Here is another idea for a new Olympic sport: Ppl who use ‘AI’ (thinking it is skynet) and making business critical mistakes. Eh - probably just stick with the dogs ruining sports idea instead. 🐕 ⛷️ x.com/zackkorman/status/2031…

If it’s that good / put it in the Index librorum prohibitorum I have a copy if you need it (needs updating) historyback.com/en/story/100…

Someone page Barbara Streisand 🍿

‘AI’ for me but not for thee

This is a thousand monkeys working at a thousand typewriters. Soon, they'll have written the greatest novel known to man!

This is called ‘The Whirlwind’. straitstimes.com/world/unite…

To be very clear: I have no problem with the ‘better search engine’. All the tools, MCP connections, etc. anything is easy if you know how to do it. From what I’ve seen - many do not know what they’re doing. This creates massive liability risk. Let’s see how it plays out.

Yawn 🥱 The AI Winter of the 1980s The term “AI winter” refers to periods when funding, interest, and progress in artificial intelligence dried up dramatically. The 1980s saw one of the most significant such collapses, though it was preceded by a brief boom. The Expert Systems Boom (early 1980s) The decade actually began with optimism. “Expert systems” — programs that encoded human expertise as rules to solve domain-specific problems — were the hot technology. Systems like XCON (used by Digital Equipment Corporation to configure computer orders) showed real commercial value, and companies poured money in. Japan’s ambitious “Fifth Generation Computer” project, launched in 1982, alarmed Western governments into funding their own AI programs. Why It Collapsed By the mid-to-late 1980s, the cracks were showing: •Brittleness. Expert systems worked well in narrow, well-defined domains but failed badly outside them. They couldn’t generalize or handle unexpected situations. •The knowledge acquisition bottleneck. Encoding human expertise into rules was painstakingly slow and expensive. Experts struggled to articulate their own tacit knowledge. •Maintenance nightmares. As rule sets grew, they became contradictory and unmanageable. •Hardware limitations. The specialized “Lisp machines” that AI ran on were expensive and undercut by cheaper general-purpose workstations. The Fallout By the late 1980s into the early 1990s, the market for AI hardware and software collapsed. The Lisp machine companies (Symbolics, LMI, etc.) went bankrupt or shrank drastically. DARPA and other government funders cut budgets sharply after overpromising results. Corporate AI labs were shuttered. Legacy The winter wasn’t total stagnation — important foundational work continued quietly, including early neural network research (backpropagation was popularized in 1986). But it bred lasting skepticism: for years, researchers avoided the label “AI” on their work to escape the stigma. That caution arguably contributed to the field’s more grounded, incremental progress that eventually led to the deep learning revolution decades later.