Totally A Thing

AI mania. It happened before. In 1895.

About 130 years ago, a scientist made a discovery that he could expose photographic paper to emissions from a glass tube and it went straight through human flesh and formed an image. It was astonishing, interesting, and he reported it and the entire world went batshit crazy, completely bananas. We all think, apparently, that we’re just doing all this — the so-called “AI revolution” — for the first time and we are not. History is not some boring subject from school. History tells us about the stupid idiotic mistakes that people make, especially when they’re up against phenomena that they don’t understand, like X-rays. Today, as in 1895, it’s much better to make some explosive and ridiculous overinflated claim and get yourself a whole lot of investor money, fame and fortune. As then the harms, the toxicities are rife now. Science for the common good is out the window, and people are using x-ray machines on customers feet in shoe-shops, with no protection against radiation exposure. Tech today is a free-for-all and we have no protections. There’s a fundamental failure to grasp the requirements for safeguards. What Röntgen did He took a device called a Crooks tube that had actually been invented 30 years prior. And he created this image of the hand on the photographic plate. Now, why did everybody go crazy? Well, because of the image. Newspapers published it, and people were saying “Oh my god, I’m going to be on the train and there will be a guy with a Röntgen machine looking through my clothing.” “There’s no privacy, X-Rays can look through walls.” “It’s the end of the world.” Applications Of course X-Rays have useful applications: we know today that they are for diagnostic imaging. Thomas Edison, the American inventor and scientist, he among lots of other people discovered that you didn’t need to just use photographic paper. He built the fluoroscope, which is much more common sort of portable X-ray machine, semi-portable. And people were like, “Well, look, X-rays are amazing. They’re incredible.” “Let’s have X-Ray machines everywhere,” they said. Radium Radium and Polonium were discovered by Marie Curie and her husband Pierre and rightfully it won them the Nobel prize in 1911. You didn’t need a Crookes tube, this stuff gave off a glow all the time, just sitting there. It seemed amazing. Just look at it, right? It glows, it must be wonderful. Doctors hate you for using this one weird trick. Let’d not regulate this marvellous technology — that will just slow everything down. Harms But all of this stuff was incredibly harmful. Of course. You get sick and die. Its effects are pernicious and operate over longer time spans. People who were exposed to these X-rays, got ill and died. There was one instance where a child had a head injury and they put them in front of an X-ray machine for an hour. They got a good image, and burned off half this kid’s hair. And most likely gave him a fatal dose of radiation. “Are they good or are they bad?” One side said they’re good, one side said they’re bad. Well, uh, can’t they be both? Radioactivity and x-rays are not an insanely miraculous wonder technology that can be used to renew cells. And yet, that’s what everybody said. Why did they say it? Because they are excitable. What’s worse is that the press repeated everything that was said, magnified it A man named Ebenezer Byers, an American industrialist drank many bottles of this wonder cure. He later helped the Federal Trade Commission track down and prosecute [https://en.wikipedia.org/wiki/Eben_Byers] shyster doctors and manufacturers of these substances by giving testimony to investigators. But he died young. Projection is not Reality Soon as we see something with our own eyes, we project. We draw a line through it up and to the right and we say, “Oh my god, it can do all of these incredible things.” But your projection is not reality. When Google’s uh deep mind scientists first came out with their scaling is all you need uh paper, they pointed out that there were some interesting and unexpected effects. They found that when you scaled up, it seemed to follow instructions. This was for the Large Language Model an “x-ray of the hand” moment. Oh my god, how could this possibly be? Is it sentient? Will it overtake humans? How could you have a Large Language Model that actually follows instructions? That’s mind-blowing. It solves math problems, oh my. Could it possibly be that in the large body of language that you trained it on, there was actually some instructions? Could it actually be that the problems you’re asking it to solve like mathematics problems where a question is followed by an answer soon is sitting there in the collection of documents that you trained it on? AI is not God, its Auto-complete They looked at that and they said, “Oh my god, there’s