Скачать или смотреть The End of Moore's Law for AI - Why AI Execs are terrified of this!

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Chapters:
0:00 - The End of Moore's Law for AI
0:45 - Welcome to StartupHakk!
1:20 - The AI Price Shock Nobody Saw Coming
2:35 - The Quadratic Cost Problem Explained
3:50 - The End of Subsidized Intelligence
5:05 - Why Batch Processing is Your New Best Friend
6:25 - The Open Source Models Revolution
7:40 - Hardware Bottlenecks Are Real
8:55 - The AI Data Quality Crisis
10:10 - Strategic Implications for Your Business
11:25 - The Microsoft Layoffs Connection
12:00 - Why Scaling Laws Are Hitting the Wall
12:40 - The Pricing Transparency Problem
13:10 - What This Means for Your Business (Summary)
13:45 - Final Thoughts & Call to Action

Google just pulled the rug out from under the entire AI industry, and almost nobody noticed. Last week, they quietly doubled the price of their most popular AI model - and then quadrupled another part of the pricing. This isn't just a price hike, folks. This is the sound of the AI gold rush hitting a concrete wall. For years, we've all been told that AI costs would keep dropping forever - that your expensive AI features today would be pennies tomorrow. That party just ended. Why did Google break the most sacred rule in AI economics? Why are AI executives at every major company quietly panicking right now? And what does this mean for your business when the AI cost spiral starts going UP instead of down?

We're witnessing the end of AI's version of Moore's Law - the belief that intelligence gets cheaper every year. Google's Gemini Flash price increase isn't just corporate greed. It's physics, economics, and reality colliding with Silicon Valley hype. This changes everything about how you should approach AI in your business.

The Price Shock We Saw Coming
Google increased Gemini Flash input token prices from $0.15 to $0.30 per million tokens - a 100% jump overnight.
Output token prices exploded from $0.60 to $2.50 per million - more than quadrupling the cost.
This marks the first time any major AI provider has raised prices on an established model rather than lowering them.
The move was done quietly with minimal fanfare, hoping nobody would notice the seismic shift.
Most companies using Gemini Flash woke up to bills that were suddenly 2-4 times higher than expected.
This breaks the fundamental assumption that AI costs always go down - an assumption that entire business models were built on.

The Quadratic Cost Problem Nobody Talks About
Here's the dirty secret of AI: processing costs don't scale linearly - they scale quadratically with sequence length.
When you send a long document to summarize, the AI has to calculate attention between every token and every other token.
That means if you double the input length, the processing cost goes up by four times, not two.
API providers have been charging you a flat rate per token while their actual costs explode exponentially.
It's like a gas station charging the same per gallon whether you buy one gallon or a hundred, but their cost quadruples with volume.
Google finally admitted they were losing money on certain types of workloads and had to correct the pricing.

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