There Will Never Be Enough Compute - by Mithil Salunkhe

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There Will Never Be Enough Compute<br>The modality no one is pricing<br>Mithil Salunkhe<br>Jul 07, 2026

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Every single morning, you wake up, move your blanket aside, swing your legs out of bed, and place both feet on the floor without a second thought.<br>But pause for a moment and consider the computational cost behind that simple action.<br>The instant you open your eyes, on the order of a gigabit per second of visual information from three-dimensional reality begins streaming into your retinal system. The retina compresses and filters the raw feed, and the brain folds what’s left into its internal model of the world, effectively “prefilling” the context needed for action. Raw data in, a usable model of the room out: your morning begins with a massive act of computation.<br>Try running that job on silicon and it sounds like a lot of compute. Let’s check the meter.<br>The meter needs three numbers: how rich your sensory stream is, how a modern model would tokenize it, and what a token costs today.<br>Start with the stream. Your photoreceptors take in on the order of 10⁹ bits per second. As a proxy, treat each eye as a camera recording 8K video at 60 frames per second. This is generous to the machines twice over. An 8K frame is about 33 megapixels, while each retina carries roughly 100 million photoreceptors.¹ And a camera records a flat image, while your eyes are two offset viewpoints that the brain fuses into a three-dimensional scene. Let’s throw the stereo geometry in for free.<br>Next, the tokenizer. Let’s use Google’s own accounting. Gemini’s video mode is built for summarizing footage, so by default it samples one frame per second and squashes each frame to 258 tokens.² We aren’t using that sampling rate here, only Gemini’s per-tile accounting. To match our 8K resolution, Gemini’s documentation specifies that images are cut into 768×768 tiles, each billed at 258 tokens.³ An 8K frame takes 10×6 = 60 tiles, so<br>60 × 258 ≈ 15,500 tokens per frame<br>At 60 frames per second, both eye streams<br>15,500 × 60 × 2 ≈ 1.9 × 10⁶ tokens/s<br>Your ten-second morning comes to<br>1.9 × 10⁶ tokens/s × 10 s ≈ 1.9 × 10⁷ tokens<br>1.9 × 10⁷ × ($2 / 10⁶) ≈ $37⁴<br>Run the meter over a full sixteen-hour waking day — 57,600 seconds — and one person’s bill balloons:<br>1.9 × 10⁶ × 57,600 ≈ 1.1 × 10¹¹ tokens ≈ $220,000 per day<br>For just one person! At I/O 2026, Google announced its entire fleet processes 3.2 quadrillion tokens a month⁵ — which works out to about 1.2 × 10⁹ tokens per second — across every product it runs, which sounds like a lot (and by today’s standards it is). Divide by your eyes’ 1.9 × 10⁶ tokens per second and the largest inference fleet on Earth is able to run the visual feed of only 650 people. For everyone to have their morning,<br>8 × 10⁹ people × 1.9 × 10⁷ tokens ≈ 1.5 × 10¹⁷ tokens<br>Ten seconds of humanity waking up would keep Google’s entire fleet busy for almost four years .<br>So we can relax — clearly there’s more compute than anyone could ever need. Except we’ve said that before, every time, and every time we were wrong.<br>The history of computing is littered with failed ceilings. The most famous is the line attributed to Bill Gates: “640K ought to be enough for anybody.”⁶ He almost certainly never said it, but the quote endures because it captures how the era genuinely thought. And it is in good company. Thomas Watson of IBM supposedly saw a world market for perhaps five computers.⁷ In 1998, Paul Krugman predicted the internet’s economic impact would be no greater than the fax machine’s.⁸<br>Time and again, humans blew past these ceilings with previously unthinkable uses of computing: a new paradigm, a new architecture, a new interface. What strikes me is how few people it took. The transistor came out of a single group at Bell Labs: Bardeen, Brattain, and Shockley, plus a supporting cast of perhaps a dozen. Nearly everything you’re using to read this, from the graphical interface to Ethernet, was invented in one building in Palo Alto by the few dozen researchers of Xerox PARC. Deep learning survived two AI winters inside a group small enough to fit in a seminar room, and AlexNet was two graduate students and their advisor training on a pair of GTX 580s in a bedroom in Toronto. And the Transformer, the architecture underneath every frontier model, arrived in a single 2017 paper written by eight researchers at Google.<br>And when those minds do conjure a new use, Rich Sutton's Bitter Lesson tells us which version of it wins: across seventy years of AI research, the approaches that prevail are the general methods that ride the curve of ever-growing computation, eventually crushing even the cleverest hand-engineered alternatives.⁹ Imagination sets the demand; the Bitter Lesson guarantees it's demand for compute.<br>Every one of these failed predictions has the same thing buried in it: at any given moment, new demand for compute has run through a few rooms' worth of...