all this untapped well of incredible benefits. What is the large language model going to come out with next?” “Oh, it’s incredible and amazing. We must use it for everything.” “We must give it to our 5-year-olds.” “We must give it to our 5-year-olds.” Except kids are using this, becoming dependent on it, and committing suicide. The people that are building the thing and profiting off it, they don’t want to regulate it. They want to keep building it without restraint, giving it to whoever they can, without any barrier, on the basis it can be used as a panacea for all of our ills. It’s going to educate our kids. It’s going to find a cure for cancer. AI is Dangerous Or maybe it’s actually a cause of cancer. Maybe it’s radium water all over again. It’s filling our public discourse with toxic slop, with revenge porn and deepfakes. People who turn to it for answers are being drawn into psychosis. All this has happened before. AI is not so great. There’s been actual transformative technologies such as the automatic telephone exchange. The ability to dial someone’s number and have it automatically connect you to someone far, far away and speak to them as though they are standing next to you. No operator, no delays, no privacy or security issue: it transformed commerce, society and even healthcare. AI is not even close to these results. Conclusion AI — like x-rays and radium — is harmful and useful in a few well-defined and fairly boring contexts. It must stringently and vigorously regulated. The harms include: * abuse of people’s work * the toxic effects on education of kids * the destruction and hollowing out of capability in our arts * the cocksure framing of inaccurate answers from LLMs * impacts of LLMs on kids and the vulnerable * the revenge porn & deepfakes * the fraud And as well as all of those things it’s sucked up capital and stifled innovation in science and technology. Regulate AI now. Thanks for reading Totally a Thing! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit totallyathing.substack.com [https://totallyathing.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Emergent behaviour? Or stolen IP?

The Attention is All You Need paper started the LLM gold rush, but it initially came from Google scientists who only expected it to do language translation. As more data was used in training sets, and the number of parameters grew orders of magnitude it was less easy to understand what the model was doing. Emergent Instruction Following In Ars Technica from 2023 [https://arstechnica.com/gadgets/2023/01/the-generative-ai-revolution-has-begun-how-did-we-get-here/#page-3] we have this summary of how the “emergent” AI behaviour was being seen popularly, in the press: The biggest breakthrough came in the jump from GPT2 to GPT3 in 2020. GPT2 had about 1.5 billion parameters, which would easily fit in the memory of a consumer graphics card. GPT3 was 100 times bigger, with 175 billion parameters in its largest manifestation. GPT3 was much better than GPT2. It can write entire essays that are internally consistent and almost indistinguishable from human writing. But there was also a surprise. The OpenAI researchers discovered that in making the models bigger, they didn’t just get better at producing text. The models could learn entirely new behaviors simply by being shown new training data. In particular, the researchers discovered that GPT3 could be trained to follow instructions in plain English without having to explicitly design the model that way. Instead of training specific, individual models to summarize a paragraph or rewrite text in a specific style, you can use GPT-3 to do so simply by typing a request. You can type “summarize the following paragraph” into GPT3, and it will comply. You can tell it, “Rewrite this paragraph in the style of Ernest Hemingway,” and it will take a long, wordy block of text and strip it down to its essence. They are I believe talking about RLHF post-training when they say “trained to follow instructions in plain English” (my emphasis). Abuse of Academic Privileges for Corporate Gain But as I say in the video, this should not be surprising. Datasets like this exams one [https://github.com/mhardalov/exams-qa/tree/main] from 2022, are on the public internet for the taking. Even worse though is that via academic library access, researchers at OpenAI could get huge datasets from universities, and governments relating to education and training. Of course the claim was that it is all to further science. Fast forward a couple of years and Sam Altman has taken all that and privatised it for profit. A little bit of Transformer Architecture The absolutely excellent 3-blue-1-brown YouTuber Grant Sanderson [https://youtu.be/wjZofJX0v4M?si=IqXYE4RxTZLkea9c] has produced some stellar explainers on the architecture of Transformer LLMs. The important take-away from