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By Noah Ogbi

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  1. Home
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  3. ›Google

Google

No. 5

Apple's Platform Gambit: Why iOS 27 Lets Google and Anthropic Power Siri

May 28, 2026
Industry·Noah Ogbi·12 minMay 28

Two weeks before WWDC, Apple's AI strategy is fully legible: own the user relationship and the hardware, outsource the intelligence. The $1 billion Gemini deal and the new Extensions framework are not signs of weakness - they are the most Apple-like move the company has made in a decade.


No. 4

The Routing Layer Arrives: OpenRouter's $113M Bet on the Post-Single-Model Era

May 26, 2026
Industry·Noah Ogbi·8 minMay 26

OpenRouter has closed a $113 million Series B at a $1.3 billion valuation, led by CapitalG, with a syndicate of enterprise cloud incumbents backing the bet. The round reflects a broader conviction that the routing layer - not the model layer - is where AI infrastructure value will concentrate.


No. 3

Cloud Revenue Vindicates Big Tech AI Spending, but Meta's Runaway Capex Unnerves Investors

Apr 30, 2026
Industry·Noah Ogbi·9 minApr 30

Alphabet, Microsoft, Amazon, and Meta reported Q1 2026 results on April 29 that collectively delivered the clearest evidence yet that AI infrastructure spending is generating real cloud revenue. The outlier was Meta, whose strong earnings were overshadowed by a capex guidance range raised for the second time this year, with no concrete product milestone attached to the ceiling.


No. 2

Gemini 3.1 Pro Reviewed: Google's Reasoning Reversal

Apr 3, 2026
AI Research·Noah Ogbi·16 minApr 3

Google DeepMind's Gemini 3.1 Pro arrived with the strongest independently verified reasoning scores of any frontier model. Three weeks later, GPT-5.4 changed the picture. A benchmark-by-benchmark assessment of where Gemini still leads, where it has fallen behind, and what the competitive gap actually looks like on verified data.


No. 1

Google's TurboQuant Compresses AI Memory by 6x. Wall Street Panicked.

Mar 28, 2026
AI Research·Noah Ogbi·10 minMar 28

Google Research has published TurboQuant, an algorithm that cuts the memory cost of running large AI models by at least sixfold - with no accuracy penalty and no retraining required. Memory chip stocks sold off sharply. The sell-off misread what the research actually says.


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