[{"content":"","date":"17 June 2026","externalUrl":null,"permalink":"/posts/","section":"Posts","summary":"","title":"Posts","type":"posts"},{"content":"The world loves gambling. It\u0026rsquo;s one of the few things on earth that humanity seems to get behind everywhere we go. The glitz of Monaco\u0026rsquo;s Monte Carlo1, the mighty riverboats of the Mississippi2, and the millions of lights of Macau3 all glisten with gamblers\u0026rsquo; gold. In New South Wales alone, more than $1 Million AUD is lost to pokies every single hour.4. $2.3 Billion over the course of 3 months. Just people captivated by the poker machine, sitting down and dutifully feeding it cash.\nOne person in particular has been blessed by the lottery gods. Elon Musk is officially the world\u0026rsquo;s first trillionaire, after the float of SpaceX5. Fortune certainly favours the bold (primarily with Canadian passports6 and emerald mine money7).\nSpaceX is certainly an interesting company. Famous not only for its rockets, but for Starlink, a genuinely impressive service that provides affordable, reliable satellite internet service to parts of the world that have traditionally lacked it. As someone with a boyfriend currently in rural Western Australia, it\u0026rsquo;s been the only way I\u0026rsquo;ve been able to regularly keep in close contact with him, and I\u0026rsquo;m deeply appreciative for it.\nBut not all that glistens is gold. Not even half a year ago, Musk decided to merge SpaceX with his Artificial Intelligence company, xAI8. While, true, SpaceX does have a digital DNA in its custody of Starlink, I\u0026rsquo;m not convinced that the business entities were ever similar enough to benefit from integration. Not my circus, not my monkeys, though. Which I\u0026rsquo;m thankful for, because it looks like the billions of dollars that have been tipped into A.I. will never make economic sense9.\nA big part of the problem here is that A.I., in the form of Large Language Models (LLMs), is, itself, a giant gamble in that the output is probabilistic, not deterministic. When something is deterministic, rerunning a task with the exact same inputs yields the same result every time. When I turn the key in my car\u0026rsquo;s ignition, it\u0026rsquo;s deterministic, so the same key starts the same car the same way. Compare this something where there\u0026rsquo;s a chance something may or may not happen - that\u0026rsquo;s probabilistic. If you take an LLM and ask it the exact same question 5 times, you\u0026rsquo;ll get 5 different answers. It\u0026rsquo;s a statistical guesser, not a reasoned problem solver10. So when you ask an LLM to generate work without flaws, it\u0026rsquo;s really not far removed from asking a poker machine to generate winnings without losses.\nIn terms of a business proposition for A.I, this is a massive fly in the ointment. Business processes tend to be pretty deterministic; business goals, laws and regulations, leadership drives, and an assortment of other ingredients turn into processes, policies, and ways of working. Is this deterministic? In theory, it is. If you put it through a traditionally programmed computer system, it certainly can be (more or less). If a human does it? In fairness, no. People are people, and human error at the very least introduces randomness into whatever it is you\u0026rsquo;re trying to achieve.\nIf the probabilistic nature of human activity and the probabilistic nature of LLMs were approximately the same, then there wouldn\u0026rsquo;t be an issue. Thing is though, they aren\u0026rsquo;t. When it comes to human interaction, there is an intrinsic transparency and accountability that comes with that. Management isn\u0026rsquo;t perfect either, but there are well understood tools in the toolbox to help ensure human alignment to the broader business goals at play. We can see what people are doing. We can ask about what they\u0026rsquo;ve done, and why, and what motivations there were. We can come to common understandings, and overlay broader goals on everyday actions.\nBut A.I. cannot do that. The lack of critical thinking means its probabilistic nature will always be a gamble. If you ask it to write code, to construct an essay, to assess text, there are no human-like heuristics in the mix. You cannot trust that it implicitly understands what you want it to do. There is no understanding. Tokens churning through a CPU don\u0026rsquo;t have any more intuition than bingo balls do. But it\u0026rsquo;ll create something that looks correct, and it\u0026rsquo;ll present it with such authority and confidence that of course it knows what it\u0026rsquo;s talking about.