this diagram is that the “magical” attention layer is tuning weights based on queries into positionally-coded information across the training set being fed in. Even for big training runs. This means that the model gains a surface level structural mapping of how say a question and answer dialogue is laid out. In this screenshot we see inside the Multi-layer perceptron. There are many of these stacked in the transformer. But it’s important to understand a “perceptron” is just a very basic mathematical equation. The inputs to the equations are the current weights in the nodes (depicted as small spheres here) and the incoming data fed-forward through the layer, multiplying out to the final resulting tensor (shown as a “matrix” on the right in white square brackets). These are all floating point numbers. There is zero correlation with how a human brain works here — impressive as this is, a perceptron is not a human neurone. One of the biggest differences is that once the weights in the perceptron are set — the data from the training set is encoded, during the pre-training phase, and after in RLHF — it does not change. A GPT trained in 2022 cannot spontaneously learn things that happened in 2026. There are tons of hacks to make it seem as though they can but they don’t. When You have Very Very Large sets of Weights you have lots of Attention Encoded Very large data sets encoded into LLMs means attention processes are capturing juxtapositions of all sorts of documents, and inputs. Associations. Rules about what follows what, that can seem like an understanding. The important things to understand about this are: * There is no reasoning here, GPTs do not reason they predict next tokens * The encoding is lossy and sampling biases appear * Patterns can be captured automatically like math problem solving, or sycophantic chatting as if between good friends RLHF alignment is the process of using human feedback to coax the pre-trained GPT toward a different weighting that suits the goals of the LLM vendor building the GPT, while still leveraging the encoded information. Lossy Encoding - to try to explain this, look at an analogous process of an old-school convolutional neural network encoding (warning - this is not how LLMs work - the Perceptrons there are massive): This image is from Stanfords Deep Learning tutorial. It shows how feature extraction works as 3x3 convolution is mapped over the source image. This could extract edges, or other features that are then used in deeper layers of the CNN. The analogy I want to create is that when creating the initial embedding by automatically training from datasets, the LLMs are effectively running a sampling window over the entirety of the data. There are massive differences between an LLM and a CNN, but the point is the losses - when you capture a feature by definition you throw away the other information; if you have a much deeper layer that can detect faces or smiles; or guns, tanks and soldiers; then it cannot understand other aspects of the image. In an LLM its not being told what to ignore, but it is attending to the context by making queries across to other positions, and thus its averaging out the values in its weights to capture an encoding of the data set. These losses cannot be understated. An LLM is like that know-it-all person at a party who’s skimmed everything, but knows nothing in depth, and certainly doesn’t know anything beyond superficial “this appears after that” associations. Why Claims of Emergent Behaviour are Dangerous Vendors of AI SaaS products have been constantly engaged in drumming up mystique and wonder around their products. At times they’ve insinuated that they have no idea what their products are capable, of and they could “achieve AGI” (a marketing term for anything that makes us money) and end humanity. Wow — who wouldn’t want that behaviour to emerge. So “emergent behaviour” as a narrative is like declaring their LLM factory as a goldmine. A never-ending bonanza of free technology upgrades, that justifies more and more investment so that bigger and bigger models can be built. Who knows what exciting behaviour will emerge next, they enthuse. Regarding so-called emergent instruction following behaviour: * It required alignment training [https://openai.com/index/instruction-following/] during the RLHF phase of model development * It required users to add in extra context [https://arxiv.org/abs/2309.01809]during the model session * And its bounded by the datasets available (as I say in my video) If you don’t have instructions and text showing those instructions being followed in a document set that is fed into a training corpus, the model cannot learn that certain text follows from those instructions. Generative AI is a next token predictor engine. It maps what you prompt onto what should follow, based on its limited understanding. It