The Standard, for anyone who needs reminding, is a free paper that largely comprises brief English translations of Sing Tao articles. It is tycoon-owned, virulently talks up the property market at every opportunity, and is resolutely pro-Beijing/Hong Kong government. But it used to have its own editorials, written by ‘Mary Ma’, which could on occasion be punchy and even funny. Then, some time ago, these were replaced by much duller pieces rehashing the government line. From today’s…
Yesterday, National Day celebrations in Hong Kong were more than just a display of flags and fireworks – they were a powerful symbol of the city’s resurgent vitality. The streets, thronged with both tourists and locals, pulsed with an energy not seen in recent years. This was not a fleeting moment of festivity, but a clear indicator of a resilient economy finding its footing amid global uncertainties. The scenes from Canton Road to bustling local cinemas tell a compelling story of recovery, reinvention and a future built on more than just financial prowess.
The visual cues of Hong Kong’s comeback are unmistakable. The re-emergence of mainland tourists shopping at luxury boutiques in Tsim Sha Tsui is a classic barometer of retail health. However, the recovery is broader and more deeply rooted. Local crowds flocking to restaurants and cinemas demonstrate a restoration of domestic confidence. This renewed sentiment has spurred the property market, with developers confidently launching new projects…
As Chief Executive John Lee Ka-chiu has emphasized, robust policies are designed to create prosperity that permeates the whole of society, not just the financial sector. The numbers validate this approach…
The government [sic] proactive role in incubating new industries is particularly forward-thinking. Take, for instance, the ambitious decarbonization agenda. Initiatives like green marine bunkering and producing sustainable aviation fuel from used cooking oil are masterstrokes of modern policy. They are not merely environmental imperatives but represent enormous economic opportunities. Furthermore, these emerging green industries require a vast spectrum of manpower, from top-tier professionals to grassroots workers, ensuring that the benefits of growth are widely shared across the workforce.
What – no ‘yacht economy’?
On reading this, my immediate feeling is deep sympathy for whoever has to write this stuff every day. But then it occurs to me that it takes a certain literary technique to craft such a vacuous string of words – or be so wrong. Post-property bubble Mainlanders will never go back to buying luxury garbage like they once did. Aviation fuel from cooking oil is not a ‘masterstroke of modern policy’.
It must, of course, be Chat GPT.
Which leads us rather elegantly to Ed Zitron on the case against generative AI. A long but worthwhile rant…
…if you generated a picture of a person that you wanted to, for example, use in a story book, every time you created a new page, using the same prompt to describe the protagonist, that person would look different — and that difference could be minor (something that a reader should shrug off), or it could make that character look like a completely different person.
Moreover, the probabilistic nature of generative AI meant that whenever you asked it a question, it would guess as to the answer, not because it knew the answer, but rather because it was guessing on the right word to add in a sentence based on previous training data. As a result, these models would frequently make mistakes — something which we later referred to as “hallucinations.”
And that’s not even mentioning the cost of training these models, the cost of running them, the vast amounts of computational power they required, the fact that the legality of using material scraped from books and the web without the owner’s permission was (and remains) legally dubious, or the fact that nobody seemed to know how to use these models to actually create profitable businesses.
…The problem is that most jobs are not output-driven at all, and what we’re buying from a human being is a person’s ability to think.
Every CEO talking about AI replacing workers is an example of the real problem: that most companies are run by people who don’t understand or experience the problems they’re solving, don’t do any real work, don’t face any real problems, and thus can never be trusted to solve them … leaving us with companies run by people who don’t know how the companies make money, just that they must always make more.
When you’re a big, stupid asshole, every job that you see is condensed to its outputs, and not the stuff that leads up to the output, or the small nuances and conscious decisions that make an output good as opposed to simply acceptable, or even bad.
…Large Language Models are also uniquely expensive. Many mistakenly try and claim this is like the dot com boom or Uber, but the basic unit economics of generative AI are insane. Providers must purchase tens or hundreds of thousands of GPUs each costing $50,000 a piece, and hundreds of millions or billions of dollars of infrastructure for large clusters. And that’s without mentioning things like staffing, construction, power, or water.
Then you turn them on and start losing money. Despite hundreds of billions of GPUs sold, nobody seems to make any money, other than NVIDIA, the company that makes them, and resellers like Dell and Supermicro who buy the GPUs, put them in servers, and sell them to other people.
…LLMs are an output-driven technology, but most jobs that AI is meant to replace require far more than just spitting out stuff. In reality, executive excitement over AI shows that they have little understanding of labor – they’re Business Idiots.
…The stock market has an unhealthy relationship with NVIDIA (it makes up 7-8% of the S&P 500). 55% year-over-year growth isn’t enough – even if NVIDIA sells $72 billion of GPUs in a year, the markets would punish them for not keeping up an unrealistic pace. NVIDIA got desperate, and birthed “Neoclouds,” debt-ridden data center companies selling AI compute. NVIDIA invests in them, sells them GPUs, and pays them for compute – all so they can raise money using contracts/GPUs as collateral…to buy more GPUs from NVIDIA.
…We are in the midst of one of the darkest forms of software in history, described by many as an unwanted guest invading their products, their social media feeds, their bosses’ empty minds, and resting in the hands of monsters. Every story of its success feels bereft of any real triumph, with every literal description of its abilities involving multiple caveats about the mistakes it makes or the incredible costs of running it.
(Warning: column uses ‘compute’ as a noun. It means computing power or resources.)
More here, if you like this sort of thing.










