function."

The ELM's self-renewing and semi-repairing functionality comes from the mycelium base structure. Following drying at 45 degrees, the material is not quite living and not quite dead, but instead in a "low-metabolic, dormant-like state", Li told Dezeen, meaning it is not actively growing.

However, new growth can be triggered by applying a nutrient solution of potato water, leading the dormant mycelium to germinate, send out new fungal filaments and renew the material's surface.

When this nutrient solution is applied over a hole, along with a small patch of fresh fungus, it triggers the living cells to grow across the gap, seamlessly repairing the surface without any adhesives or stitching. The material is also naturally self-cleaning, as it is hydrophobic.

The blue colour and UV resistance, meanwhile, come from brewer's yeast – also known as saccharomyces cerevisiae – and aspergillus niger fungus, respectively. These are mixed in with the cordyceps militaris at the beginning and simply co-cultured, avoiding the need for genetic engineering.

A prototype dress has been made out of the scientists' material by Berlin-based Peelshere, whose founder YouYang Song is a friend of Li's. Song developed the conceptual and aesthetic design of the dress, while her China-based colleague Ruochen Wang took care of the cutting and construction.

The dress features several versions of the material, including some co-cultured with brewer's yeast for the consistently self-pigmented blue shade in the body of the garment.

Li told Dezeen that the material is suitable for applications such as conceptual fashion, accessories, decorative textile surfaces, exhibition pieces and biodegradable packaging.

"Its distinctive surface texture, biological colouring, controlled repair and biodegradability may be particularly useful in applications where visual expression and a defined product lifetime are important," she said.

"Further improvements in durability, moisture resistance, safety and manufacturing consistency would be needed before it could be considered for routine clothing or permanent architectural use."

Peelsphere's main product is a plant-based and waterproof leather alternative made of fruit peels and algae.

Photography by Ke Li.

Generative AI isn’t just changing how we work. It’s may also be affecting how we think, create, collaborate, procrastinate, and occasionally lose the plot.

You may have heard of AI Mania and AI Psychosis. 

Here’s a field guide to more potential AI-induced mental habits, compulsions and behavioural quirks that may be emerging in the age of AI.

None are recognised medical conditions.

Many might feel uncomfortably familiar.

Did we miss any? Suggest one

Written and designed by David McCandless. Additional contributions from Nik Roope, Cal Newport, Hanna Piotrowska

Some examples of AI induced mental habits & compulsions

AI Psychosis
Total, unquestioned ‘buy in’ to the idea that AI is a must-have super-human replacement for anything human: creativity, cognition, coding, customer-relations, companionship, conversation – and common sense. 

NarcAIssism
Euphoric ego-inflation induced by prolonged interaction with overly agreeable AI chatbots. The sufferer develops an outsized certainty of their creative brilliance, personal insight and strategic acuity.

PolyLLMory
The inability to commit to a single AI model, leading to the insertion of all prompts into Claude, ChatGPT & Gemini simultaneously. 

Though there may be a ‘primary’, relationships with all three models remain technically ‘open’. 

FOFAB
Fear of Falling Behind. Ambient dread and background anxiety over the possibility that everyone else and their hairdresser are using AI more effectively than you.  And of becoming professionally obsolete by Tuesday.

Agentic Burgerflipping
Spending entire workdays supervising AI bots & agents rather than doing any actual skilled work. 

Today’s tasks: 1) evaluating outputs 2) re-prompting after errors and 3) typing “fix it” followed by the return key.

Claudependency
Irrespective of the task or issue – personal, professional, large, small – a <strong>Claudependent</strong> must ‘discuss’ it first with their Anthropic LLM, reporting back their decision with “My AI said…”

UpSkill Sisyphus
Constant efforts to learn different AI models, test new tools, and stay current generates a permanent cognitive churn. Often laundered as “adaptability” but actually an exhausting, perma-treadmill with no destination.

Cognitive Laxity
Atrophy of memory, reasoning and problem-solving muscles due to habitual outsourcing of mental effort to AI. 

Example: all universities graduates from 2025 onwards

AI Burnout
Exhaustion caused by the endless steering, correcting and re-prompting of mediocre AI output. 

Did we miss any? Suggest one

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 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.

– 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 companies¹. 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 in², 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 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 straight³.

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 “Contra Ptacek’s Terrible Article On AI”, which was focused on the fact that many of Ptacek’s points in his own essay “My AI Skeptic Friends Are All Nuts” were internally inconsistent⁴. 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.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 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 control⁶. This is to say that we’re facing a 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 boards⁷ 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 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.

Just over seven months from completing phase one of this mega-project, Chinese engineers have finished the build and switched on the world's first underwater data center (UDC) powered by offshore wind turbines. What's more, it doesn't need freshwater and cuts land use by more than 90% compared with above-ground centers.

We reported on the big build in October 2025, when the first stage had been constructed. At the time, there was no projected timeline for it to become operational. The underwater infrastructure, off the coast of Shanghai in the Lin-hang Special Area, was officially switched on in late May, and it's far more impressive than it may sound on paper.

Data centers don’t need freshwater to function – but it remains the simplest cooling option, as it puts fewer demands on surrounding infrastructure, thanks to its lower levels of salts, minerals and biological impurities that can corrode pipes or reduce cooling efficiency over time. Unlike many inland facilities that still rely on freshwater, UDCs instead use the surrounding ocean as a heat sink, transferring this heat through sealed cooling systems.

This center, built by a subsidiary of China Communications Construction, uses a circulating copper-pipe heat exchange system that reportedly reduces electricity consumption by 22.8%. Offshore wind farms are also estimated to generate 95% of the electricity needed to run its 192 server racks across four levels, significantly reducing reliance on existing power infrastructure.

"For an undersea data center of the same scale, the electricity used for cooling would only account for about one-tenth of total power consumption," Tsinghua University Professor Li Zhen told China Daily. "If data centers of the same scale were placed underwater, even allowing extra margins, cooling consumption could fall to around 30-billion kW. That would save about 50 billion kWh of electricity each year."

According to state media, the center is currently operating at 2.3 MW – but has a planned capacity of 24 MW (enough to power 20,000 households). This "room to move" is essentially future-proofing the UDC's usefulness, as companies turn their attention from initial builds to longevity when it comes to hardware upgrades and compute capacity.

Nonetheless, while UDCs may reduce freshwater demands and land use, underwater computing is still a largely unknown at commercial scale. Questions remain around how these facilities will endure – and what the ecological effects of continuously releasing heat into local marine environments might be.

But considering tech companies are racing to put data centers in space to meet rising demand, real-world projects like China's UDC could serve as valuable test cases in the AI age, revealing whether moving computing infrastructure into new environments can offset existing land-based issues – or reveal entirely new ones.

Source: China Daily