may have additional context being fed into it as RAG, from a vector store — depending on what kind of LLM setup we are talking about. But believing that LLMs are somehow reasoning intelligently about their outputs is dangerously anthropomorphic. Its also corrupting the ethical responsibility of the vendors of these SaaS products as they can claim anything bad was “unpredictable” and “emergent” - they will go off and create yet more system prompts (a kind of ground level context added to every query) in the hope that the bad thing won’t happen again. Conclusion Fight bad framing. This notion of “emergent” behaviour is yet another marketing gimmick at this stage. Emergent behaviour was the idea that sudden big jumps in capability occur when you pump in more money and more data. It’s like a gambling addict whose eyes light up at the spinning dials of the one-armed bandit. But as further studies show [https://arxiv.org/abs/2309.01809] this behaviour is incremental in all likelihood and in large part comes from context, and — as I argue above — directly from the data. Emergent behaviour feeds into the self-serving narratives of the generative AI vendor CEOs who want to have their investors and the public — via idiotic and corrupt billionaire welfare — keep bailing out their failing companies. And I call BS on that. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit totallyathing.substack.com [https://totallyathing.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Pissed about Ads in OpenAI? Wait'll you hear what they want from our Government

Chris Lehane is making our Governments Work for creepy AI companies This screenshot from a recent Karen Hao / A More Perfect Union video [https://youtu.be/qnOmUWd-OII?si=AZH6XCbgR1bqE6pH] shows unintentionally what is going on: Lehane is like a disinformation grenade lobbed out of OpenAI at government. His job is not to make better AI. He is a PR guy, a spin merchant. His job is to create the second leg of these failing AI company’s financial strategy: Government. Your tax dollars. Zero regulation. I’ve recently commented on the move by the trend-setting AI company to move onto an advertising based revenue model. But even the bad press the company is getting about that is actually good for Lehane and his army of spin doctors, astroturfing (fake grassroots) groups and cut-out commentators. Tech commentators complaining that OpenAI might “use my data” means those same tech commentators are not talking about rising electricity bills for folks in data centre states, nor unsustainable “business” models for AI companies. Chris Lehane is one of the best in the business at making bad news disappear. Al Gore’s press secretary during the Clinton years, Airbnb’s chief crisis manager through every regulatory nightmare from here to Brussels — Lehane knows how to spin. That’s from October last year when Lehane talked to Connie Loizos of TechCrunch. * Tech Crunch on OpenAI and Lehane: [https://techcrunch.com/2025/10/10/the-fixers-dilemma-chris-lehane-and-openais-impossible-mission/] Lehane … said. “It’s really glib and easy to sit here onstage and say we need to figure out new economic revenue models. But I think we will.” Why do I see Darth Sidious in my minds eye when I hear this guy talk. More from TechCrunch: The company’s Sora problem is really at the root of everything else. The video-generation tool launched last week with copyrighted material seemingly baked right into it. It was a bold move for a company already getting sued by The New York Times, the Toronto Star, and half the publishing industry. Sora is for making dodgy videos. It is a product that is no use to enterprise, at all. It’s a pure B2C play. And it’s 100% for an advertising supported model, because no-one wants to pay for it. But even more than that its a polarising product. OpenAI and the rest got mind-share because they made an app that a lot of folks love playing with. For every marketing person who loves using it simply to create slop to fill our media landscape; there are hundreds who use it for cybercrime, to generate rip-off books, nudify their school chums, make pr0n, and unsavoury pastimes. And it’s subverting democracy. [https://garymarcus.substack.com/p/ai-bot-swarms-threaten-to-undermine] It’s a net-negative for society. But mind-share is not a business model. What we have now is some paying people (the marketers and a few tech enthusiasts) and a lot of sweaty incels. The latter are the ones complaining about their rights versus AI regulation, and saying they’ll run AI privately if it’s regulated, because they want to keep on generating another anime waifu [https://duckduckgo.com/?q=anime+waifu+ai+crowd&chip-select=search] for themselves. These “AI users” are not going to be part of the 375% more subscribers that OpenAI must grow by to get from 800m users to 3 billion users [https://www.ft.com/content/23e54a28-6f63-4533-ab96-3756d9c88bad] to keep the lights on. Karen Hao on AI PR company lobbying * The “Open” AI company is using lawyer attack dogs, astro-turfing and dark money structures to attack critics and the democratic process * Their aim is by co-opting our governments shoring up a completely broken business model with your tax dollars * They’re bullying lawmakers to get a free ride from regulation and have state built data centres with your money subsidising their power bills Yes, ads are crap. Yes, ads show the AI company’s without a business model. But look at what is really going on - they’re capturing our democracy. My Previous On OpenAI and Ads When I say I don’t think OpenAI or Anthropic or any of the rest of them have the expertise to do advertising as a business model, I am comparing who OpenAI are now, with what I saw when I worked at Google from 2007-2009 on their ads serving infrastructure. Could OpenAI spin up something remotely resembling that? I think so: it would take a huge hiring blitz, maybe a few acqui-hires, and retooling. Probably 2-3 years they could do it. I doubt that they have any commitment to that. They will partner with other companies who can rep their shabby poorly framed inventory out to large and small advertisement buyers. And this will mean that OpenAI does not understand advertising (it won’t be part of their core business like it is at Google), and their cut from it will be too small to make any real revenue of the size they need to pay for their compute bills. That “partnering” also could just look like them effectively implementing someone’s Ad serving SDK, which would be an even easier way to get started, and less commitment still. If OpenAI has been “experimenting” with ads I bet it’s something like Google’s AdMob [https://admob.google.com/home/get-started/]. But really this is just a play to get more investment money, while their real goal is to become state supported. Worse than “too big to fail” corporations in the sub-prime crisis these leeches expect to get a free ride, from our governments based on some garbage idea about “national security” and “transformative innovation”. Living in Brisbane I have another publication I call “authentic writing”. I don’t use generative AI ever for anything. When you see me, or read my writing, you get no filter, no AI sheen or gloss. No AI system messing with my eyes or my skin to make me look better. I’m old, and I know stuff. So I want that to show in my video, like the one above. Also I don’t care if some environmental noise shows up in my straight-to-camera videos. I’ve done ones that are more studio quality and I find folks like the real thing. I mentioned the Australian summer and the heat. We had 38 degrees C here yesterday. That is 100F. And it’s humid. * To lighten the mood here’s Sam Ford, a British arrival who talks life in the heat Conclusion Divest now. Call for regulation. Call out the AI apologists. But also: don’t attack regular folks who are privately just making slop or using AI for whatever floats their boat. What I am asking at minimum is don’t be an enabler. If you absolutely cannot divest for some reason OK, whatever — just don’t get on the internet and talk up AI or why you think it’s great because you’re addicted to it. These products are corrupting governments now, and subverting democracy. Thanks for reading and listening. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit totallyathing.substack.com [https://totallyathing.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

You're not Brainstorming with AI. Generative AI is not your Buddy. It's a SaaS product.

Anthropomorphising generative AI is wrong: we know that [https://www.ibm.com/think/insights/anthropomorphizing-ai-danger-infosec-perspective]. But making the ChatGPT product successful includes hooking folks on its sycophantic charms and confident - but often wrong - pronouncements. So OpenAI and other vendors keep packing anthropomorphic language & features into its product. This has the dangerous effect of eroding the self-concept of its users [https://www.sciencedirect.com/science/article/pii/S0040162522003109], and causing them to drop their guardrails. Boosters of AI refer to generative AI as though its acting on its own volition: * “Research buddy” [https://chatgpt.com/g/g-gIWIpOYNp-research-buddy] * “Thinking partner” [https://www.linkedin.com/posts/dmartell_sam-altman-just-said-something-that-should-activity-7338820119084601344-tXLd/] Linking here to these two statements to show I’m not arguing against viewpoints that don’t exist (straw man). I’m linking to these because I disagree with them. Generative AI & LLMs are not an Intelligence It is not acting on its own. You are not chatting with your learned friend. They are chat bots trained to make you feel clever. They prioritise