On Computers

Ever since my dad brought home an Apple IIe in 1984, I've loved computers. Not computer science, or computer programming (though eventually I'd come to love these too): the power-sucking, pixelated, thrumming physical gadgets. I don't love them because they are useful or intellectually stimulating, but because they are intrinsically fun machines. I like it when they feep; when they chime at power on; when their fans run, heating up the room; the ever-changing acoustic and tactile feedback of their keyboards; and the clunking and screeching and clicking of their storage devices, though there is less of that in these solid-state days. Computers are just cool to me, the way muscle cars or mechanical watches are to their enthusiasts.

For most of my life, this fascination has been wholly disconnected from the role these physical machines play in the economy. Even though computers impact a bunch of important industries (software, IT services, the Internet, etc.) the computers themselves have been kind of marginal in capitalism's grand parade. It can be tricky to decide what counts exactly as computers as distinct from semiconductors, personal electronics, and other categories more cleanly captured in economic statistics. For my purposes, I think of the "physical compute substrate" as being composed of CPUs, RAM, and accelerators where applicable (lately, of course, GPUs). These three are often the principal components of what problems a given computing device is fit to solve.

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As the 2022 vintage AI boom has progressed, leading to ever more financial heroics in datacenter construction, my childhood notion of this "core" segment of the hardware market as a niche industry has been feeling ... off. Financed with sophisticated combinations of equity and debt, compute spend is driving multiyear funding plans that have edged the margin-rich and asset-light hyperscalers of yore to look more like classic heavy industries than the cottage industry I came of age in. But I am in AI-besotted San Francisco near the peak of a market cycle, and our intuitions can deceive us in moments and places like this. What do the numbers show? And how will they change, to the extent we can foresee?

Trying to guesstimate the revenue of a particular slice of an industry like this always involves some guessing, but between Epoch AI's epic chip sales dataset, WSTS's industry billings, and a stack of Intel, Nvidia, and AMD 10-Ks, we can put together a guess that computers qua computers were about a $15B¹ industry all-in in 1984². To give some sense of scale, that is about the size of Major League Baseball. MLB is, of course, considerable, and irreplaceable to its aficionados. But it is also not a lynchpin of global commerce. I believe the US DoW has no contingency plans to wage war against the Dominican Republic should it disrupt the baseball talent supply, for instance.

When I grew up and joined the workforce of software engineers in 2000, the computer brain industry had grown up to about $90B in revenues; about the revenue of global distilled spirits.

2020, the last time I drew a paycheck as a software engineer, it reached about $200 billion. About the global pet care industry.

But then in 2025, it nearly doubled to $350 billion. (Global cement.) And we have every reason to expect this explosive growth to continue, given the hyperscalers and the frontier labs' commitments to compute spend over the foreseeable future.

The vibeshift around compute, then, is not wholly illusory. The economy around the physical substrate for the computing revolution has spiralled up in size from Major League Baseball to cement. If it should double again, as seems very likely in the next year or two, it will be comparable to the global cosmetics industry. Another doubling from there would put it in the heady air of the advertising and apparel industries, among the largest on Earth.

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So what?

So, computing machinery is bigger than you think it is, and getting bigger faster than you can update your intuitions about how big it is. As technologists, we at Pebblebed find these machines fascinating. But we're also investors, and with our fiduciary hats on, we're compelled to ask "So what?" OK, computers are blowing up. How do we act on this information?

Infrastructure Software

One consequence of computing moving up an order of magnitude is that infrastructure software, loosely defined as software that exists to unlock latent capabilities in the hardware, can create proportionately more value. When we were both at Facebook, my partner Pamela Vagata once earned a coveted piece of corporate swag with an understated patch casually boasting "$1B SAVED", with a graphic suggesting the transition from exponential decay to exponential growth. She saved the company over $1B by inventing the ORC file format, which radically improved the storage and compute efficiency of many critical workflows.

