Google reported a 150x increase in the volume of tokens processed in just the past 17 months, demonstrating explosive real-world AI usage.
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Google reported a 150x increase in the volume of tokens processed in just the past 17 months, demonstrating explosive real-world AI usage.
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At 3:15 · chapter starts 2:50
David George opens with staggering numbers: $1 trillion of data centers in the US, plans to add $3–4 trillion over five years, a data center buildout that already dwarfs the entire US interstate highway system in inflation-adjusted dollars, and a 150x increase in Google's token processing over just 17 months [2] — Gavin Baker "10-point ROIC increase for big CapEx spenders: The biggest public GPU spenders have seen approximately a 10-point increase in return on inv…" 04:45 . The scale sounds terrifying, but Baker is unfazed [1] — Gavin Baker "In the 2000 bubble, 97% of all fiber laid in America was dark — unused and worthless. Today, every GPU is running hot. Baker's ROIC test co…" 03:38 . He walks through two key comparisons: Cisco peaked at 150–180x trailing earnings in 2000 versus NVIDIA's current ~40x multiple, and unlike dark fiber — where 97% of installed cable sat unused at the bubble's peak — every GPU is running at capacity. The most rigorous test, Baker argues, is ROIC: the biggest public GPU spenders have seen roughly a 10-point increase in their returns since ramping up CapEx [2] — Gavin Baker "10-point ROIC increase for big CapEx spenders: The biggest public GPU spenders have seen approximately a 10-point increase in return on inv…" 04:45 . The ROI on AI infrastructure spending, he concludes, has been unambiguously positive — even if future Blackwell spending will be a fair test.
Cisco peaked at 150–180x trailing earnings during the 2000 bubble; NVIDIA trades at roughly 40x — a much more moderate valuation.
In the 2000 bubble, 97% of all fiber laid in America was dark — unused and worthless. Today, every GPU is running hot. Baker's ROIC test confirms it: the biggest AI CapEx spenders have seen roughly a 10-point increase in return on invested capital since the buildout began. The numbers say this is not a bubble.
At the peak of the 2000 internet/telecom bubble, 97% of fiber laid in America was dark — unused and unlit — the defining sign of overbuilding.
The biggest public GPU spenders have seen approximately a 10-point increase in return on invested capital since ramping up AI CapEx, signaling strong returns so far.
The major hyperscalers collectively generate around $300 billion of free cash flow per year, with $500 billion of cash on their balance sheets, underwriting AI CapEx.
Lighting up 1 gigawatt of AI compute capacity on NVIDIA chips costs approximately $40–50 billion.
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
Eyal and Yali shut down all marketing and spent 4 months completely rebuilding PropGPT from scratch.
PropGPT has accumulated over 40,000 total downloads since launch.
PropGPT's large language model (AI) operating costs are just $20 per month, and the cost is continually falling.
Ad-based monetization works well for game apps where users spend extended time in-session, as seen with Grid and Wordle.
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