Manas Bihani
About

the questions

  1. What is a moat in an AI world?
  2. Why do AI products converge?
  3. What becomes scarce when intelligence becomes cheap?
  4. Does distribution matter more than technology?
  5. Why might human-made things become more valuable?
  6. What happens to expertise when everyone has the same models?
  7. Which parts of an AI startup are actually defensible?
  8. Where does value move when intelligence becomes commoditized?

everything on the desk

  1. The periodic table of the AI stackVisualization
  2. What is a moat when the model isn't yours?Note
  3. The problem-selection premiumNote
  4. Same model, different wiringNote
  5. Selection is the new bottleneckNote
  6. Get friendly with the AI raceEssay
  7. The convergence taxNote
  8. The luxury of realityNote
  9. The non-technical technical advantageNote
  10. The verification economyNote
  11. The bets against the wallVisualization
  12. You can't buy your way outVisualization
  13. How a chatbot writes one wordVisualization
  14. The grid is the last wallVisualization
  15. Who got paidVisualization
  16. Why this paper mattersExplainer
  17. Transformer: Why did transformers replace RNNs?Vaswani et al., NeurIPS 2017
  18. KV cache: Why does a long conversation get slower and cost more than a short one?Shazeer, 2019
  19. Mixture of experts: Why do some AI models have experts?Fedus, Zoph and Shazeer, 2021
  20. FlashAttention: Why is attention slow when the GPU is barely doing any arithmetic?Dao et al., NeurIPS 2022
  21. Mamba: Why does a model reread the whole conversation instead of just remembering it?Gu & Dao, 2023
  22. PagedAttention: Why does a GPU with free memory still refuse new requests?Kwon et al., SOSP 2023
  23. DeepSeek: How did DeepSeek train a frontier model so cheaply?DeepSeek-AI, 2024
  24. Jamba: Why does Jamba matter?Lieber et al., AI21 Labs, 2024
  25. BitNet: Why does BitNet matter?Ma et al., Microsoft Research, 2025
  26. DeepSeek-R1: Can a small AI model learn to reason like a huge one?DeepSeek-AI, 2025
  27. Kimi K2: Why does Kimi K2 matter?Kimi Team, Moonshot AI, 2025
  28. Sliding-window attention: How do models handle huge context windows without the memory bill exploding?Gemma Team, Google DeepMind, 2025
  29. How electricity becomes intelligenceVisualization
  30. This desk, as a datasetDataset
  31. The first version of this roomNote
  32. The aura dividendNote
  33. Distribution is rented attentionNote
  34. The Convergence TestNote
  35. A shelf for thinking about cheap intelligenceCollection
  36. Anatomy of an AI startupNote
  37. Six shocks to expertiseNote
  38. Nineteen Public KeysEssay
  39. The value migration machineModel
  40. AAA-Rated GPUsEssay
  41. Moats, before and afterVisualization
  42. The rhinoceros problemNote
  43. What becomes scarce when intelligence becomes cheap?Essay
  44. AI Has Passed Every Exam. It Has Never Had an Idea.Essay
  45. What Becomes Scarce After Intelligence?Essay
  46. India’s Carbon Markets : A New Test for Global Climate PolicyEssay
  47. Google Wants AI to Become BoringEssay
  48. The Wall That Wasn’t YoursEssay
  49. The Rate-Limiting StepEssay
  50. The Speed of Being WrongEssay
  51. Uber Burned a Year of AI Budget in Four Months. A Rat Catcher in 1902 Knew WhyEssay
  52. Finding a Flat in India Is Broken. We Have the Technology to Fix It. Nobody With Power Wants To.Essay
  53. Why We Can Never Have Good Social MediaEssay
  54. Gen Z Is Going OfflineEssay

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Essay · 4 Jul 2026

Google Wants AI to Become Boring

Why Google may be trying to make intelligence disappear.

First published on Substack, 4 Jul 2026.

In 1998, two Stanford PhD students wrote a paper about how to rank web pages, and the entire internet reorganized itself around the answer. You know how that one ended. The two kids were Larry Page and Sergey Brin, the paper was PageRank, and the company they built off it spent the next twenty-five years being the toll booth every road on the internet ran through.

One mistake and we got world’s biggest search engine, read interesting  story behind the birth of Google

In 2017, eight researchers wrote another paper. It was called “Attention Is All You Need,” and it introduced the transformer the thing underneath ChatGPT, underneath Claude, underneath every model that got a magazine cover for supposedly being the Google killer. Here’s the punchline. Those eight researchers worked at Google.