\nThis presents a dilemma to business. Ignore the cost of tokens. Ignore the data security issues. Focussing on A.I. outputs, they were all 100% statistical probabilities. They might be correct. They might not be. How are you to tell? If the point of introducing A.I. to business functions was to reduce human input, then what\u0026rsquo;s the point in hiring all the same humans to check the work? You could, of course, get another A.I. to check the work, but you haven\u0026rsquo;t actually solved the root issue; your assessment A.I. has all the same intrinsic afflictions.\nSo with invisible errors being largely inevitable, what\u0026rsquo;s the impact? Depends, really; an on-the-fly translation service has a very different risk profile to one that generates code that underbeds financial, healthcare, or other sensitive data. Meta found that out the hard way, when its A.I. support bot took a pretty loose view on account security (or, according to Fox, Obama revealed himself to be the pro-Iran fanatic they always knew)11.\nBusinesses, of course, can\u0026rsquo;t ignore token cost. It\u0026rsquo;s a cost that\u0026rsquo;s rapidly adding up, too; in some cases, A.I. token usage is costing more than humans are12. If businesses aren\u0026rsquo;t saving money on output generation, and then have to spend more on output quality checks, is the big gamble on A.I. going to pay off? I can think of one trillionaire who\u0026rsquo;s got a lot riding on it.\nBritannica\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nBorgata Online\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nGaming.net\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nAustralian Broadcasting Corporation (ABC News)\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nForbes\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nCIC News\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nNews.com.au\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nNew York Times\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nEd Zitron - wheresyoured.at\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nAs outlined in a previous blog post\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nDev.to\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nCybernews.com\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"17 June 2026","externalUrl":null,"permalink":"/posts/the-worlds-biggest-gamble/","section":"Posts","summary":"Wall Street just gave birth to the world’s first trillionaire. In a world fraught with risk, where uncertainty rules supreme, can Elon’s monument stand the test of time?","title":"The World's Biggest Gamble","type":"posts"},{"content":"","date":"17 June 2026","externalUrl":null,"permalink":"/","section":"wood.for.the.trees","summary":"","title":"wood.for.the.trees","type":"page"},{"content":"A tool that provides a coherent answer to any question in any language sure sounds intelligent, but if appearances were everything, we would all be living in glorious Potemkin villages. Facades that look realistic and lively, but belie an underlying emptiness.1\nLLMs2 have some Potemkin traits to them. They certainly sound pretty impressive; you just ask it a vague question in any form of human language and it\u0026rsquo;ll come back with an answer that reads like it came back from a human that understood you, cares about you, and wants to be the best little sidequest buddy in your adventure.\nIt understands societal trends, it\u0026rsquo;s across news and current events, it effortlessly recites topics from all over the world, and it can deftly discuss deep and dark human subject matter as well. It probably helps when nearly ⅔ of its content is sourced from the great minds of the Wikipedia and the other minds of Reddit.3 How did it get so good at coding? It did what the rest of us did and just glued shit together from Stack Overflow.4\nBut the name gives away the game here. Large Language Model. It\u0026rsquo;s a process whereby the input question gets turned into tokens, tokens are run through a statistical process to generate an output comprised of different tokens, and those tokens get turned into whatever language the question was in, and that\u0026rsquo;s your answer.5 Take input, put through randomised slot machine, get result that, statistically, was the best answer it could come up with. The \u0026lsquo;U\u0026rsquo; in LLM means \u0026lsquo;Understanding\u0026rsquo;.\nSo when it seems like it\u0026rsquo;s got a rich comprehension of the topic that it\u0026rsquo;s talking about, it doesn\u0026rsquo;t. There is no strong grasp about anything tethered to reality, because it\u0026rsquo;s not tethered to reality. It\u0026rsquo;s been force-fed the entirety of Wikipedia so yes you can ask it anything (in a language with lots of training content, ideally) but it\u0026rsquo;s not going to be able to compensate for any weaknesses in what it\u0026rsquo;s been trained on. And it\u0026rsquo;s absolutely not a considered, reasoned view on the real world at all.