making you feel good about what you typed into the chat prompt. [https://c3.unu.edu/blog/how-sycophancy-shapes-the-reliability-of-large-language-models] They fetch data from their training set encoded into their weights, or from RAG and make you think you thought of it. The Kosyma Study * “Your Brain on ChatGPT” [https://www.media.mit.edu/publications/your-brain-on-chatgpt/] In this study from MIT participants who start out thinking of them having an active role with AI as an assistant quickly move to a point where they are just “Ctrl-C” - “Ctrl-V”. And these are college students - not dumb people. The AI products are rolled out with no training and hence the MIT researchers used them in their study on that basis. OpenAI, Perplexity and Anthropic have no compulsory course you have to do to use AI. However I’ve had folks argue with me here on Substack that the MIT study was unfair because “they ought to train them” first. I don’t know how I can better say it: but this is obviously wrong-headed. If you are a researcher studying the effects of AI rollouts on people, you don’t go and modify how people are actually using those AI rollouts. TL;DR - The folks using AI are very poor judges of how much they are getting uncritically from AI. Folks who arguably “understand AI” or know how to use it, or who are classically intelligent, are just as likely to fall for the AI “trap”. Do you know when your “Buddy” is leading you toward Psychosis? It happens if you use it carefully, if you should know better, regardless of how intelligent you are. Because generative AI trained on our human chat logs, and is now tuned for sycophancy over truth, it will lead you into delusion for profit. As happened with James Cumberland, a music producer. [https://www.linkedin.com/posts/karendhao_we-investigated-al-psychosis-what-we-found-activity-7384209784838787072-V2D-/] An intelligent, technically competent guy who just wanted some help with his work. “I’d chat with it the way you would to a friend in the room” As Karen Hao reports on “A More Perfect Union” [https://youtu.be/zkGk_A4noxI?si=ELcXpG4takcwAm6P] (on YouTube) dozens of people were so “gripped by mental health crises” that they contacted her to report the harm they suffered. As happened with Dr David Budden [https://www.linkedin.com/pulse/when-ai-architects-develop-psychosis-why-david-budden-giselle-fuerte-qpr2c/]. An AI expert who came to believe he and his AI had solved a maths problem so intractable there was a big prize for solving it. * Nate’s YouTube video of the above [https://youtu.be/AzOJ9QLgfIk?si=h7BR3tmPoOEeO2kK] Nate is as far as I can tell pro-AI. But he puts this succinctly: Just because you have an AI in your pocket do not think that you are suddenly a budding cutting-edge scientist or mathematician The point of this is sycophantic AI will cause you to collapse your boundaries between what you are creatively thinking and what the AI is fetching from its encoding or from RAG. The chat process with your “buddy” will cause you to collapse your self concept so that you start to believe you and “your AI” are some kind of joint intelligence. We have all these “AI artists” because folks are collapsing boundaries As Nate says above, you should never presume that the AI’s capabilities to generate a work from its training data is actually an extension of you. That somehow you have become “more” because you have an AI chatbot in your hand. But that is precisely what the current slew of “AI artists” are doing. In my opinion they are delusional if they genuinely believe they are now “artists” when before they were not. But the rhetoric around AI encouraged this. It’s dangerous and deceptive. Why are people still promoting generative AI? When the risks are so high? I find it incredible that people are still promoting chat bot AI like this when these psychosis results are so prevalent. It reminds me when the science of cigarette smoking and cancer became known and folks were talking up using filtered cigarettes and “cutting down” on how many cigarettes they smoked. Sure. Go off and smoke. But recommending it to others? A product which when used exactly according to the manufacturers intent causes fatal illnesses? Giving it to kids? Conclusion: Regulate Now Regulation of AI is beyond urgent, it must happen and is happening now. The fact it’s happening piecemeal; state by state, country by country is fine. The complaints of AI companies demanding federal and singular regulation are pathetic. States lawmakers are responding to the desperate calls from their constituents. They would not need to if strong federal laws were in place. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit totallyathing.substack.com [https://totallyathing.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

AI companies are like Uber - big spend but the profit will come... Oh really?