Sit with that for a second, because everyone breezes past it. Google published the founding document of its own disruption, put it on the internet for free, and then watched a startup in San Francisco read its homework and nearly steal the crown with it.

Now. If you invented the bomb, handed it to the guy across the street, and watched him aim it at your house, how would you feel?

Furious, right? You’d learn your lesson. You’d never give away the paper again. You’d lock the lab, patent everything, sue somebody.

Google’s response looks like the opposite. And that response, a company nearly disrupted by its own invention answering by giving away even more is the whole story. It’s the tell. It’s the thing that suggests Google is playing a different game than most of the industry, and a lot of people cheering the stock haven’t noticed which game.

Let me show you the game.


ChatGPT vs Google Bard - Comparing usage 🤖 — Follow for more ChatGPT  content 🤖 Follow @chatgptricks Follow @chatgptricks Follow @chatgptricks —  #ai #chatgpt #productivity #tech #prompts

In early 2023, it looked like Google had fumbled the future. Bard hallucinated in its own launch demo, and Alphabet lost about $100 billion in market value in a single day. Kodak with better logos. For about a week, half of Silicon Valley published some version of that obituary.

Hold onto Bard. We’re coming back to him.

Because in hindsight, that moment says more about what everyone was measuring than about what Google was building. Everyone was grading a chatbot. Google was increasingly behaving as though the chatbot wasn’t the thing worth winning.

What if the whole industry was watching the wrong game?


Here’s the game everyone thinks they’re watching.

As the AI tools continue to rapidly change, are you changing too?,  WadiDigital recently stopped paying for ChatGPT Premium., We found that  Premium we were paying for and using with Claude, Gemini… | …

There’s a race. The prize is the smartest model. OpenAI, Anthropic, Google, a rotating cast of Chinese labs, all sprinting, and every few months somebody posts a benchmark score and the tech press treats it like a Game. A model climbs three spots on a leaderboard and it’s a headline. Drops two points on a reasoning test and by dinner someone’s declared the moat dead.

Everyone agreed, with total confidence, on the rules. Ship the smartest model, win. That’s the whole theory. Nobody interrogated it, because it’s the obvious read, and the obvious read is usually right.

So watch what Google does, if that’s really the game.

It gives away Gemma a genuinely good model free, to any nine-person startup that wants to go build a competitor with it. It publishes compression research that teaches the whole world how to run Google-quality models on cheaper hardware, meaning less need for Google’s own cloud. Then it ships Gemini Nano straight into Android, so the model runs on your phone, where Google can’t meter it, can’t bill for it, can’t own the moment.

Three moves. All in the same direction. All insane if the model is the prize.

You do not hand the vault key to strangers when you’re racing to own what’s in the vault.

Unless you were never racing for what’s in the vault.

Here’s the sentence the whole thing hinges on, and I want you to feel the weight of it:

Gemini isn’t the product. It’s the electricity.

Now hold on. Before you go repeat that at a dinner party it’s not quite right, and the part that’s wrong is the part that matters.

Electricity is dumb. A grid doesn’t get smarter because more toasters plug in. The kilowatt that hit your coffee maker this morning learned nothing about how you take your coffee.

Google’s grid learns.

Every search sharpens the ranking. Every wrong guess the model makes on your phone teaches it to guess better. Every doc in Workspace, every route in Maps none of it dead-ends. It flows back in. Imagine a power grid where every toaster quietly reported how it liked its toast, and next winter the electricity itself showed up better suited to bread.

That’s not a grid. That’s a grid with a nervous system.


So if the model was never the prize what is?

You win the way a utility wins. Own the grid. Then get somebody else to help pay for the power.

Google moved the meter onto your phone. When Nano runs on-device, the compute drains your battery, not Google’s data center. Google’s own docs say the quiet part out loud: on-device inference eliminates the server call and its cost. Say that last word slower. You. One of the most-used AI systems alive, and Google doesn’t pay to power it, cool it, or run it. The customer just built his own substation and doesn’t know it.

