AI and Tech Summaries - AI Weekly Malaysia

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Short AI and tech summaries with source links, signal scores, and why each update matters for builders, founders, and Malaysian tech workers.

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Date	Provider	Score	Summary
02 Sep 2026, 2:35 AM	Hacker News	6.5	How accurate have Ed Zitron's AI skeptic predictions been? Dan Luu systematically checks Ed Zitron's AI-skeptic predictions against actual outcomes, starting with Zitron's November 2024 claim that Meta, Google, and Microsoft are 'dying' companies thrashing on AI. Meta's GAAP results contradict this: revenue grew from $135B (2023) to $201B (2025), with profit rising from $47B to $83B. Luu, who has no financial stake in AI companies, frames the exercise as a test of prediction accuracy rather than a pro-AI argument. Why: If you've been deferring AI adoption or tooling decisions based on Zitron-style 'AI is a bubble, the big players are collapsing' narratives, this gives you concrete financial data showing at least one flagship prediction was flatly wrong. Use it to recalibrate which commentators you treat as credible forecasters versus entertaining contrarians, especially before making build-vs-wait calls on AI features. ai-skepticism prediction-tracking meta-analysis

Date

Provider

Score

Summary

02 Sep 2026, 2:35 AM

Hacker News

6.5

How accurate have Ed Zitron's AI skeptic predictions been?

Dan Luu systematically checks Ed Zitron's AI-skeptic predictions against actual outcomes, starting with Zitron's November 2024 claim that Meta, Google, and Microsoft are 'dying' companies thrashing on AI. Meta's GAAP results contradict this: revenue grew from $135B (2023) to $201B (2025), with profit rising from $47B to $83B. Luu, who has no financial stake in AI companies, frames the exercise as a test of prediction accuracy rather than a pro-AI argument.

Why: If you've been deferring AI adoption or tooling decisions based on Zitron-style 'AI is a bubble, the big players are collapsing' narratives, this gives you concrete financial data showing at least one flagship prediction was flatly wrong. Use it to recalibrate which commentators you treat as credible forecasters versus entertaining contrarians, especially before making build-vs-wait calls on AI features.