AI Product Case Studies 3
Must Read for Every Product Manager
Part 1 ( AI Product Case Studies ) — Read Here
Part 2 ( AI Product Case Studies ) — Read Here
Why Amazon suing Perplexity BUT Walmart partnering with OpenAI ?
Why are two of the world’s largest retailers running in opposite directions?
It might be Amazon’s High Profitable Ad’s Business.
There are two type of Shopping -
1) You know what to buy [You go to Amazon etc]
2) You discover things which you don’t know [You discover on Instagram etc.]
While shopping on Amazon, you encountered (n) number of Sponsored products , you click some of them before making a purchase. The funnel is slightly long and inefficient.
But LLMs are making the discoverability of Categories efficient , and long tail categories more efficient.
This can shorten the funnel and maybe the Ad Revenue. It’s like an ecommerce expert who is trained on a lot of data.
But for Walmart, the Ads Profit may not be the primary profit. They may go after the discoverability of the categories on their platform.
They can acquire more customers from the channel as well.
Google Cloud Revenue Growth remain almost same 35% - 34% YoY But Margin Jumped from 17 to 23%? Why?
In the tech industry, there are two basic types of problems.
A Demand Problem: This is when you have plenty of capacity (servers, products) but not enough customers. You have to offer discounts to get business, which crushes your profit margins. This is a bad place to be.
A Supply Problem: This is when you have far more customer demand than you can possibly handle. Your “factory” is running at 100%, and customers are waiting in line to give you money, if only you could serve them.
Google’s Q3 results—with its combination of accelerating revenue and rising margins—are a flashing neon sign that it has a supply problem.
This is also substantiated with this - Google announced it’s raising its estimate for annual capital expenditures (CapEx) to a stunning $91-$93 billion. This is a massive increase from $52.5 billion in 2024.
Every new, expensive data center Google builds is being filled with high-paying customers the second it’s plugged in. The $93 billion in spending isn’t a blind gamble; it’s a frantic effort to keep up with overwhelming demand.
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How companies are thinking of the Problem of Energy & Power Required for AI?
Google’s new moonshot initiative — Project Suncatcher — plans to launch solar-powered satellite constellations carrying TPUs and linked by free-space optical communications, with the goal of building a space-based, highly-scalable AI compute infrastructure.
Why it makes sense:
8× more solar energy: Satellites in sun-synchronous low Earth orbit get near-constant sunlight, offering up to eight times more solar power than ground systems — dramatically improving energy availability.
No land, no grid: Space compute removes the dependency on terrestrial real estate, cooling infrastructure, and grid electricity — a huge sustainability and logistics advantage.
Vacuum cooling advantage: In the vacuum of space, there’s no air resistance or weather; radiative cooling systems can dissipate heat efficiently without energy-hungry fans or chillers — cutting thermal management costs drastically.
Ultra-fast optical networking: Satellites flying in tight formation can interconnect via high-bandwidth laser links, achieving tens of terabits per second of throughput for distributed AI training.
Space-hardened chips: Google’s TPU v6e has passed radiation-tolerance tests, showing reliable performance even under high cosmic radiation exposure.
AI isn’t just about models and algorithms — it’s equally about power, infrastructure and resources.
If that happens, AI bubble might leave us with a Solid Infrastructure for unlimited Energy and Power
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About Author
Shailesh Sharma! I help PMs and business leaders excel in Product, Strategy, and AI using First Principles Thinking. For more, check out my Live cohort course, PM Interview Mastery Course, Cracking Strategy, and other Resources
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