There’s a quieter bet buried in this, and it’s the whole ballgame. Cheaper intelligence could go two ways. People ask the same number of questions for less money flat demand, thin margins, a shrinking business in an efficiency costume. Or it behaves like cheap coal in the 1800s: get it cheap enough and nobody uses less, they invent a thousand new things to burn it on. Every move Google makes only pays off in the second world. Coal, not tax returns. If intelligence turns out to be capped if people just want their ten questions answered and then stop the entire strategy is a very expensive mistake. Google is betting it’s coal. I think they’re right. But that’s the bet.


Now step back and look at what Google actually owns, because this is the part where it should click.

Want to ask a question? Search. Want it on your phone? Android. In your browser? Chrome. At work? Workspace. By the time an answer reaches your eyeballs, you’ve walked through four Google toll booths and never saw a single gate.

“Fine,” you say, “but half the world’s on iPhone.” Right and in January 2026, after years of trying to build its own AI stack, Apple gave up and licensed Google’s Gemini to run the next Siri, reportedly paying Google roughly a billion dollars a year for the privilege. Google doesn’t need to own the phone if it supplies the intelligence running inside it. The hallway changes. The current still flows.

And yes, Google kills products constantly. Reader, Inbox, Stadia, a whole graveyard site to catalogue them. But look at what’s actually buried there. Products. Things you were meant to open and judge. Google’s real wins were never things you open, Search became how you find, Chrome became how the web runs, Android became the layer under everything. Google is bad at things you visit and terrifyingly good at things you stop noticing.

Which is why the strangest thing about Google I/O 2026 was that almost nothing launched as its own thing. No new destination. No “download this.” Gemini just… appeared, inside five products you already had open. Google wasn’t shipping an AI app that quarter. It was rewiring five houses you already lived in.

And then here’s where the metaphor stops being a metaphor.

Google bought enough renewables to power its operations for the year | Vox

I started this essay calling Google “electricity.” Then I noticed Google spending a suspicious amount of time thinking about actual electricity. Not buying green power for PR, the way everyone does. Buying a $4.75 billion power company outright to secure dedicated generation for its data centers. And through X’s Tapestry project, trying to build an operating system for the electric grid itself. Backing grid-storage projects. Investing in the physical grid beneath its digital one, years before it needs the capacity the same instinct that gave away the transformer, aimed now at literal electrons.

Read that staircase again, top to bottom. Models, given away. Phones, paying their own compute. Hallways, all owned. Cloud, pouring concrete years early. And underneath all of it, the actual power plants.

That’s not a company. That’s a public utility!

I’ll be honest about the other reading, because I don’t fully trust how much I like this one. Maybe it isn’t architecture. Maybe it’s just a company with more cash than judgment buying in every direction and calling the pile a strategy after a few bets land. That reading exists; I can’t kill it. What keeps dragging me back is the direction every move points the same way, and coincidence doesn’t aim.


There’s exactly one place the physics won’t cooperate, and honesty demands I name it.

2025–present global memory supply shortage - Wikipedia

Running intelligence on billions of cheap phones takes memory. Memory comes from a few factories, and AI’s own hunger is eating the supply mobile memory prices have nearly doubled in a single quarter, and Micron, one of the few companies that makes the stuff, has all but abandoned consumer chips to chase AI customers, posting an 84.9% gross margin for the trouble. The AI boom Google is fueling is making the cheap phones Google’s plan depends on more expensive to build. You can’t out-engineer a factory shortage. You can only spend to outlast it which is part of why a company sitting on $127 billion in cash just raised one of the largest equity offerings in American history anyway. And the more Google looks like the hallway everyone has to walk through, the more regulators stop seeing infrastructure and start seeing a bottleneck. If intelligence becomes infrastructure, those hallways get more valuable and more dangerous to own.

One crack. In an otherwise sealed argument. I’ll take those odds.


Remember Bard?

Why has Google’s new AI chatbot caused the company’s stock to fall? - AS USA

The one that hallucinated on stage and cost Alphabet a hundred billion dollars before the next day’s close. The moment everyone still opens the story with. The proof that Google lost.

What if that was just the wrong exam?

Try something. Right now. Name the model that answered your last five questions. The one in your search bar. The one that finished your email. The one that summarized the doc, the one on your phone that guessed your next word.

You can’t.

That’s not a hole in your memory. That’s the entire thesis, landing three years late and very quietly. Everyone graded Google on whether it could build a chatbot you’d remember. Google was building something stranger a world where you never notice which model answered, because the second you asked, you were already standing inside its grid.

The winners of this era won’t be the companies whose models people remember. They’ll be the companies whose infrastructure people forget they’re using.