Atanu Dey: “I am sure most of us (in the US, India and elsewhere) are keenly aware that politicians are the ruling class and the citizens are second-class people. Especially in India, politicians inherited the privileges that the colonial rulers of British India had given themselves. Free palatial housing, free first class travel, free security, free everything, including life-time pensions are a burden on a deeply impoverished population. What hurts the most, in the case of India, is that the reason the people are pathetically poor is precisely because of the corrupt, blood-suckers that the majority of the politicians are. Knowing that the people actually elect the politicians is cold comfort for those who understand the injustice built into the system. The set of rules that Indians chose and abide by is what keeps them poor and underdeveloped.”
Dylan Patel: “First off, let’s define the parameters of the search market. Our sources indicate that Google runs ~320,000 search queries per second. Compare this to Google’s Search business segment, which saw revenue of $162.45 billion in 2022, and you get to an average revenue per query of 1.61 cents. From here, Google has to pay for a tremendous amount of overhead from compute and networking for searches, advertising, web crawling, model development, employees, etc. A noteworthy line item in Google’s cost structure is that they paid in the neighborhood of ~$20B to be the default search engine on Apple’s products. Google’s Services business unit has an operating margin of 34.15%. If we allocate the COGS/operating expense per query, you arrive at the cost of 1.06 cents per search query, generating 1.61 cents of revenue. This means that a search query with an LLM has to be significantly less than <0.5 cents per query, or the search business would become tremendously unprofitable for Google…If the ChatGPT model were ham-fisted into Google’s existing search businesses, the impact would be devastating. There would be a $36 Billion reduction in operating income. This is $36 Billion of LLM inference costs.”
Richard Waters: “The internet was meant to be a free-for all where all views would flourish. The trouble is, markets like social media and internet search are more like the old world of broadcast TV; there are really only a couple of real alternatives. For decades, the US had something called the “fairness doctrine” to force broadcasters to maintain balance. No one wants anything like that for the internet. Competition is the only answer.”
Michael Robinson: “The SaaS recession is here and it’s time to optimize with decisive action…Tech markets have experienced a paradigm shift, swinging from 2021’s record-setting funding activity at sky high valuations to a sharp market correction in 2022. Growth at all costs has been replaced by a return to SaaS fundamentals: revenue durability, growth efficiency, and healthy unit economics…[Here are] eight initiatives to increase efficiency in an economic downturn.”
Dan NcQuillan: “Large language models (LLMs) like the GPT family learn the statistical structure of language by optimising their ability to predict missing words in sentences (as in ‘The cat sat on the [BLANK]’). Despite the impressive technical ju-jitsu of transformer models and the billions of parameters they learn, it’s still a computational guessing game. ChatGPT is, in technical terms, a ‘bullshit generator’. If a generated sentence makes sense to you, the reader, it means the mathematical model has made sufficiently good guess to pass your sense-making filter. The language model has no idea what it’s talking about because it has no idea about anything at all. It’s more of a bullshitter than the most egregious egoist you’ll ever meet, producing baseless assertions with unfailing confidence because that’s what it’s designed to do. It’s a bonus for the parent corporation when journalists and academics respond by generating acres of breathless coverage, which works as PR even when expressing concerns about the end of human creativity.”