Back in 2013 when she was doing this work, computing was too small to support a venture-scale outcome for a company driven by these kinds of insights. But in the context of 2026 hardware budgets, a comparable feat of invention and technical derring-do might easily save $10B, or more. "Savings" of this order of magnitude aren't best modeled as cost reduction, but as unshackling the company to face enormously more ambitious projects. Capturing even a small fraction of the value created in this way can lead to outcomes that would have been historic 10 years ago.

In the Pebblebed portfolio, we focus our infrastructure investing close to the hardware/software interface. We believe that unlocking inefficiencies and operational ease at this layer is going to explode over the decade ahead. Cedana, which allows GPU jobs to be migrated, increases neoclouds' revenue per MW. Northflank provides the undifferentiated heavy-lifting that has to happen to turn raw k8s running on your cluster into a usable, observable, resilient system. And Lemurian Labs are building the modern analog to the Java Virtual Machine, bringing write-once/run-anywhere to accelerated compute.

Application software

The boom in AI compute is radiating out into the larger economy in various ways, and we are starting to see its impact in our application software as well. Build makes AI for the built world. In product terms, they build AI capable of performing previously-labor-intensive information-gathering processes in commercial real estate. From their start, Build has made hard choices to prioritize serving those developers who are building datacenters. This was not an entirely consensus perspective at the time of our investment a year ago, but it has panned out even better than all but the most ardent bulls would have predicted so far.

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These are our perspectives to date. We don't have any glib conclusions, and will not know with precision how this all turns out for a while. Computing has gotten much, much bigger, and sheer momentum guarantees that will continue for a while. Anyone offering firmer conclusions than this is probably epistemically overconfident. I feel grateful to my younger self for finding these unusual machines so compelling.

Footnotes

1. All dollar figures are inflation-adjusted to 2025 USD equivalents unless otherwise noted. ↩

2. We are counting the merchant market for the three things I'm calling a computer's brain: CPUs; memory (DRAM, with a little SRAM); and compute accelerators crunching numbers beside them (the discrete FPUs of yore, and GPUs, inclusive of datacenter GPGPUs). With the help of my friendly local AI, I have tried to tally the worldwide spend in dollars for each chip, then used CPI to turn them into 2025 dollars. Recent accelerator figures lean on Epoch AI's chip-sales dataset. The rest is assembled year by year:

   • 1984:
     • CPUs: a Dataquest worldwide table, checked against Intel's 1984 annual report
     • DRAM: the same Dataquest table (MOS-memory line), plus USITC anti-dumping filings
   • 2000:
     • CPUs: SIA product-line billings, with Intel's 10-K behind the figure
     • DRAM: Gartner Dataquest, against the SIA billings
     • GPUs: NVIDIA's 10-K
   • 2020:
     • CPUs: WSTS and IC Insights, with the Intel and AMD 10-Ks behind the figure
     • DRAM: the same WSTS and IC Insights lines
     • Accelerators: Jon Peddie for the gaming boards; Nvidia's segment filings for the datacenter
   • 2025:
     • CPUs and DRAM: the same houses as 2020, plus TrendForce for the high-bandwidth memory
     • Accelerators: Epoch AI
   • 2026 (estimates):
     • Memory: WSTS and TrendForce projections
     • Accelerators: Nvidia's quarterly run-rate

   The 1984 number has the spottiest sources. Back then memory, not logic, was the major revenue driver: in 1984 the world bought about $6.2 billion of memory chips but only $3.2 billion of microcomponents (CPUs, and the microcontrollers and glue that ride along with them), of which true CPUs were a sliver. So, dollar-for-dollar, the brain of that Apple IIe was mostly RAM. (Those figures come off Dataquest's 1984 worldwide table, scanned into the Computer History Museum's archive.) Also, an important judgement call: I am leaving out the processors in phones and tablets (which I file under personal electronics, vs. computers), and I subtract the high-bandwidth memory soldered onto the AI accelerators from the memory column, since it is already counted in the price of the accelerator and I'd rather not double-count. Memory revenue is also violently cyclical (DRAM can halve in a year), so any single snapshot is partly a story about where we happened to catch the wave. 2026 is half a forecast, riding a memory supercycle; please do not take it as more than one significant digit. ↩