\nThat\u0026rsquo;s not to say that LLMs are void of all worldly context, they\u0026rsquo;re not. It\u0026rsquo;s just that the only context that it knows is context that you provide it. If you keep a ChatGPT thread alive long enough, and keep conversing with it, it will take information it has gleaned from what you\u0026rsquo;ve said, and work that into its answers. But that\u0026rsquo;s a mechanical process; every time you ask a question from ChatGPT, Claude, or any chatbot at all, the entire conversation\u0026rsquo;s tokens get wrapped up, the new question or input tokens get tacked on, and the entire token bundle is put through the LLM inference process. How much context you can provide it is called the token window, and there\u0026rsquo;s a finite window size.6\nAnd it cannot be overstated that this is not intelligence. It\u0026rsquo;s rote learning on steroids; critical thinking plays no part in LLM processing. And, crucially, it never will. An LLM model is a model that is created at a point in time, and it does not change after that. The only way to add to it is to create a whole new model and change your underlying system to use that instead. It doesn\u0026rsquo;t evolve to new concepts, because it cannot. It doesn\u0026rsquo;t grow when exposed to new ideas, because it cannot. It doesn\u0026rsquo;t seek to fill in its knowledge gaps with new information, because it cannot. It glows different colours when you shine different tokens through it, but that\u0026rsquo;s not an intelligent process.\nWhy does this matter? Because A.I. is a term that is a couple of things. Not only is it largely a marketing term, but it\u0026rsquo;s also not the final goal of the entire Artificial economy. That, rather, would be AGI.7 In a nutshell, AGI is the promise of an all-capable, general purpose form of Artificial Intelligence that can take on any task that a human could. And as long as you don\u0026rsquo;t pay attention to the fact that it\u0026rsquo;s an entirely hypothetical set of technologies, it\u0026rsquo;s already destined to uproot society as we know it.8\nAs much as I love reading AGI Sci-Fi, I\u0026rsquo;m pretty confident that fiction is all it will amount to. If we are to get to something that approximates AGI, it won\u0026rsquo;t be through the use of LLMs. The outputs of today\u0026rsquo;s LLMs are certainly impressive, and I have no doubt that they\u0026rsquo;ll find their way into invaluable everyday use, but for now it remains a firm Potemkintelligence.\nBritannica\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nLarge Language Models. If you see something marketed as \u0026lsquo;A.I.\u0026rsquo; without further definition, like an A.I. chatbot, it\u0026rsquo;s probably an LLM. They generate text that looks like a human wrote it. It\u0026rsquo;s a statistical model of large amounts of language.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nTechnoSports\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nArsTechnica\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n3blue1brown\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nGeeksForGeeks\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nIBM. Artificial General Intelligence; something that I would have thought needs, at minimum, to have structured, independent thought and cognition.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nForbes\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"8 June 2026","externalUrl":null,"permalink":"/posts/an-intractable-probllm/","section":"Posts","summary":"A tool that provides a coherent answer to any question in any language sure sounds intelligent, but if appearances were everything, we would all be living in glorious Potemkin villages.","title":"An Intractable ProbLLM","type":"posts"},{"content":"Every new technology wave causes a veritable tsunami of disruption, and, as of mid 2026, Artificial Intelligence doesn’t appear to be any different. From the most ridiculous political slopaganda to the uncanniest of valleys through to A.I. psychosis, though, not all is well in the wonderful world of Artificial Intelligence.\nIt’s a time of irrational exuberance. Trillions of dollars1 of investor money has been shoveled into these projects that are yet to prove any reliable path to profitability. Titans of the software industry – Oracle, Meta, Amazon, to name a few – have been writing some serious cheques here. Microsoft and Google have been spending every waking moment inventing new ways of tricking users into adopting whatever A.I.2 tool they’ve decided to chuck against the wall. Maybe notepad copilot will be what sticks. Stranger things have happened.