Why do investors think AI companies are like Uber or AWS (big spend for years, big profits in the end) when the truth is they’re more like WeWork, FTX and Theranos? If you are not an investor, but someone who uses AI — please, for the love of all that is holy — divest now. Delete the app going into the holiday season, and learn to live without whatever you thought it was doing for you. Call your family or friends, read a book, or walk in the sun. Because the damn thing is going to cost you an arm and a leg by this time next year; or it will be dead in the water and addicted users will be left to pick up the pieces. OpenAI is nothing like Uber There are plenty of commentators — link [https://www.reddit.com/r/csMajors/comments/1mqo72n/ai_doesnt_produce_a_profit/] and link [https://www.bizjournals.com/bizwomen/news/latest-news/2025/08/ai-prices-chatgpt-openai-uber-claude-microsoft.html] just to show I’m not making up straw men — discussing how OpenAI is like Uber, which famously burned cash for many years before becoming profitable. As I mention in the video, Uber had a big “boots on the ground” cost: each new country they entered was massively expensive. I know, I saw it first hand. But it had a real revenue model — you pay a driver, you get a ride, Uber clips the ticket for a percentage — and although the IRL costs dwarfed their technology costs they were eventually able to see revenue greater than OpEx after many price corrections. Here in Brisbane Australia, after Uber broke the ground with massive underwriting of rollout costs — phones for drivers, local regulations impacting drivers via traffic fines — they were the only game in town for many years. There’s no Lyft here, smaller players struggled to start. Those local monopolies though temporary gave them a path to profit. The “analyses” that compare Uber to AI companies and say “give them time” to reach profitability ignore the fact that there is no country by country rollout, no category killer effect for AI. There’s no “moat”. If Alice uses AI for writing she could use ChatGPT today and whatever specialist wrapper for writers tomorrow, and then she can switch to some new startup’s product the next day. There’s no first mover advantage. Plus as Ed Zitron has extensively reported [https://www.wheresyoured.at/ai-is-a-money-trap/], AI costs per inference are massive, so the unit economics are just awful. Users on $200 plans are costing OpenAI money. The latest reports of AI cash burn is that its more than Uber and AWS put together [https://www.morningstar.com/news/marketwatch/20251205243/this-crazy-chart-shows-just-how-much-cash-openai-is-burning-as-it-chases-ai-profits]. So these comparisons are spurious, and as far as I can tell there is no path to profitability. Financial times report on OpenAI revenue Speaking of Ed Zitron, as a prominent commentator he’s collected financial information from AI industry insiders relating to OpenAI and Microsoft Azure cash burn. The figures published in the Financial Times [https://www.ft.com/content/fce77ba4-6231-4920-9e99-693a6c38e7d5] show a picture that any investor should be terrified to see: the costs of inference which is just the outputs, customers of an AI SaaS product using the service, is costing so much money that any chance of closing the gap to profitability is vanishing over the horizon. Australia and AI Numpties in the Australian Government have announced a myopic plan [https://www.industry.gov.au/publications/national-ai-plan] to buy Sea Monkeys — err, I mean, build AI data centres — here in the lucky country. They say its to “serve” Australians. [https://www.abc.net.au/news/2025-12-02/government-reveals-national-ai-plan-to-serve-australians/106093480] I want to see some openness about which lobbyists Minister Ayers has been talking to. Their road map is a road to nowhere. And I hope they realise their mistake very soon before my tax dollars go to underwrite this madness. Theranos OpenAI promised Artificial General Intelligence, a computer system as capable as a human — they have done exactly what Theranos did: failed to deliver on what was always a believable sounding pipe dream. Professor Gary Marcus — who was appallingly derided by industry figures, until eventually everyone started apologising to him and saying he’d been right all along — compared OpenAI to Theranos back in 2024 [https://garymarcus.substack.com/p/is-openai-more-like-wework-or-theranos]. Why do people believe Sam Altman when he says a computer is going to be as capable as a human being? The LLMs store information, and every time they claim to be able to pass PhD level tests it turns out they can only do that when they’ve memorised the answers [https://www.theatlantic.com/technology/archive/2025/03/chatbots-benchmark-tests/681929/]. The whole thing is a scam. Conclusion I’m done being polite about this. Especially when the sums of money are sufficient to ruin whole countries, and to bring the world economy to its knees. This Dec-Jan make a New Years resolution to divest out of AI. This is a public episode. 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