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"In the short term, generative AI will replace a lot of people because productivity increases while demand stays the same due to inertia. In the long term, the creation of new jobs compensates for the loss of old ones, resulting in a net positive outcome for humans who leave behind jobs no one wants to do. The most important aspect of any technological revolution is the transition from before to after. Timing and location matters: older people have a harder time reinventing themselves into a new trade or craft. Poor people and poor countries have less margin to react to a wave of unemployment. Digital automation is quicker and more aggressive than physical automation because it bypasses logistical constraints-while ChatGPT can be infinitely cloned, a metallic robot cannot. Writing and painting won't die because people care about the human factor first and foremost; there are already a lot of books we can't possibly read in one lifetime so we select them as a function of who's the author. Even if you hate OpenAI and ChatGPT for being responsible for the lack of job postings, I recommend you ally with them for now; learn to use ChatGPT before it's too late to keep your options open. Companies are choosing to reduce costs over increasing output because the sectors where generative AI is useful can't artificially increase demand in parallel to productivity. (Who needs more online content?) Our generation is reasonably angry at generative AI and will bravely fight it. Still, our offspring-and theirs-will be grateful for a transformed world whose painful transformation they didn't have to endure. Certifiable human-made creative output will reduce its quantity but multiply its value in the next years because demand specific for it will grow; automation can mimic 99% of what we do but never reaches 100%. The maxim "AI won't take your job, a person using AI will; yes, you using AI will replace yourself not using it" applies more in the long term than the

GREAT ARTICLE ON AI "There is some debate about how soon AI will reach human-level general intelligence-the median year on a survey of hundreds of scientists about when they believed we'd be more likely than not to have reached AGI was 204012-that's only 25 years from now, which doesn't sound that huge until you consider that many of the thinkers in this field think it's likely that the progression from AGI to ASI happens very quickly. Like-this could happen: It takes decades for the first AI system to reach low-level general intelligence, but it finally happens. A computer is able understand the world around it as well as a human four-year-old. Suddenly, within an hour of hitting that milestone, the system pumps out the grand theory of physics that unifies general relativity and quantum mechanics, something no human has been able to definitively do. 90 minutes after that, the AI has become an ASI, 170,000 times more intelligent than a human."

"What explains the shift? Is it just hype? Or could we really have Artificial General Intelligence (AGI) by 2028? In this article, I look at what's driven recent progress, estimate how far those drivers can continue, and explain why they're likely to continue for at least four more years."

"LeCun argues that even self-supervised learning and learnings from neurobiology won't be enough to achieve artificial general intelligence (AGI), or the hypothetical intelligence of a machine with the capacity to understand or learn from any task. That's because intelligence - even human intelligence - is very specialized, he says. "AGI does not exist - there is no such thing as general intelligence," said LeCun. "We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence.""

"For higher-complexity problems, however, the models would enter "collapse", failing to generate any correct solutions. In one case, even when provided with an algorithm that would solve the problem, the models failed. The paper said: "Upon approaching a critical threshold - which closely corresponds to their accuracy collapse point - models counterintuitively begin to reduce their reasoning effort despite increasing problem difficulty." The Apple experts said this indicated a "fundamental scaling limitation in the thinking capabilities of current reasoning models"."