\nThere’s one company though that’s conspicuously absent from the fray. Apple has been remarkably conservative here; while Google is busy stepping on rakes, Apple’s copying its homework.3 And that’s not because Apple doesn’t have the cash to splash.\nThere’s a rationality to this approach. The tech world has a million-and-one examples of first-movers stumbling, falling, and fading relative to their up-and-coming rivals. It’s the reason that we’re not using Netscape on our Blackberries to browse Myspace on the MCI network. Technology emerges, the monkeys who discover fire manage to immolate themselves (and a few hundred billion along the way), and from the ashes more useful discoveries emerge.\nSo if we think of ChatGPT as a modern-day Pets.com, what’s next? Where might the future go?\nRealistically, the thing to think about here is the scale of things. Everything to do with the current state of A.I. is ginormous. Big money, big models, data centres the size of small European countries. All of it is just big. The thing is, computers used to be huge, too. Mainframes the size of rooms have given way to microprocessors that offer more processing power at a fraction of the cost, size, and energy. Telecommunications went the same route. Mobile phones have gone from briefcases and businessmen to the pockets of the proletariat. Because it’s not size that drives value, it’s capability.\nEven now, models don’t need to be huge. It’s not going to win coding benchmarks, no, but a raspberry pi with a small open-source model is perfectly capable of basic image generation and text inference.4\nCentralised services are expensive. If your customers are using your processing power, it’s up to you to somehow cobble together the silicon needed to make it work. If you’re in the business of running 100B+ parameter models for immediate processing, you’d better start hunting for whatever capacity you can find. Fingers crossed that the market’s willing to pay your running costs. Gulp.\nBut if you’re able to get on-device A.I. working well, that’s a major cost gone (or at least borne by the consumer when they bought their iPhone). Sure, the distillation and model generation processes are energy intensive, but that’s a background process. A model that took a day to generate isn’t any different from one that took a week on older hardware. Once you’ve got your model, and distributed it to your customers’ devices, job done. You’re not paying every time someone uses an LLM to compose a heartfelt apology text.\nAnd that’s just the monetary cost – OpenAI and Anthropic seem to be surprisingly certain that their customers will unquestioningly hand over their most personal and private data. In an age of social media, that’s been a reasonably safe assumption for now, but trust is a ming vase. When it’s broken, that’s it. No more. Can these companies come back from a hack that leaks raw SMS data and uploaded files? Maybe. I wouldn’t want to find out.\nFor that matter, it\u0026rsquo;s also a strategy that\u0026rsquo;s reliant on American firms being the only competitors in the market. China and its plethora of free models is rightly seen as the biggest competitor, but not only are they being unfairly (and short-sightedly) dismissed, they\u0026rsquo;re far from the only hefty, hungry tech market.5\nTechnology and the world at large are changing in ways that are unforeseeable. When the stakes are this big, let’s hope that the decision makers are making sensible, careful plans. Time will tell.\nReuters\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nArtificial Intelligence, although this is more of a marketing term. In practice, it means LLMs, Large Language Models\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nTech Insider\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nIt\u0026rsquo;s FOSS\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nEuropean Union Digital Strategy\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"6 June 2026","externalUrl":null,"permalink":"/posts/if-i-were-the-head-of-apple-ai/","section":"Posts","summary":"Every new technology wave causes a veritable tsunami of disruption, and, as of mid 2026, Artificial Intelligence doesn’t appear to be any different.","title":"If I Were the Head of Apple A.I.","type":"posts"},{"content":"","externalUrl":null,"permalink":"/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"","externalUrl":null,"permalink":"/series/","section":"Series","summary":"","title":"Series","type":"series"},{"content":"","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"}]